In this exclusive interview, IoT Business News sits down with Steven Baker, Chief Product Officer at KORE*, to discuss the company’s groundbreaking achievements, recent technological advancements, and strategic initiatives in the rapidly evolving Internet of Things (IoT) landscape.

From the acquisition of Twilio’s IoT connectivity business to the development of cutting-edge eUICC technology, Steven provides insights into how KORE is positioning itself as a leader in the market. He also shares success stories, highlights partnerships, and outlines the company’s future plans, offering a comprehensive overview of KORE’s vision and impact in the IoT space.

IoT Business News: Can you elaborate on KORE’s most significant achievements in the IoT space over the past year and how these have positioned the company in the market?

Steven Baker: The acquisition of Twilio’s IoT connectivity business unit has brought KORE an unparalleled combination of carrier IMSI and eUICC product capability. It has also positioned KORE with a highly scalable route to market with a self-service, developer-centric model enabling KORE to capitalize on the OEM drive for built-in connectivity during the manufacturing process.

Over the last year KORE has also launched the Pre-configured Solutions (PCS) business unit focused on delivering high-value IoT enablement services incorporating connectivity, hardware, and managed services into pre-configured bundles that reduce the complexity of deploying IoT applications.

KORE has also reached over 19 million IoT subscriptions in service making KORE one of the largest IoT MVNOs in the world.

What are the latest technological advancements that KORE has integrated into its IoT solutions? How are these innovations enhancing your offerings compared to your competitors?

One of the most recent technological advancements KORE has released is an eUICC technology called the Local Profile Management Applet. The KORE LPM Applet enables a host CPU to manage previously downloaded eSIM profiles locally using AT commands. This capability can be used to implement device-initiated fallback, device-initiated failover, resiliency, and similar advanced capabilities. The LPM applet combines the simplicity and reliability of M2M orchestration with the flexibility and power of local profile management, in a manner that’s forward-compatible with the SGP32 IoT standard. The applet interface is based on the SGP22 ES10 standard ensuring developers who use it get to keep their investment as they eventually migrate to SGP22 and SGP32 solutions.

Could you share a recent customer success story that highlights the effectiveness of KORE’s IoT solutions in transforming their business operations?

A key element of KORE’s ability to positively impact our customer’s business operations is through our multi-country, managed service and logistics solutions. KORE has a long history serving Healthcare OEMs and DMEs with critical healthcare logistics solutions. Recently, two KORE customers in this space experienced a 40% reduction in annualized new inventory spend by leveraging KORE’s reverse logistics, sanatization, and redeployment of pre-used equipment including custom device configuration enabling a single device to support multiple business lines.

With increasing concerns around IoT security, what steps has KORE taken to enhance the security features of its solutions?

KORE’s eSIM technology includes support for IoT SAFE. IoT SAFE establishes the SIM as the ‘Root of Trust’ to enable chip-to-cloud security. This technology can be configured during SIM manufacturing making it both scalable and simple to leverage using standard (D)TLS standards. It obviates the need for post manufacturing device provisioning which can be difficult to both provision and maintain when requiring periodic security updates over time.

How do partnerships shape KORE’s strategy in expanding its IoT solutions? Are there any recent or upcoming collaborations that we should be aware of?

KORE’s partner portfolio spans 30+ MNOs worldwide as well as many professional and managed service providers. These partnerships enable KORE to pull together nearly any combination of connectivity, hardware, and service to solve IoT challenges. KORE also has strong relationships with multiple cloud providers including most recently with Google. Recently, KORE also added partners with multiple third-party service providers to augment our manual and professional service portfolio to enable our vertically focused pre-configured solutions.

What markets does KORE plan to target in the near future? Are there new industries or regions where you see significant growth opportunities for your IoT solutions?

From a regional perspective, KORE has traditionally had a significant presence in North America and has been growing in the UK and EU with localized sales, logistics, and support teams. KORE’s carrier partner ecosystem is expanding in 2024 enabling KORE to focus more on the APAC region, and more specifically China.

From a vertical market perspective, KORE’s current PCS solutions target 3 vertical areas including enterprise Fixed Wireless (FW), Connected Health, and Fleet Management. These solutions are deployed throughout North America and are expanding into the UK, Europe, and Latin America in the coming months. KORE is also currently trialing an Industrial pre-configured solution.

How is KORE leveraging its IoT technology to promote sustainability within its operations and among its clients? Are there specific initiatives or projects that exemplify this approach?

KORE launched an initiative to reduce waste and support sustainability by reducing the size of card bodies in its SIM shipments, which ties into “IoT for Good” – a key initiative here at KORE that leverages innovations in IoT such as SIM and connectivity to enable us to live greener and longer, all while making informed, intelligent use of our global resources. Since the commercial launch of SIM cards three decades ago, approximately 4.5 billion SIM cards are sold and shipped each year industry-wide, accounting for more than 560,000 tons of carbon dioxide and 18,000+ tons of plastic waste annually. While the SIM card has reduced in size over the last three decades, the packaging the card body that holds the SIM has not. The KORE initiative reduces the card body by 50% and, relating to SIM cards, is expected to:

• Reduce shipping costs by 50% due to the weight reduction
• Reduce KORE’s carbon footprint by 16%
• Aid customers in reducing plastic waste by 50%

Looking ahead, what are the next big steps for KORE in terms of product development and market strategies? Are there any upcoming innovations or technologies that you are particularly excited about introducing to the market?

KORE is evolving our eSIM technology to incorporate the SGP.32 (IoT) standard and increasing our coverage footprint with in-country coverage in the APAC region. OEMs worldwide will be seeking ways to leverage iSIM technology to open up new service potential and simplify logistics and provisioning at scale.

KORE also continues to evolve our AI modeling with new initiatives around it for managing IoT real-time operations and logistics analysis and monitoring as well as custom AI solutions for individual use cases requiring intelligent automation at scale.

The post Innovating IoT: An Exclusive Interview with KORE’s Chief Product Officer Steven Baker appeared first on IoT Business News.

To avoid fallout from internet connectivity outages, here’s what you need to do.

Our world today is defined by digital dependence and constant connectivity, and enterprises face many challenges, including cyber threats and infrastructure vulnerabilities. Robust network resilience has never been more crucial. As a cellular connectivity provider at the forefront of mobile and IoT connectivity, we understand the importance of safeguarding against disruptions, and our solutions are geared for this. Here’s how.

Resilience refers to a network’s ability to detect, respond to, recover from, and adapt to faults and challenges to normal operation without downtime or functionality loss. It is crucial for success and longevity.

David Farquharson, founder and CEO of iONLINE Connected Networks, an enterprise private LTE/5G provider supplying IoT connectivity solutions globally, likens a lack of network resilience to trying to drive a car without fuel: it simply won’t move and is a risky game to play.

He says: “Clients are looking to grow, expand, and scale their businesses and without network resilience, their operations can come to a complete standstill. This impacts not only the availability of their products but also their brand reputation and financial performance.”

A resilience plan is essential to mitigate risks, Farquharson adds.

“This is where our role becomes critical. By providing resilient solutions, we prepare our customers for the inevitable challenges, ensuring that, when faced with disruptions, they can continue to operate smoothly.”

Resilience failure in real life

Farquharson exemplifies this with the recent widespread outage experienced by AT&T in the USA, which left thousands of companies without connectivity.

“In the USA, our solution aggregates all three major carriers, enabling multi-carrier resilience on a single SIM. So, when AT&T’s network went down recently, our clients experienced zero downtime. As an example, one of our clients is NOAA, the National Oceanic and Atmospheric Administration. At the time of this AT&T outage, NOAA was able to continue reporting on weather systems and patterns and provide forecasts and warnings without interruption – crucial information on which a lot of people depend. Without the resilience of our solution, this would not have been the case.”

A similar situation was also seen recently when the subsea cables off the African coast were severed. “Thousands of companies went down when their supporting network was impacted, but iONLINE’s network did not,” Farquharson explains. “Our connectivity solution continued unhindered. Our SIMs can connect to multiple carriers, with multiple breakouts to different links, making our network highly resilient. This allowed us to maintain connectivity for our clients.”

iONLINE recently launched FlexiSIM, its intelligent network switching (eUICC) SIM, in the USA. Offering multi-network resilience, FlexiSIM provides local and global connectivity, always ensuring the best connection, regardless of location. It connects in more than 189 countries, on over 700 carriers.

FlexiSIM: an ideal solution for enterprises

FlexiSIM is a network IoT SIM with tens of thousands of unique connectivity options differentiated by country, carrier, interconnect, breakout, price, and more. While traditional global SIMs break out at a single location, FlexiSIM breaks out locally in-country, decreasing latency and improving performance. It can be remotely updated with new carrier information over the air, controlling in which countries and to which carriers it can connect.

Several international organizations already benefit from FlexiSIM, including NOAA, Fujifilm, AIoTSense, and Fidelity ADT. Fidelity ADT is the largest security services provider to large-scale enterprises in Southern Africa. They use thousands of FlexiSIM SIM cards to run their operations and keep their staff, the public, and the assets in their care safe. This includes critical infrastructure such as airports, government, health and education departments, as well as hospitality venues, casinos, shopping centres, and residential housing estates and their occupants.

Resilience cannot be overlooked

“The recent massive outages in the USA and Africa are a stark reminder of the importance of network resilience,” says Farquharson. “Connectivity is so much more than a service; it’s the lifeline of modern enterprises; fueling operations, empowering innovation and connecting us globally. iONLINE is proud that, amid widespread disruptions, our clients remained not just operational but ahead of the pack, flourishing where others faltered.”

To find out how FlexiSIM can help your business, schedule a free demo with one of our connectivity experts.

You can also follow us on LinkedIn and Facebook, where we share insights, trends, use cases, and industry news every week.

For more information on FlexiSIM or to schedule an interview with a company representative, contact Cory Mabry, Global Marketing & Communications Specialist at iONLINE, at cory@ionlinesp.com.

The post Prevent outages with built-in network resilience appeared first on IoT Business News.

IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today has published its latest research on the global cellular IoT module and chipset market.

The report reveals that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, our research projects the market to climb back to near 2022 levels in 2024. The research article further delves into the evolution of cellular IoT modules and chipsets with the latest technological innovations in smart and AI-enabled cellular IoT modules as key drivers for the project growth.

Key insights:

• The latest update to the Global Cellular IoT Module and Chipset Market Tracker and Forecast shows that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, the tracker projects the market to climb back almost fully to 2022 levels in 2024.
• The rise of smart and AI-enabled cellular IoT modules, which enable data processing and decision-making near or at the edge, is helping to drive this projected growth.
• AI is not the same across the board—the capabilities of AI-enabled cellular IoT modules vary between low, medium, and high based on the speed of AI inference, typically driven by the chipsets used.

Key quotes:

Satyajit Sinha, Principal Analyst at IoT Analytics, remarks “IoT devices are evolving beyond connecting devices and expanding to analyzing the data they produce to make swift, informed choices. As a result, there is a growing need for more computational power and intelligence, especially at the edge closer to the data generated. This trend is also apparent in the cellular IoT field, where integrating AI with cellular IoT modules and chipsets leads to more autonomous decision-making. It also minimizes data transmission over cellular networks, reducing bandwidth and costs. On-device AI models powered by NPUs enhance this capability by enabling smart decision-making at the edge.”

Cellular IoT module market update

Global shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023, according to the updated Global Cellular IoT Module and Chipset Market Tracker & Forecast (Q1/2024 Update). Two factors contributed to this decline:

1. Inventory optimization: Initial supply shortages caused by the COVID-19 pandemic and trade tensions around the same time led to manufacturers overordering modules and chipsets in 2021 and 2022, resulting in a surplus of these on the market. To address the surplus in 2023, manufacturers prioritized using existing modules and chipsets, thus delaying new orders.
2. Economic uncertainty: Inflation, rising interest rates, recession fears, and renewed US–China trade tensions have created a cautious investment climate, impacting new orders.

Fortunately, corporate executives appear to be easing their economic concerns, which could lessen the impact of the second factor as supply rebalances with demand. As this balance is achieved, cellular IoT module and chipset shipments are expected to rebound in the near term and are forecasted to grow at a 22% CAGR until 2027, with the slump of 2023 almost fully eradicated by the end of 2024.

Also likely to help rejuvenate this market is the rise of smart and AI-enabled cellular IoT modules—technologies that leverage embedded computational resources to execute advanced data analysis or even AI inference directly on IoT devices. Together, shipments of these advanced modules are forecasted to grow at a CAGR of 76% until 2027.

Smart and AI-enabled cellular IoT modules represent the latest frontiers of cellular IoT connectivity—the latest interactions of cellular IoT technology operating alongside their more basic, yet still quite capable, predecessor modules worldwide. Below, we will look at the evolution of IoT modules and chipsets and delve further into AI-enabled cellular IoT modules, including a look at their various processing capabilities and some applications for these intelligent modules.

Evolution of IoT cellular modules

The evolution of IoT cellular connectivity can be seen as 3 overlapping generations: legacy, smart, and AI-enabled.

“AI-driven productivity is inevitably evolving as an essential to extend the capabilities of IoT devices, significantly [improving] operational efficiency by enriching the IoT device with edge computing.” – Eden Chen, General Manager of MC BU at Fibocom

In the beginning: Legacy cellular IoT modules

Legacy cellular IoT models have been around for nearly two decades, simply providing connectivity for IoT devices to send and receive data from other locations. They include a cellular chipset/baseband to connect to a specified cellular technology, e.g., 2G, 3G, 5G, or NB-IoT.

In 2023, legacy cellular IoT modules comprised 96% of global cellular IoT module shipments. While shipments of these modules are forecasted to grow at a CAGR of 18% until 2027, their share of global cellular IoT module shipments will begin to give way to smart and AI-enabled cellular IoT modules, as discussed below.

Example of a legacy cellular IoT module

An example of a legacy cellular IoT module is the EM9190 5G New Radio (NR) Sub-6 GHz Module from Sierra Wireless, a Canadian wireless communications equipment manufacturer. This module enables devices to connect to 5G networks with 4G and 3G fallback when 5G is unavailable. Sierra Wireless announced the EM91 series of these legacy modules in August 2020, which is fairly recent; however, this reflects that legacy cellular IoT modules are still in demand when edge processing is unnecessary.

*Note: US-based semiconductor and IoT systems provider Semtech acquired Sierra Wireless in January 2023.

A move toward the edge: Smart cellular IoT modules

Smart cellular IoT modules have been on the market for nearly a decade. In addition to providing the connectivity capability found with their legacy counterparts, these smart modules include powerful CPUs and GPUs for on-device data processing. They can also support operating systems like Linux or Android to enable advanced functions and multimedia capabilities.

In 2023, these smart modules comprised 2% of global cellular IoT module shipments; however, the tracker forecasts this number to rise to 10% by 2027, with a CAGR of 79%.

Example of a smart cellular IoT module

An example of a smart cellular IoT module is the CQS290 Smart Cellular IoT Android Module from US-based cellular IoT module manufacturer Cavli Wireless. Cavli announced the unveiling of this module at the India Mobile Congress in October 2023. This LTE Cat 4 module, with Android 12, runs on an ARM Cortex A53 quad-core processor and has a built-in Adreno 702 graphics processing unit (GPU).

Intelligence at the edge: AI-enabled cellular IoT modules

AI-enabled cellular IoT modules are relatively newer than their legacy and smart counterparts, having been on the market for over 5 years. Along with the connectivity capabilities of the other types of cellular IoT modules, AI-enabled versions include NPUs, TPUs, PPUs, or other dedicated parallel-processing chipsets (e.g., GPUs) for AI inference.

While still in its early stages, AI and cellular IoT convergence holds immense potential to revolutionize industries. Integrating AI directly into IoT modules means AI inference can occur at the edge, allowing for rapid and intelligent decision-making at the edge. This reduces data transmission over cellular networks, saves bandwidth and costs, and facilitates immediate, autonomous decision-making for time-sensitive applications. Further, embedding AI chipsets within connectivity modules can save space and streamline the form factor of IoT devices. In all, these modules are evolving from mere data communication enablers to intelligent edge nodes capable of handling certain workloads independently.

In 2023, AI-enabled cellular IoT modules comprised 2% of global cellular IoT module shipments. The tracker forecasts that by 2027, this will grow to 9%, with a CAGR of 73%.

Example of an AI-enabled cellular IoT module

In November 2023, China-based wireless communications modules vendor Fibocom announced the release of its SC228 LTE smart module, which is powered by Qualcomm’s SM6225 (aka Snapdragon 680) SoC. With its 8 processing cores (4 x A73 at 2.4GHz and 4 x A53 at 1.9GHz), the SC228 is designed to handle AI algorithms, such as image processing algorithms. It is geared toward industrial IoT, smart retail, in-vehicle infotainment, and similar applications. The system comes with Android 14 but is upgradable as new software develops. For connectivity, it supports 4G LTE, 3G, WiFi, and Bluetooth.

Capabilities and applications of AI-enabled cellular IoT modules

AI is not the same across all applications. Within AI-enabled cellular IoT modules, there are varying processing capabilities based either on the needs of specific applications or the limitations of the hardware. IoT Analytics generalizes these modules’ capabilities into three categories: low, medium, and high.

1. Low AI capability

Cellular IoT modules with low AI capability conduct AI inference at less than 5 trillion (or tera) operations per second (TOPS), the standard measure of AI performance based on the number of computing operations an AI chip can handle in one second. Common applications of these modules include:

• Acoustic event detection
• Gesture/Activity recognition
• Voice ID/ Keyword spotting

These low AI capability modules comprised 59% of global AI-enabled cellular IoT module shipments in 2023. While the tracker projects the number of shipments of these modules to grow at a CAGR of 30% until 2027, cellular IoT modules with medium and high AI capabilities are expected to grow faster.

Example of a cellular IoT module with low AI capability

Fibocom’s SC138-EAU module features a Qualcomm QCM6125 SoC with an AI engine capable of 1 TOPS.

2. Medium AI capability

Cellular IoT modules with medium AI capability conduct AI inference at 5–10 TOPS. Common applications for these modules include:

• Human detection
• Vehicle detection
• People counting
• Face detection

In 2023, these medium AI capability modules comprised 36% of all global AI-enabled cellular IoT module shipments. The tracker projects that the shipment of these modules will grow at a CAGR of 102% until 2027.

Example of a cellular IoT module with medium AI capability

Quectel’s SG-530C-CN module hosts a UNISOC P778 SoC, which contains an NPU and is capable of 8 TOPS.

3. High AI capability

Finally, cellular IoT modules with high AI capability conduct edge AI inference at over 10 TOPS. Common advanced applications for these modules include:

• AI-driven predictive maintenance
• Enhanced decision-making with advanced analytics
• AI-enhanced driver safety solutions
• Real-time monitoring for drowsiness and distractions
• Comprehensive safety analysis
• Intelligent voice assistance

According to the tracker, these high AI capability modules comprised 5% of all global AI-enabled module shipments in 2023. The tracker forecasts the shipments of these modules to grow at a CAGR of 128% until 2027.

Example of a cellular IoT module with medium AI capability

MeiG’s SRM930 module bears a Qualcomm QCM6490 SoC, which includes Qualcomm’s 6th Gen AI Engine capable of reaching an AI performance of 12 TOPS.

Analyst takeaway

IoT is evolving beyond mere connectivity—it now encompasses connecting devices, understanding the data they generate, and making fast, informed decisions based on this data. As such, computing power and intelligence are becoming increasingly essential, particularly closer to where data is generated—at the edge. Thus, it is beneficial to have a dedicated chipset, such as a GPU or NPU, that can be used for AI inference directly on IoT devices, whether embedded in the printed circuit board (PCB) or as a component within the main processor.

Cellular IoT modules are undergoing a similar evolution. Although still in the early stages, integrating AI with cellular IoT promises to transform various industries. However, the core technology is driven by chipset companies like Qualcomm, Sony Altair, and UNISOC. Other chipset companies like MediaTek and ST may enter this market soon. So far, as seen above, vendors are predominately using Qualcomm chipsets equipped with AI engines that utilize the chipsets’ CPU, GPU, or NPU components.

With the rise of AI-enabled cellular IoT modules, two trends are emerging that are worth watching:

• AI-enabled 5G modules in automotive: The adoption of AI-enabled cellular modules focused on automotive applications, especially with 5G connectivity, is expected to accelerate. By 2027, AI-enabled 5G modules for automotive applications are projected to constitute 21% of all AI-enabled cellular module shipments.
• AI in cellular LPWA modules. So far, most of the modules are focused on standard 5G and 4G technology (with 2G and 3G as fallbacks). However, cellular LPWA modules are already entering the scene. For example, the Sony Altair ALT1350 is a low-power, LTE-M/NB-IoT SoC equipped with AI capabilities for low-power acceleration. This chipset is designed for edge processing and tiny ML model inference, opening doors for AI-enabled modules in the cellular LPWA segment.

Disclosures

Companies mentioned in this article—along with their products—are used as examples to showcase market developments. No company paid or received preferential treatment in this article, and it is at the discretion of the analyst to select which examples are used. IoT Analytics makes efforts to vary the companies and products mentioned to help shine attention to the numerous IoT and related technology market players.

It is worth noting that IoT Analytics may have commercial relationships with some companies mentioned in its articles, as some companies license IoT Analytics market research. However, for confidentiality, IoT Analytics cannot disclose individual relationships.

The post The rise of smart and AI-capable cellular IoT modules appeared first on IoT Business News.

What tech skills companies recruited for in Q1 2024? AI, Gen AI, and 5G

IoT Analytics today released the results of their latest report titled: “State of Tech Employment”.

The report analyzes over one million U.S. job postings to determine which tech expertise and skills are in demand and assess the hiring intensity of 450+ companies across various sectors. The findings from Q1 2024 show that 1. AI-related job postings are on the rise, 2. 5G- and WiFi-related job postings are up, while most other connectivity technologies decline, and 3. Cloud-related job postings have notably fallen.

Key insights

• Tech job openings decreased 2% in Q1 2024 in the US and are now down 47% since their peak in April 2022, according to the inaugural State of Tech Employment Spring 2024 report.
• AI-related job postings increased in Q1 2024, driven by strong demand for generative AI expertise. The number of 5G-related job postings also increased, while demand for cloud-related expertise declined QoQ.
• Hiring density in the industrial automation industry currently tops other industries, with the telecommunications industry at the tail end.

Key quotes

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Our new State of Tech Employment Spring 2024 report highlights a pivotal shift towards AI and expertise in the tech industry. Despite a 47% downturn in overall US tech job openings since April 2022, we’re witnessing a surge in demand for generative AI skills. February also marked the first month of increase in total job openings after seven quarters of decline. AI hiring is widespread, but AI providers such as OpenAI, Nvidia, and Anthropic are clearly leading the pack—each firm is currently at the forefront of the hiring intensity ranking.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds:

“The jump of AI-related job postings in Q1 2024 indicates that companies have realized that the talk of AI needs people that actually work on AI projects.”

The big picture: US tech role openings are down overall, but AI roles are up

The number of tech job postings decreased by 2% in Q1 2024 compared to Q4 2023, according to IoT Analytics’ inaugural 85-page State of Tech Employment Spring 2024 report. With that, Q1 marks the 7th consecutive quarter of overall declines in tech job openings in the US. Total job openings are now down 47% since their post-COVID peak in April 2022. For comparison, the US Bureau of Labor Statistics reports a 27% drop in overall monthly job openings over the same period, showing that the tech industry has certainly taken a hit in the recent economic slowdown.

However, on the bright side, February 2024 saw the first month-to-month growth in tech job openings since the start of the slump, at 3% compared to January 2024. Helping to steer this upward trendline was a significant climb in AI and AI-related fields, which will be discussed further below.

The State of Tech Employment report analyzes over one million U.S. job postings to determine which tech expertise and skills are in demand and assess the hiring intensity of 450+ companies across various sectors. Below are three key findings from the report that reflect the state of tech employment going into Q2 2024.

Key finding 1: AI-related job postings are on the rise

As a whole, job postings seeking expertise in AI and AI-related fields are up 4% QoQ, with AI itself climbing 8% QoQ. Most impressively, job postings seeking Generative AI (GenAI) expertise were up 38% QoQ in Q1 2024, and this steep climb helped propel the 3% bump in tech job openings in February 2023.

For recent historical context, AI-related job postings have followed a similar path as other tech job postings, peaking around April 2022 and declining significantly since then. However, in January 2023—shortly after the public release of OpenAI’s ChatGPT—the rate of decline in AI-related job openings began to steady while other fields continued declining. By July 2023, the number of AI-related job postings began to rise, increasing 18% through February 2023.

This upward trend in AI-related job postings correlates with corporate executives’ rising interest in AI. Compared to Q4 2022, the number of quarterly earnings calls mentioning AI in Q1 2024 has risen 19 percentage points, from 13% of calls to 32% of calls.

Executives are concerned about a labor shortage and skill gap in this area, thus creating the need to upskill existing or future workforces. Companies appear worried about losing existing AI talent, exemplified by Google’s co-founder Sergey Brin personally calling an employee to convince them not to move to OpenAI. Other executives (e.g., from Cognizant and Accenture) highlight the need to develop the skills in-house.

Key executive quotes on upskilling for AI

• “In 2023, 90% of our global workforce spent time in learning with 270,000 of our employees acquiring at least one new skill proficiency. And 88,000 completing AI and generative AI courses.” – Ravi Kumar, CEO, Cognizant, February 06, 2024
• “Invest more in the people than in the technology. […] There is no AI-ready workforce you can hire a year from now, or two years from now, or three years from now. You need to bring your workforce with you and develop them.” – Paul Daugherty, CTO, Accenture, 12 December 2023

A closer look at AI-related job postings shows that Python skills are very important for AI roles—52% of all AI-related job postings mention it, much more than other programming languages such as C++ (12%) or R (8%). Expertise related to TensorFlow (14%) and Pytorch (11%), both machine learning libraries, also play an important role. There is also a significant demand for cloud-related skills in AI job postings, with Azure (15%) topping AWS (10%) in terms of the skills mentioned.

The increasing demand for AI-related skills and expertise naturally influences the salary for skilled workers with desired skills. The platform Comprehensive.io indicates that the median salary of skilled employees in AI jobs is now $172,000 per year, 17% higher than that of a software engineer with a similar level of experience.

Key finding 2: 5G- and WiFi-related job postings are up, while most other connectivity technologies decline

Job postings seeking 5G expertise rose 13% QoQ in Q1 2024, the highest climb of connectivity-related job postings. This aligns with what IoT Analytics observed at the Mobile World Congress 2024 in Barcelona, Spain, where advancements in 5G technology to enhance quality and performance and enable private 5G connectivity were on full display.

WiFi-related job postings also rose in Q1 2024, though these only grew 2% QoQ. Meanwhile, in Q1 2024, job postings seeking 4G, cellular, Bluetooth, and LoRa expertise declined 3%, 6%, 9%, and 15% QoQ, respectively.

Key finding 3: Cloud-related job postings have notably fallen

According to the State of Tech Employment report, cloud-related job openings were down 3% QoQ in Q1 2024. Though this tech field has the highest number of job postings in absolute terms, the declining number of job postings is part of a continued downward trend since the peak of cloud-related job postings in April 2022. As discussed in the recent State of IoT Spring 2024, cloud revenue growth has reduced significantly in 2023 as cost optimization took center stage in many companies. That also impacted the cloud-related job postings. The job postings largely include companies that build products or services in the cloud and seek expertise in utilizing certain hyperscalers. Job postings that mentioned AWS dropped by 2% QoQ, Azure (-2% QoQ), and Google Cloud (-4% QoQ).

Other notable findings from the State of Tech Employment report

The State of Tech Employment covers more than just the changes in expertise and skills related to tech-related job postings. It also delves into the hiring intensity—the number of openings publicly displayed per 100 current employees—and layoff data of specific companies or industries.

Hiring intensity

Out of 7 tracked industries in the report (including the catchall “Other”), the industrial automation industry had the highest median hiring density in Q1 2024 at 3.1 open positions per 100 current employees. By comparison, the median hiring density across all industries tracked was 2.4.

Four industrial automation companies stood out in terms of hiring density:

• Johnson Controls International plc had a hiring intensity of 6.2
• Siemens had a hiring intensity of 4.8
• Schneider Electric had a hiring intensity of 4.4
• ABB Ltd. had a hiring intensity of 4.3

Companies in the telecommunications industry had the lowest median hiring intensity, with only 1 open position per 100 employees in Q1 2024. Some examples include:

• AT&T had a hiring intensity of 1.05.
• Verizon had a hiring intensity of 0.83.
• Orange had a hiring intensity of 0.26.

Layoffs

Some tech companies continue to lay off staff, although at a much smaller scale than a year ago. In Q4 2023, approximately 24,000 tech-related layoffs were registered, compared to 167,000 in Q1 2023—a stark decline. The areas with the highest cuts were:

1. HR – Our tracker shows that US-based cloud computing and virtualization technology company VMware cut 44% of its HR personnel in 2023.
2. Marketing – Our tracker shows that US-based software company Informatica, for example, cut 16% of its marketing team in 2023.
3. Support – US-based technology giant Microsoft, for example, cut its support staff by 18% in 2023, according to our data.

The post What tech skills companies recruited for in Q1 2024? appeared first on IoT Business News.

IoT Analytics released a research article that highlights 10 out of 41 telecom industry trends included in the Mobile World Congress (MWC) conference report.

This report presents a comprehensive summary of the key highlights and trends assembled by the IoT Analytics analyst team and discusses more than 130 companies.

Key insights:

• The current state of telecommunications was on full display at MWC Barcelona 2024.
• IoT Analytics’ three-analyst team produced a 123-page event report, covering 130+ companies, and presents its top 10 trends on the state of IoT & telecommunications here.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks “Mobile World Congress Barcelona 2024 brought to light the ongoing evolution within the IoT and telecom sectors. AI showcases took center stage this year. 5G advances, the anticipation around 6G and a pronounced focus on sustainability was also visible. Our team put together an in-depth 123-page event report, covering major developments from more than 130 companies that presented or exhibited at the fair.”

Satyajit Sinha, Principal Analyst at IoT Analytics, adds that:

“Leveraging eSIM and eUICCs as a hardware-based root of trust represents an innovative step in enhancing IoT security. Securing hardware forms the foundational layer for chip-to-cloud security. As we approach the era of quantum computing, focusing on quantum-resistant solutions and cryptographic agility will serve as a proactive defense against new cyber threats.”

Mobile World Congress 2023

The Mobile World Congress (MWC) Barcelona 2024, which took place from February 26 to February 29, is the telecommunication (telecom) industry’s main event showcasing the latest technologies and solutions, such as 5G, edge computing, and AI.

The MWC saw a significant increase in attendance in 2024, with a rise of 14% compared to the previous year. The event drew in 101,000 attendees. It hosted 2,700 exhibitors—a testament to the industry’s resilience and the undiminished allure of cutting-edge mobile technologies.

However, attendance was still 7% lower than at MWC Barcelona 2019, before the COVID-19 pandemic. On the other hand, the number of exhibitors increased by 12% from the pre-COVID era, reaching a new five-year record high.

“MWC Barcelona embodies the energy and vibrancy of the mobile ecosystem. We are honored to host this special event, which, once again, has delivered an exceptional four days of debate, thought leadership, inspiration, and deal-making.” – John Hoffman, CEO, GSMA Ltd.

IoT Analytics had a team of 3 analysts on the ground during the event’s four days. The team visited approximately 60+ booths and conducted more than 50 in-person interviews to comprehensively understand the most recent developments. Nearly every meeting started with, “What’s new in MWC, apart from AI?”

That said, AI was a common theme among the numerous new products and projects showcased at MWC 2024, from network operations management down to the chipsets themselves. However, AI was not a part of every notable advancement.

Our research clients can refer to the full 123-page MWC Barcelona 2024 Event Report to read more about the myriad of new technologies, trends, announcements, and insightful quotes from key telecom industry players. This article only highlights some of the major trends.

10 IoT and telco trends observed at MWC 2024

The full report shares 41 telecom industry trends the IoT Analytics team observed, including AI integration, telecom and satellite convergence, security, and sustainability. Here, we will highlight 10 trends that showcase the future of telco IoT connectivity, with a summary of each.

1. AI integration is enabling smarter, more efficient networks

The telecom industry spotlighted the integration of AI into telecom networks, particularly Radio Access Networks (RAN), emphasizing the shift towards more intelligent and automated network operations. Propelling this trend is the need for cost reductions, enhanced user experiences, and the mitigation of skill shortages.

AI’s role in telecom networks now includes smart monitoring, efficient analysis, and improved anomaly detection—offering solutions to the growing complexity of networks and cybersecurity threats. Companies are focusing on user-friendly AI tools that are accessible to developers with varying levels of expertise in AI/ML, thereby addressing the pressing need for innovation in network management and security.

Sweden-based multinational telecommunications company (telco) Ericsson introduced the Telecom AI concept, emphasizing zero-touch operations, trustworthy AI, and AI’s integration into networks. This initiative underscores the industry’s move towards automation, with AI enabling networks to adapt to user demands dynamically. This fosters a more responsive and adaptive telecom infrastructure.

Meanwhile, Japan-based mobile network operator Rakuten Mobile partnered with OpenAI, the US-based developer of ChatGPT, to develop AI tools for the telecom industry. This partnership aims to deploy AI solutions to open RAN’s architecture to enhance customer service and network optimization.

2. Cellular network technologies are enhancing performance and services

At MWC 2024, the integration of standalone 5G (SA 5G) with network slicing, the commercialization of 5G-Advanced (5G-A), and AI’s foundational role in the impending 6G networks highlight an evolution in telecoms. Collectively, these developments enhance network performance, efficiency, and the capacity to support a myriad of new applications and services.

Network slicing, a significant innovation driven by SA 5G deployments, allows customized network segments catering to diverse needs, optimizing resources, and bolstering security. It facilitates novel business models by offering personalized network experiences underpinned by technologies like Open RAN Intelligent Controllers (RICs) and machine learning.

Regarding SA 5G with network slicing, Finland-based telecom and IT company Nokia demonstrated multi-access edge slicing in partnership with e& UAE, a UAE state-owned telco, over a live mobile network. Multi-access network slicing allows e& UAE to offer new premium slicing services across 4G/5G, fixed wireless access, and fixed access that can support several use cases and applications simultaneously. Further, US-based semiconductor company Qualcomm showcased AI-driven 5G RAN network slicing capabilities within its Edgewise Suite.

Meanwhile, regarding 5G-A, China-based telecom equipment and services provider Huawei announced at MWC 2024 the world’s first 5.5G intelligent core network, which offers a tenfold speed increase over the initial 5G speeds.

Finally, regarding AI integration into future 6G networks, South Korea-based telecommunications operator SK Telecom shared a proof-of-concept video demonstration of a collaborative AI integration effort between it, Japan-based telco NTT, NTT’s telecom-provider subsidiary DOCOMO, and Nokia. The demonstration was for this team’s implementation of AI in the 6G air interface, which aims to enhance performance while minimizing energy consumption.

3. GenAI is revolutionizing customer interactions and network management

The IoT Analytics team noted 20 generative AI (GenAI) showcases at MWC Barcelona 2024, highlighting a significant shift toward leveraging GenAI to enhance customer interactions and improve network operations. Telcos showcased various GenAI-powered solutions across different stages of development, with most aiming to provide real-time, efficient customer service and network troubleshooting.

Of the 20 identified GenAI showcases, 10 were customer interaction and support use cases, making it the largest use case. Only 5 of the 20 showcases have been rolled out across the respective organizations or to customers—most others are either being piloted or their status is otherwise not known.

UK-based multinational telecom company Vodafone showcased its updated TOBi chatbot supported by Microsoft Azure’s OpenAI Service. TOBi has managed various customer interactions, including billing journeys, offers, and appointment booking, for Vodafone for years, but with GenAI integration, it can handle natural language well and personalize customer experiences. Meanwhile, Qualcomm presented its Edgewise Suite, a GenAI-powered RAN network management consultant system designed to simplify network and slice management tasks for RAN engineers. Its solution features impact assessment modeling, network topology, network performance tracking, gateway provisioning, and knowledgebases (including external data sources).

4. Semiconductor and AI advancements redefine the future of vRAN

The evolution of virtual RANs (vRANs) is at the forefront of mobile network development and is greatly influenced by semiconductor innovations and AI integration. Semiconductor companies are pivotal in this transformation, offering specialized processors and chipsets that boost vRAN performance, which can provide a cost-efficient, adaptable, and advanced architecture.

Meanwhile, AI’s inclusion within vRAN accelerator cards revolutionizes network efficiency, flexibility, and intelligence. AI algorithms run on specialized hardware such as CPUs, GPUs, and vRAN accelerator cards to optimize 5G networks.

Collaboration between leading telecoms is facilitating these advancements. At MWC 2024, Nokia and US-based semiconductor company NVIDIA unveiled a partnership integrating Nokia’s RAN software and layer 1 acceleration with NVIDIA’s CPUs for layer 2 processing and its GPUs for AI and vRAN acceleration. This collaboration marks a significant milestone towards AI-driven RAN solutions (AI-RAN)

US-based semiconductor company Intel announced the upcoming launch of its Xeon Granite Rapids-D processors, designed specifically for vRAN workloads. These processors will be equipped with high-performance, next-generation P cores and enhanced with Intel vRAN Boost acceleration.

5. AI integration into connectivity and chipset technologies

The previous trends highlight AI integration into telecom infrastructure, with advanced modules and chipsets to help accelerate AI. However, the technology industry is experiencing a transformative phase with AI integration into modules and chipsets themselves.

Telecom vendors are now embedding AI directly within connectivity modules and 5G chipsets, aiming to revolutionize how devices process data, manage network traffic, and optimize performance.

At MWC 2024, several companies showcased their latest module and chipset innovations with AI integration. For instance, Cina-based cellular module provider MeiG presented a comprehensive product line of AI modules—the SNM930 and SNM970—which boast AI computing power up to 48Tops and support high-end applications such as service robots.

Similarly, China-based cellular module providers SIMCom and Fibocom showcased their AI integrations. SIMCom introduced the SIM9650L Wi-Fi 6E module, featuring an 8-core processor and AI capabilities exceeding 14 Tops. This module is suitable for devices like intelligent point-of-sale systems and VR/AR devices. Meanwhile, Fibocom highlighted their SC208 module, powered by Qualcomm’s Snapdragon 460 platform and capable of efficiently handling complex tasks like 1080P video transmission and multi-camera inputs.

6. 5G RedCap chipsets, modules, and devices are advancing

Speaking of chipsets in general, several manufacturers unveiled new 3GPP Release 17-compliant 5G RedCap chipsets at MWC 2024. These chipsets emphasize enhanced power efficiency, lower development costs, and support for various applications.

Module manufacturers appear to be seizing the 5G RedCap opportunity by developing pre-tested modules that facilitate quicker and more reliable device development. These modules, often equipped with advanced power-saving features and high data throughput capabilities, are fostering the deployment of new devices and gateways across various industries.

For example, China-based IoT module manufacturer Lierda showcased its TE310 5G RedCap Industrial Gateway, equipped with a 5G RedCap NR90-HCN module, Gigabit Ethernet port, and Wi-Fi6 technology. Meanwhile, US-based private network solutions provider MosoLabs presented its Moso Connect 5G mobile adapter, which allows legacy machines with serial or ethernet connectivity to communicate over a private 5G network. Additionally, Ericsson demonstrated video surveillance capabilities using 5G AIoT cameras integrated with 5G RedCap modules.

Devices like these underscore 5G RedCap’s versatility and potential to enhance connectivity and reliability for various applications.

7. Cellular and satellite ecosystem convergence enhances IoT connectivity

The integration of satellite connectivity into cellular networks is revolutionizing global IoT deployment. Underscoring this trend is the proliferation of 3GPP non-terrestrial network (NTN) capabilities in chipsets and modules and the telcos’ embrace of satellite connectivity for ubiquitous IoT coverage.

At MWC 2024, the IoT Analytics team noted several collaborations and certifications highlighting this convergence. Examples on the telco front are Deutsche Telekom, a German telecom provider, launching IoT tariffs with geostationary satellite providers, and US-based non-geostationary satellite infrastructure provider Omnispace collaborating with MTN, a South African multinational telecom provider, to enhance 5G connectivity through low-Earth orbit satellites.

On the satellite front, in July 2023, US-based NTN service provider Skylo Technologies partnered with Keysight Technologies, Inc., a US-based electronics testing and measurement equipment manufacturer, to expand cellular testing to NTNs via a certification program for NB-IoT devices over satellite. At MWC 2024, Skylo announced that Keysight extended the number of available test cases to 145 in the Keysight RF/RRM Carrier Acceptance Toolset, which enables certification of 3GPP 5G Rel-17 NTN chipsets, modules, and devices for Skylo’s network.

Such advancements aim to enhance performance, reliability, and coverage, especially in remote areas where traditional connectivity is limited.

8. eSIM adoption for IoT and automotive on the rise

The global landscape of eSIM technology is witnessing transformative shifts, notably with China’s recent embrace of eSIM technology, advancements in pre-standard development for GSMA SGP.31/32 compliance, and the increasing implementation of eSIM/iSIM in IoT devices and vehicles.

After years of hesitation, China is now moving towards eSIM adoption. At MWC 2024, the IoT Analytics team observed strategic partnerships between major telecommunication entities, such as Chinese state-owned telecom operator China Unicom and eSIM technology providers Thales and Giesecke+Devrient.

“A sophisticated eSIM solution for China Unicom will help it reach a greater degree of security and reliability in its business development and user services in the 5G future.” – Eva Rudin, Vice President of Mobile Connectivity Solutions, Thales

On the regulatory and standards front, the introduction of GSMA’s new eSIM IoT specifications, SGP.31 and SGP.32, has prompted eSIM management companies to develop pre-standard solutions compatible with these requirements. IoT device manufacturing companies like Thales, Kigen, Webbing, and Redtea Mobile showcased advanced solutions that address the transition and coexistence of M2M and IoT eSIM standards, thereby facilitating the IoT ecosystem’s growth.

Moreover, the adoption of eSIM/iSIM technology in IoT devices and vehicles is gaining momentum, offering enhanced connectivity, flexibility, and scalability across various sectors, including pharmaceuticals and automotive. Initiatives like the Saga Card by Iceland-based pharmaceutical supply chain automation enabler Controlant and Deutsche Telekom, or German automotive company BMW‘s partnership with NTT Data for personal eSIM activation, underscore the vital role of eSIM/iSIM in enabling global connectivity and seamless network management for IoT solutions and connected vehicles.

9. On-device AI to quantum-resistant technologies enhance cybersecurity

In September 2023, IoT Analytics noted that 66% of cellular IoT modules shipped without dedicated hardware security. Fortunately, it seems the cybersecurity landscape is undergoing a transformative shift, driven by advancements in AI, enhanced IoT security through embedded universal integrated circuit card (eUICC) technology, and the development of quantum-resistant cryptography.

Integrating AI into cybersecurity solutions enables dynamic threat detection, prevention, and response. Traditional methods fall short in identifying novel cyber threats, but AI’s adaptability and learning capabilities ensure robust defense mechanisms. At MWC 2024, Israel-based endpoint security solutions provider Bufferzone highlighted this evolution by upgrading its endpoint security solutions to include AI-based security powered by Intel Core Ultra processors.

Further, eUICC helps fortify IoT device security by establishing a hardware-based root of trust. This approach allows for the local generation of cryptographic keys, leverages IoT-SAFE protocols, and enhances the security framework of IoT applications.

Notably, at MWC 2024, Ireland-based global IoT connectivity provider ZARIOT and UK-based IoT security solutions provider Crypto Quantique unveiled their collaboration to bolster IoT device security. Their solution integrates ZARIOT’s hardware root of trust SIM cards and Crypto Quantique‘s QuarkLink IoT device security platform to enhance secure provisioning, onboarding, and device lifecycle management.

10. Telcos and manufacturers strive for sustainability

Some of the above trends highlight energy efficiency as a driver or outcome of the respective advancements. IoT Analytics recently noted that discussions around sustainability and energy efficiency trended upward in Q1 2024 corporate earnings calls, and that trend appeared on showcase at MWC 2024.

Specifically, companies appear to be focusing on passive sustainability through 5G RAN energy optimization. Currently, RAN consumes approximately 70% of a 5G network’s total energy. As networks expand and data demand grows, the energy consumption of radio units and other RAN components escalates, negatively impacting not only operational costs but also environmental sustainability.

Based on observations at MWC 2024, vendors are focusing on passive sustainability efforts such as energy management in RAN. Key strategies include the following:

• Fronthaul control, cell admin state management, and energy-saving modes, all focusing on optimizing the power usage of radio units
• Enhanced power management states for servers and telemetry use, improving energy efficiency by monitoring and adjusting power usage in real time
• Deployment of RAN intelligent controllers, which use policy-based management and AI/ML techniques to enable intelligent energy management, including dynamically adapting network operations to current needs without compromising service quality

For example, Nokia showcased a new “extreme deep sleep” mode designed to reduce energy consumption. This feature uses Nokia’s AirScale radio architecture and its Reefshark system-on-chip chipsets and works by shutting down radio equipment during no-traffic periods, aiming for what Nokia calls “zero traffic, zero Watt.”

Additionally, VMware, a US-based cloud computing and virtualization technology company, presented its Network Efficiency framework, which was developed with Intel and Equinix, a US-based digital infrastructure company. The framework consists of two parts: a tool for analyzing energy usage and a tool to help discover cost savings. The former includes “green window”-based workload placement that simulates the impact of running some workloads during peak hours with lower energy costs.

“One of the main drivers for integration cost efficient energy saving features is ongoing global recession. Another driver is state policy with focus on ESG strict regulations and commitments of the companies to reach net zero in the next 10–15 years.” – Solution Architect at VMware

What these telco IoT trends mean for the future of connectivity

From AI integration across the network-to-device spectrum to enhanced security and sustainability, the telecom trends observed at MWC Barcelona 2024—presented above and in the MWC Barcelona 2024 Event Report—are going to have a significant impact on IoT connectivity and the telecom industry as a whole. As more and more mobile and IoT devices come online, being able to provision them and ensure data security will be important for the continued growth of the IoT market. However, most of the projects supporting these trends were either announced or demonstrated at MWC 2024 or are currently being piloted—it may take 2 or 3 years before we see mass commercialization or impact of these projects. That said, these trends suggest a future of interconnected, intelligent, and sustainable telecom ecosystems catering to evolving consumer demands and technological advancements.

The post Top 10 IoT & telco trends – as seen at MWC 2024 appeared first on IoT Business News.

IoT Analytics today released the results of the quarterly company earnings call analysis.

This analysis is based on a comprehensive dataset of Q1 2024 earnings calls from 6,000+ leading US-listed firms. The findings from Q1 2024 show that three key themes are currently trending in CEO discussions: 1. AI, 2. sustainability, and 3. election. These influential topics have captivated boardrooms worldwide and are shaping the future investment priorities for companies across various industries.

Key insights:

• According to the latest “What CEOs talked about” report, three themes gained noticeable traction in Q1 2024: 1) AI, 2) sustainability, and 3) elections.
• AI discussions continue to move away from ChatGPT and other chatbots and toward individual technologies, especially GPUs and LLMs.
• Economic topics like recession significantly declined, though inflation saw a bump in mentions in Q1 2024.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “In Q1 2024, CEO discussions clearly reflect a growing focus on AI, with one in three executives highlighting its significance during their earnings calls. The discussions around GPUs and LLMs have seen a notable increase, indicating a deeper engagement with the technological aspects of AI. There is also a renewed emphasis on sustainability, although we have yet to surpass the engagement levels of Q1 2022. The report “What CEOs talked about in Q1 2024″ offers a comprehensive analysis of current executive dialogues. My primary insight? Both AI and sustainability are poised to be pivotal in shaping 2024.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“In Q1 2024, the hype around ChatGPT has decreased further, and executives are increasingly discussing how to generate value by integrating LLMs into their operations and products.”

The big picture

In Q1 2024, economic concerns remained the most discussed topic area in corporate earnings calls. Inflation remained the most mentioned tracked keyword, rising 7% quarter-over-quarter (QoQ) to 48% of calls; however, recession and uncertainty had fewer mentions, down 22% and 11% QoQ, respectively.

AI and its related terms (e.g., generative AI, data centers, and LLMs) continue their rise.

Key rising themes in Q1 2024

1. AI

In Q1 2024, discussions regarding AI rose 17% quarter-over-quarter (QoQ) to 32% of earnings calls. This constitutes a new peak after a noted short decline in mentions of AI in corporate earnings calls in Q4 2023. Additionally, AI-related terms witnessed rises in their discussions:

• Generative AI (GenAI): +10.4% QoQ to 8.8% of calls
• Data center: +20.6% QoQ to 9.4% of calls
• LLM: +43% QoQ to 2.1% of calls
• AI infrastructure: +34.5% QoQ to 0.7% of calls

Discussions around US-based semiconductor vendor—and leading provider of data center GPUs—NVIDIA experienced a rise of 19% QoQ to 2% of earnings calls. This follows a slight QoQ decline in Q4 2023.

Following a trend noted in Q4 2023, GenAI has further cemented its separation from the groundbreaking ChatGPT as discussions seem to focus more on actual applications of AI rather than impressive chatbots.

Executives from nearly all verticals discussed how to integrate AI into their products and operations or how to prepare themselves for the age of AI. The CEO of British multinational oil and gas company Shell, for example, highlighted how AI assists their engineers in detecting anomalies remotely. The CEO of India-based IT services and consulting company Tech Mahindra announced that the company plans to upskill all IT staff in AI-related skills in the next financial year.

Key quotes on AI

“We have millions of sensors collecting over 5 trillion rows of data that our AI models, combined with our conventional models, used to monitor equipment 24 hours a day, 7 days a week, alerting engineers to anomalies from a distance.” – Wael Sawan – CEO, Shell plc, February 01, 2024

“We plan to train 100% of our IT talent in AI in FY 2025.” – Mohit Joshi – CEO, Tech Mahindra, January 24, 2024

Some executives discussed their LLM strategy quite in-depth. For example, US-based software and data company ZoomInfo’s president, David Travers, shared that the company is integrating LLMs agnostically. ZoomInfo developed its own LLM to support its operations but partnered with Anthropic to support its Copilot offering. It also leverages OpenAI’s ChatGPT in other aspects of its operations and offerings. Meanwhile, US-based business intelligence software and services provider MicroStrategy’s CEO, Phong Le, stated the company utilizes retrieval-augmented generation (RAG) to combine its data with OpenAI LLMs to support its flexible AI bot offering across industry verticals.

2. Sustainability

Sustainability had a resurgence in Q1 2024, climbing 18.5% QoQ to 21.8% of earnings calls. This follows a drop to its lowest point in two years in Q4 2023. Further, keywords related to sustainability also had significant gains:

• Energy efficiency: +50.4%QoQ to 3.2% of calls
• Renewable energy: +33.3% QoQ to 6% of calls
• Emission: +32.2% QoQ to 14.8% of calls

Since its peak in Q1 2022 at 26.6% of earnings calls, sustainability has generally hovered around 22% (±4 percentage points) of earnings calls, indicating it remains a topic of interest to executives. In context to this, February 2024 was the hottest February on record and the 9th consecutive month of record-breaking monthly averages. While the share of companies discussing has not grown much in recent years, vendors that market sustainability-related products and services frequently point out the business value, such as Germany-based automation and digitalization company Siemens’ CEO highlighting sustainability as a driver for business in almost every market.

Key quotes on sustainability

“Sustainability is a secular business driver in almost every market segment, such as electrification and renewables integration, energy efficiency, or safety in buildings, among others.” – Roland Busch – CEO, Siemens AG, February 13, 2024

“We also installed solar panels providing an extra 2.4 megawatt peak power compared to last year, and […] renewable energy community—the first ever energy community in Italy to be backed by an industrial company for the benefit of its local area.” – Benedetto Vigna – CEO, Ferrari N.V., February 1, 2024

3. Elections

2024 marks a year of elections in many countries and regions across the globe. Citizens of India, Indonesia, the EU parliament, the U.K., and the US are all scheduled to cast votes in upcoming elections this year—with Russia just having concluded its elections—and executives appear aware of this fact. The share of earnings calls mentioning election rose 28.4% QoQ to 17.2% of earnings calls. Notably, only 16.6% of companies based in North America discussed the upcoming elections, while companies in EMEA (19.2%) and APAC (22%) were much more likely to discuss the topic.

Elections in India (April–June 2024) and the European Union (June 2024) are coming up sooner than the presidential and congressional elections in the US, which are scheduled for November 2024. The analysis of earnings calls during the last presidential election gives a taste of what we can expect: 32% of all CEOs of North American earnings calls discussed the elections in Q4 2020. One keyword that had not been discussed for a while is rising strongly again: Trump. The keyword Trump rose 450% QoQ (though to only 0.8% of earnings calls).

Declining themes in Q1 2024

1. ChatGPT

While GenAI and LLMs appear to be developing into their own distinct topic areas within earnings calls, ChatGPT has further declined (-19% QoQ) in its mentions and was the only tracked AI-related term that did so.

This does not mean ChatGPT’s usage has declined. As of March 1, 2024, it had over 180 million users, and many companies have incorporated it into their operations. However, the release of ChatGPT and the availability of LLMs in easy-to-use web applications sparked new hype around AI in general. Now, as mentioned earlier, executives are moving past just ChatGPT and discussing broader AI with a focus on enterprise-grade projects and real-world use cases.

Key quote:

“Last year was the year of AI talk. Now, the conversation will shift to more tangible things, shift features, successful deployments, [and] practical value.” – Matt Calkins – CEO, Appian, February 15, 2024

2. Recession and most other economic concerns

Mentions of recession declined 22.3% QoQ to 7% of earnings calls in Q1 2024, as executives appear to be easing on this concern. Uncertainty also dropped a fair amount (-10.7% QoQ), though it still appeared in a quarter of earnings calls.

Key quote on recession:

“Even though we’ve got moderating economic growth, we are assuming an avoidance of a deep recession […].” – Mike West – President of Moody’s Investor Service, Moody’s Corporation, February 13, 2024

Not all economic concerns have dropped, though. After a slight drop in Q4 2023, inflation bounced 7% QoQ to 47.6% of Q1 2024 earnings calls and remained the most discussed tracked topic. This comes as inflation in the US was higher than economic forecasters had expected in January and February 2024. A jump in gas prices largely fueled this bump, though grocery prices generally remained flat during this period.

Key quote on inflation:

“The inflation has muted to a certain degree, but not gone away.” – Vimal Kapur – CEO, Honeywell, February 20, 2024

Mentions of interest rates also fell 7% QoQ to 35% of earnings calls, the second most-mentioned keyword we tracked. This is unsurprising, as the US Federal Reserve has held interest rates since July 2023. On March 20, 2024, the Fed indicated it still expects to cut interest rates three times in 2024, with the Federal Reserve Chair Jerome Powell adding that the surprising uptick in inflation in January and February 2024 had not changed the Fed’s picture of the economy: a cooling in inflation, though more gradual than previously expected.

3. War

War had the sharpest drop in executive mentions in Q1 2024, decreasing 28.7% to 5.8% of earnings calls. In Q4 2023, the Israel–Hamas war started, and the Russia–Ukraine war carried on, adding to geopolitical uncertainty during boardroom discussions.

However, the decline in the mention of war indicates that most industries have not experienced operational disruptions from these conflicts, even as Yemen’s Houthi rebels have conducted drone attacks on commercial vessels in the Red Sea (with the first fatal attacks on March 6, 2024).

What it means for CEOs

5 key questions that CEOs should ask themselves based on the insights in this article:

1. Sustainability: Given the increasing focus on sustainability, energy efficiency, and emissions in earnings calls, how are we tracking against our own emissions and energy savings goals, and what new sustainability initiatives should we implement or enhance?
2. Elections: How might the upcoming elections in the US, India, Indonesia, and the UK—and related global political shifts—impact our business, and what strategies should we develop to mitigate potential risks?
3. Recession and economy: How should we adjust our financial planning and strategies in response to ongoing muted business confidence, muted global GDP growth for 2024 and 2025, and declining concerns about recession and the current economic landscape by peers?
4. AI: As companies ramp up AI efforts (specifically generative AI), does our company have the necessary infrastructure, talent, and data to test and implement AI solutions effectively? Do we know where AI technologies add the most value to your company and might help us differentiate from our competition?
5. Labor market: Given rising inflation and ongoing skill gaps, how can we ensure our salary structure is competitive enough to attract top talent while also being sustainable for our business?

What it means for those serving CEOs

5 key questions that those serving CEOs should ask themselves based on the insights in this article:

1. Strategic alignment: How can I ensure that our company’s strategy aligns with the current trends in AI, sustainability, and political climates, especially considering their growing importance in corporate discussions?
2. Competitive analysis: What are our competitors doing regarding AI and sustainability initiatives, and how can we differentiate ourselves or learn from their approaches?
3. Risk management: What potential risks (e.g., market changes, regulatory developments, geopolitical issues) should I monitor that could impact our business strategy and operations?
4. Training and development: What training or development programs should we consider to enhance our team’s understanding and capabilities in AI and sustainability?

Long-term vision: How does the current focus on AI, sustainability, and the political landscape influence our long-term business vision and strategy?

The post What CEOs talked about in Q1 2024 appeared first on IoT Business News.

IoT Analytics has published a new analysis that provides an overview and insights into the LPWAN market in 2024.

This analysis is derived from the “Global LPWAN Market Tracker and Forecast 2015-2027 (Q1/2024 Update)” – a tracker that includes market data on worldwide LPWAN connections and module shipments from 2015 to Q4 2023, including market projections for 2024 to 2027.

Key Insights:

• LPWAN connectivity is on the rise. There were nearly 1.3 billion LPWAN IoT connections globally by the end of 2023, according to IoT Analytics’ Global LPWAN Tracker and Forecast 2015–2027 (updated Q1 2024). This is expected to grow at 26% CAGR until 2027.
• NB-IoT comprises 58% of these connections. However, that does not tell the whole story about NB-IoT’s global adoption, as China’s nationwide adoption policy has greatly skewed this number.
• In 2023, licensed LPWAN connections surpassed unlicensed LPWAN connections, even when excluding China’s saturation of NB-IoT, a licensed LPWAN connectivity technology.

Key Quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “In less than a decade, LPWAN technology has transformed from a nascent market into 1.3 billion connections. This remarkable growth, propelled by both licensed and unlicensed LPWAN technologies, underscores the critical role of low-power wide-area connections in powering IoT applications across diverse industries. At IoT Analytics, our dedication lies in delivering precise, actionable insights that empower stakeholders to adeptly navigate the rapidly evolving IoT connectivity landscape.”

Satyajit Sinha, Principal Analyst at IoT Analytics, adds that:

“LPWAN technology is evolving rapidly. Integration with satellite IoT connectivity is a natural progression in the field and will likely pave the way for new applications and connect previously unconnected things. Both NB-IoT and LoRa technologies have important roles to play in this hybrid connectivity model, enhancing the efficiency and reach of IoT applications.”

LPWAN Market overview

There are nearly 1.3 billion LPWAN IoT connections globally, according to IoT Analytics’ Global LPWAN Tracker and Forecast 2015–2027 (updated Q1 2024), which tracks LPWAN market data at a granular level across regions, industries, and types. This represents approximately 8% of the over 16 billion connected IoT devices worldwide in 2023.

The LPWAN tracker forecasts that the number of LPWAN connections will grow at a 26% CAGR until 2027, reaching 3 billion at that time, or 10% of all IoT connections worldwide. Behind this growth is the need for use cases like remote monitoring that require infrequent data transmission and battery operation, which LPWAN is especially suited.

The tracker shares granular LPWAN market data across regions, industries, and technology types, including revenue, shipments, and connections. Here, we will look at three insights from the tracker that merit context, as discussed below:

1. China’s “Big Connectivity” strategy skews global LPWAN connection data
2. LoRa remains the leading LPWAN technology outside of China
3. Licensed LPWAN connectivity technology surpassed unlicensed in 2023, even when excluding China’s weighted adoption rate

Market insight 1: China’s “Big Connectivity” strategy skews global LPWAN connection data

Globally, NB-IoT has the largest share of LPWAN connections at approximately 54%. However, this does not paint a clear picture of the world’s adoption of this LPWAN technology.

In 2016, China—the world’s most populated country—made the nationwide rollout of NB-IoT part of its “Big Connectivity” strategy for 2016 to 2020 to support a wide range of use cases. One such use case is smart metering, in which China is a regional leader in adopting smart gas and water meters. According to the LPWAN tracker, by 2023, ~81% of all LPWAN connections in China were NB-IoT, and the country accounted for ~84% of all global NB-IoT connections.

The following charts help demonstrate the impact of China’s dedicated adoption of NB-IoT on global LPWAN connections. On the top chart, we see NB-IoT’s share of LPWAN connections skyrocket between 2016 and 2023—the timeframe for China’s “Big Connectivity” strategy. However, on the bottom chart, China’s LPWAN data are excluded from the global totals, and NB-IoT’s climb—while significant—is nowhere near as pronounced. By the start of 2024, LoRa had a sizeable lead over the other technologies.

This does not mean NB-IoT is not gaining steam elsewhere, however. As the bottom chart shows, when excluding China, NB-IoT comprised 20% of LPWAN connections in 2023—a quick climb since its 3GPP standardization in June 2016. By 2027, the LPWAN tracker forecasts NB-IoT to reach 23% of China-excluded global LPWAN connections, while LoRa is expected to maintain its lead at 36%.

Interesting new use cases are helping drive NB-IoT’s increasing share of LPWAN connections. In July 2023, Spain-based low-Earth orbit (LEO) constellation satellite operator Sateliot and Spanish multinational telecommunications company Telefónica successfully tested an end-to-end roaming 5G cellular network in space using NB-IoT. In January 2024, IoT Analytics noted this test as the most innovative IoT connectivity technology development in 2023.

Market insight 2: LoRa remains the leading LPWAN technology outside of China

When excluding all LPWAN data from China, LoRa has the leading share of global LPWAN connections at 41%—more than double NB-IoT’s share.

Though LoRa’s share of LPWAN connections is decreasing, the technology’s market is still forecasted to grow at a CAGR of 17% by 2027. Helping drive this growth are smart water and gas metering applications, sustainability applications, such as agricultural resource management and optimization, and asset monitoring and tracking solutions, such as US-based semiconductor manufacturer Semtech’s LoRa Edge technology.

Market insight 3: Licensed LPWAN connectivity technology surpassed unlicensed in 2023, even when excluding China’s weighted adoption rate

According to the LPWAN tracker and forecast, licensed LPWAN technology reached a milestone in 2023: its share of LPWAN connections surpassed that of unlicensed LPWAN connections without the assistance of China’s weighted adoption of NB-IoT, a licensed LPWAN technology.

When considering China’s adoption of NB-IoT, licensed connections had already surpassed unlicensed ones by 2020. However, as shown above, NB-IoT’s share of LPWAN connections is not representative of global adoption due to China’s dedicated nationwide rollout of the NB-IoT. Now, without China’s significant boosting considered, it appears that licensed connections are on the rise worldwide and are forecasted to comprise 58% of LPWAN connections in 2027.

Helping drive the rise of licensed LPWAN are cases like smart city management. China offers a good use case with its smart meters, but there are other case studies around the world. For example, Germany-based IoT sensor and data analysis company Sentinum sought to address inefficiencies with public waste management, such as waste collection trucks driving routes where some waste bins are not full. It wanted to use LPWAN connectivity due to its low-power demands, further adding to sustainability.

Sentinum partnered with Vodafone, a UK-based global telecommunications provider, to leverage Vodafone’s licensed LPWAN (specifically, NB-IoT) to reliably relay bin fill data to Sentinum’s backend servers and alert municipal waste disposal staff what bins need collection. Vodafone notes that some applications can experience a time savings of 40% and a CO2 reduction of around 25%.

Analyst assessment: Key LPWAN trends to watch

These insights are from the updated Global LPWAN Tracker and Forecast 2015–2027, which readers can leverage for granular data across regions and industries. Later in 2024, IoT Analytics is planning to publish a full LPWAN market report, which will dive into the LPWAN market data, trends, and company insights. For now, here are two trends worth watching: 1) cooperation and convergence and 2) addressing LPWAN’s limitations.

Trend 1: Convergence and collaboration

The LPWAN industry has evolved significantly over the last decade, and the technology has become more popular. With the market maturing and taking hold within greater IoT connectivity, there appears to be a shift from the early days of high competition to an increased focus on convergence and cooperation.

For example, on July 25, 2023, US-based semiconductor manufacturer Semtech Corporation announced a collaboration with UnaBiz, a Singapore-based IoT solutions provider specialized in LPWAN connectivity, to integrate Unabiz’s Sigfox 0G technology into Semtech’s LoRa Edge and LoRa Connect platforms. This partnership aims to create a cost-effective, versatile platform by offering Sigfox technology support in Semtech’s LR1110, LR1120, and LR1121 products through Sigfox-specific APIs.

Moreover, this initiative highlights the industry’s push towards sustainable, adaptable IoT solutions, allowing customers to choose optimal connectivity based on their unique use cases and sustainability objectives. It is these solutions that will drive the market through 2027, and possibly beyond.

Trend 2: Addressing LPWAN limitations

There are still some limitations of LPWAN that need to be addressed. For example, LPWAN was designed around point-to-point connectivity, not large-scale connectivity with heavy data loads. Higer packet transmission can make LPWANs susceptible to interference from a number of sources (e.g., atmospheric/electrical noise, other radio networks, or even jamming).

To address this, transmission protocols need to ensure that complete data structures are delivered to their endpoints. One notable approach gaining attention in the LPWAN field is from mioty Alliance, a group of businesses, institutes, and engaged individuals aiming to enhance IoT connectivity solutions.

The alliance’s solution is to leverage the Telegram Splitting Multiple Access (TSMA) method to split data packets into smaller subpackets at the sensor level and transmit the packets over different frequencies and time marks. An algorithm on the receiving end will monitor for mioty subpackets and reassemble them into complete messages, ensuring complete messages are received even if one or a few frequencies are experiencing interference.

The post LPWAN market 2024: Licensed technologies boost their share among global 1.3 billion connections as LoRa leads outside China appeared first on IoT Business News.

IoT Analytics has published a new analysis that highlights 10 emerging IoT trends driving market growth.

This analysis is derived from the comprehensive “State of IoT – Spring 2024” – a report on the current state of the Internet of Things, including market updates and projections, the latest trends, market sentiments, investments, M&As, industry expert opinions, and more.

Key Insights:

• According to the latest State of IoT – Spring 2024 report, IoT remains a top-three corporate technology priority.
• While AI has surpassed IoT in corporate prioritization, combining IoT and AI is on the rise and seen as a tailwind for the $236-billion IoT market rather than a disrupter.
• IoT Analytics identified 40+ current IoT market trends in this research, 10 of which are shared below.

Key Quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks:

“In 2023, the IoT market demonstrated significant resilience among economic fluctuations and geopolitical tensions. We now estimate a robust growth of 17% per annum through 2030. This growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity within the IoT sector.”

IoT remains a top corporate priority on the back of the current AI wave

IoT remains a top priority. IoT remains a top-three corporate technology priority while AI has taken over as the top technology priority, according to the latest 148-page State of IoT – Spring 2024 report. In recent surveys from PWC, KPMG, and BCG, respondents ranked IoT second or third after AI in terms of investment prioritization for emerging technologies, with AI coming in first across the board.

AI is a tailwind for IoT. The latest research finds that the growth of AI is a strong tailwind for the $236-billion IoT market, as companies are gaining interest in both AI and IoT within their organizations. One indication is from IoT Analytics’ analysis of company earnings calls: since Q3 2022, the mention of these two technologies in the same earnings call rose by 61%.

40 current trends identified by the IoT Analytics team. IoT Analytics’ market analysis relies on the valuable findings of its research analyst team and input from industry experts and advisors. These professionals contributed greatly to the spring 2024 State of IoT report, which showcases nearly 40 IoT market trends, along with IoT market data, recent IoT news and developments, and the performance and activities of IoT companies. The analysis shows that AI is not the only trend that will help drive the IoT market—10 of the trends discussed in our report can be found below.

IoT market expected to continue its growth path. IoT Analytics assesses that the 10 trends discussed in this article (among many more) will contribute to an IoT market CAGR of 17% until 2030, which is a cautious downward revision from the forecasted 19% CAGR in early 2023 but nonetheless a testament to the strength of the technology and its impact in a variety of markets.

CEO quote: “In 2023, the IoT market demonstrated significant resilience among economic fluctuations and geopolitical tensions. We now estimate a robust growth of 17% per annum through 2030. This growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity within the IoT sector.” – Knud Lasse Lueth, Chief Executive Officer

Spring 2024 macro environment outlook: Lingering economic uncertainty, but IoT remains a key investment

Inflation coming down. The high inflation rates that we saw in 2023 seem to be subsiding. The Euro area inflation estimate for January 2024 was 2.8%, down from 2.9% in December 2023. In the US, consumer inflation has greatly eased from its peak of 9.1% in June 2022; it currently stands at 3.2% in February 2023. In Asia, some countries witnessed their lowest inflation levels since 2022.

Muted global economic growth projections. However, despite inflation progress, global economic growth projections are still below the historical annual average. The IMF forecasts a 3.1% global economic growth in 2024 and a 3.3% growth in 2025. Additionally, by December 2023, China—the second-largest economy and the country with the most connected devices—experienced its longest deflation streak since 2009, with prices declining for the third consecutive month. On the flip side, India’s economy has been outperforming the rest of the world.

IoT markets affected, but positive sentiment shows. The IoT market has seen positive economic news lately. Going into 2024, the Global Supply Chain Pressure Index is back to its long-term average, indicating a normalization of supply chains after stormy times during the pandemic. Additionally, business sentiment around IoT appeared overall positive, and many IoT companies reported significant year-on-year growth in both revenue and gross margin within the IoT sector in Q4 2023 (e.g., Supercom, Lantronix, and Globalstar). Unfortunately, some IoT companies have reported revenue declines and market weakness.

Analyst quote: “India has emerged as the ‘new China’ in terms of growth outlook.” – Philipp Wegner, Principal Analyst—Data

With this market backdrop, the following is a list of 10 notable IoT and tech-related trends and opinions identified by the IoT Analytics team as part of the research for the report with accompanying commentary:

Trend 1: Semiconductor companies invest in embedded chipset security

Semiconductor companies are increasingly investing in embedded chipset security to address the growing security threats IoT devices face. Securing hardware at the chipset level with secure elements and physical unclonable functions (PUFs) can help protect data flowing from edge devices to the cloud.

Example: The ecosystem of US-based semiconductor company Intel is growing with security partners like US-based digital authentication company Intrinsic ID. In February 2024, Intel Foundry added Intrinsic ID to its Accelerator IP Alliance program, aiming to ensure the availability of hardware-based root-of-trust solutions for the Foundry’s members. Accepting that security and reliability are valuable for applications, the Foundry opened access to Intrinsic ID’s QuiddiKey X00 product family of root-of-trust (RoT) solutions, which use standard SRAM as a PUF to generate a hardware RoT without needing additional security-dedicated silicon.

Analyst quote: “Semiconductor companies are at the forefront of tackling [the growing security threats IoT devices face], focusing on investing in embedded chipset security, as hardware forms the foundational layer.” – Satyajit Sinha, Principal Analyst – Connectivity and hardware

Trend 2: Industrial automation hardware is becoming more intelligent with the integration of AI chipsets

With the advancement in AI technology, companies are now looking to leverage AI at the edge, increasing the demand for real-time data analytics also at the edge. AI chipsets are also becoming smaller in size while growing in power. This trend has led to the emergence of IPCs and gateways embedded with AI chipsets, resulting in edge AI equipment that can perform parallel computations and train algorithms with very low computational response latency.

One benefit of using AI chipsets at the edge is the acceleration of data processing directly on the industrial equipment. This, in turn, reduces network traffic and enhances security, as the amount of data going to the cloud for processing is reduced.

Example: At SPS in November 2023, Germany-based automation and digitalization manufacturer Siemens presented its SIMATIC IPC520A Box PC embedded with 6-core NVIDIA Carmel for edge capabilities and NVIDIA Jetson Xavier NX GPU for A, making it suitable for AI-oriented operations. The IPC520A is designed to work seamlessly with AI-based applications across various industries, including factory automation and logistics.

Analyst quote: “Advancements in AI chipsets specifically designed for the edge are noteworthy. Embedding AI chipset in edge hardware such as IPCs and gateway is bringing decision-making closer to the edge and opening doors to new IoT applications such as machine vision.” – Kalpesh Baviskar, Market Analyst—Connectivity and hardware

Trend 3: The race for generative AI solutions in manufacturing has begun

Many industrial generative AI (GenAI)-based solution showcases are popping up. Vendors in the industrial and manufacturing space are racing toward developing GenAI-based solutions around coding, troubleshooting/support, operational analytics, and generative design, among others.

Examples: The following are just two of the six showcase examples found in the State of IoT – Spring 2024 report:

• At SPS 2023, Germany-based automation technology company Beckhoff showcased its TwinCAT Chat for the TwinCAT XAE engineering environment. The TwinCAT Chat Client enables AI-supported engineering to automate tasks such as the creation or addition of function block code, code optimization, documentation, and restructuring.
• In November 2023, Canada-based industrial AI software company Canvass AI announced the next evolution of its industrial AI software with Hyper Data Analysis. Through the use of GenAI, the Canvass
  AI software now incorporates learnings from text and visual-based data—adding it to production data streams—to advance traditional time-series-based AI insights for applications such as visual inspection, predictive maintenance, and quality within the process industries.

Analyst quote: “15 industrial automation and related vendors at SPS 2023 told us that GenAI is currently one of their top technology priorities. Moreover, we observed that these GenAI-based solutions are mostly in the stage of showcasing their capabilities rather than being widely available to the public. We believe this is to evolve in the coming months when vendors will go ‘live’ with these products for purchase.” – Fernando Brügge, Senior Analyst—Industrial IoT and AI

Trend 4: Generative AI has a positive (not negative!) impact on the manufacturing workforce

The race to integrate GenAI solutions in manufacturing—and how it differs from other technologies so far—revolves around the speed of adoption and the level of investment in the technology. Within three months of its public launch, ChatGPT reached an estimated 123 million users, an incredible feat for a new type of product. Additionally, soon after ChatGPT’s launch, Microsoft made a $10 billion investment in OpenAI, helping increase ChatGPT’s profile. This investment also showed the seriousness big tech companies like Microsoft are placing in this technology, leading companies across industries to question how they could leverage GenAI in their processes.

Adoption of new technology in manufacturing is often associated with negative impacts on the workforce. However, GenAI adoption in manufacturing is expected to boost employment and upskilling, shifting focus from automation to strategic growth. With GenAI contributing potentially $2.6–$4.4 trillion to the global economy annually, according to McKinsey, manufacturers are likely to deepen their investment in AI technologies.

However, AI’s impact on the workforce appears counterintuitive to common automation narratives. According to the Manufacturing Leadership Council, 32% of manufacturers anticipate an increase in headcount due to AI, suggesting that AI will create new roles and require upskilling rather than just automating existing ones. With 96% of manufacturers projecting increased investment in AI, there is a clear trend toward embracing AI for cost savings, growth, and revenue generation.

Emerging roles will likely include AI strategy managers and data specialists, reflecting a shift toward higher cognitive work.

Advisor quote: “The urgency for upskilling is underscored by the current lack of a dedicated AI training budget in 65% of manufacturing firms, signaling a potential increase in investment in human capital.” – Jeff Winter, Industry 4.0 expert and advisor

Trend 5: Companies are in danger of neglecting tech adoption basics in the rush to generative AI

GenAI is everywhere. Vendors are looking for ways to implement it in their products or to create new ones, and end users are eager to adopt. This rush, however, is not always helpful when it comes to adopting new technologies. The hype can often shift the mindset of adopters and vendors alike from “What technology should I use to alleviate X pain point?” to “How should I use this technology to alleviate some (sometimes non-existent) pain point?”

In almost all the surveys that IoT Analytics conducts—be it about IoT use case adoption, Industry 4.0, IoT software, or similar—“having a set goal” is always on the list of success factors that respondents mention. This is often forgotten whenever a new technology promises to “change the way we work” (the metaverse, for example).

Analyst quote: “[AI] should be treated like any other technology. First, think of the why, who, and how before deciding on implementing it. Second, if you are a (software) vendor, you should also keep in mind that being fast to innovate is not always the most important factor to keep your customers happy.” – Dimitris Paraskevopoulos, Senior Analyst—Quantitative data

Trend 6: Marketplaces are gaining in importance for technology procurement

Companies and sellers alike are embracing the subscription-based economy and seeking to simplify the procurement process. In January 2024, IoT Analytics published an article delving into the rise of B2B marketplaces, noting that B2B marketplaces are the fastest-growing procurement channel for software.

Analyst quote: “Cloud hyperscaler marketplaces currently lead in cloud-based software spending since many businesses have already committed cloud spending that can be utilized to procure software from these platforms.” – Justina-Alexandra Sava, Market Analyst—Software

Trend 7: Data fabric is emerging as an advanced evolution of data lakes

Though a relatively new term, data fabric describes a comprehensive data integration and management framework. It encompasses architecture, management tools, and shared data sets and is designed to assist organizations in handling their data.

Data fabrics differ from data lakes in that they go beyond storing raw data and from data warehouses in that they handle only processed or refined data. A core benefit of data fabrics is they offer a cohesive, consistent user interface and real-time access to data for all members of an organization, regardless of their global location.

Examples:
In 2023, several large data management vendors either upgraded their already existing data fabric solutions or launched new solutions:

• In February, US-based data integration platform provider Talend—one of the early users of the term data fabric—announced upgrades to their Talend Data Fabric solution, which was initially launched in 2015.
• In May 2023, US-based technology and software company Microsoft introduced Microsoft Fabric and launched it in November.

Analyst quote: “Given the increase in data complexity because of the exponential growth in big data, propelled by hybrid cloud, AI, IoT, and edge computing, there seems to be a good opportunity for vendors [offering data fabric].” – Mohammad Hasan, Market Analyst—Software and cloud

Trend 8: Hyperscalers pivot their edge strategies to innovate and secure their IIoT market position

Cloud providers are strategically adapting to the evolving IIoT market. In recent developments within the IIoT landscape, important shifts have occurred among major cloud service providers, with more focus on edge and containerization strategies.

Examples:

• Microsoft pivots its Azure IoT strategy toward Kubernetes. Microsoft has notably redirected its strategy toward Kubernetes, a move signaling a pivot within its Azure IoT Operations offering. This transition, accompanied by organizational restructuring and the discontinuation of the Azure Certified Device Catalog, highlights how Microsoft’s IoT strategy seems to evolve as technology and market dynamics shift.
• Google ends IoT Core but keeps Manufacturing Connect via Kubernetes. Similarly, Google’s decision to terminate its IoT Core offering in August 2023 has prompted attention. Despite this closure, Google’s IIoT solution, Manufacturing Connect, remains viable through its Kubernetes-compatible architecture, a strategic alignment reflecting the company’s technical prowess in this domain.
• AWS boosts IIoT investment with Sitewise and Monitron enhancements. AWS seems to double down on the IoT edge, which is particularly evident in the revitalization of Sitewise and other offerings like Monitron.

Advisor quote: “As the IIoT market evolves, cloud giants like Microsoft, Google, and AWS are moving further to the edge by embracing Kubernetes and enhancing edge computing capabilities. Their strategies revolve less around serving individual use cases themselves but rather participating in the edge (software) platform layer, which serves as the basis of digitalization for their partners and customers.” – Matthew Wopata, Edge solutions expert

Trend 9: Industrial vendors are strongly investing in DataOps solutions

Several vendors are investing in industrial DataOps solutions to tackle data integration and analysis challenges.
Industrial DataOps is an approach to data integration focusing on enhancing data quality through contextualization and modeling. This approach is experiencing growing attention within the industrial connectivity space.

Examples:

Some of the key vendors that offer industrial DataOps for data contextualization and modeling include:

• New vendors: Cognite – CDF, HighByte – Intelligence Hub, Prosys – OPC UA Forge, Litmus – Litmus Edge, Element – Unify, and Crosser – Flow Studio
• Industrial incumbents: ABB – Ability Genix, Aveva – PI System, Aspentech – Inmation, GE Digital – Asset Modeler, and Halliburton – DecisionSpace 365

Analyst quote: “In the world of AI, OT data stands as the cornerstone. It’s the quality and context of this data that truly empowers insights. We are seeing vendors aggressively advance DataOps tools for modeling and contextualization, with both new entrants and established OEMs perfecting their solutions. This concerted effort underscores the pivotal role of enhancing the quality of data from varied assets/software in unlocking digital transformation’s full potential.” – Anand Taparia, Principal Analyst—Industrial IoT

Trend 10: Robots charged per hour are starting to replace manual labor due to labor shortage

Manufacturing companies can benefit from equipment as a service (EaaS) by replacing labor-related operational expenses with another type of operational expense: robotics as a service (RaaS). RaaS is a relatively new business model, where a robot is provided by a machine builder on an outcome-based basis (paying per parts produced with the equipment) or runtime basis (paying per hours of equipment used) instead of as a direct purchase.

Example: US-based truck trailer chassis manufacturer Cheetah Chassis chose to hire welding robots per hour and explained that it could not find enough welders to fulfill demand. Its CEO, Garry Hartman, explained that it had trialed robotics before, but it was unsuccessful because it did not have the capacity to program and service robots. With RaaS, Cheetah Chassis can now enjoy the benefits of robotics without having to do so because it is provided by the RaaS vendor.

Analyst quote: “Companies are more likely to consume equipment by the hour if they are trying to fill in the gaps in the workforce (which is already paid by the hour) rather than purchasing new equipment.” – Matthieu Kulezak, Senior Analyst—Industrial IoT

Conclusion

The IoT sector is undergoing transformative changes. The new 148-page State of IoT – Spring 2024 report highlights the continuous evolution and resilience of the IoT market, driven by technological advancements and strategic shifts.

The shift of hyperscalers towards edge and containerization strategies, the integration of AI into industrial automation, the advent of generative AI in manufacturing, and the rise of data fabric solutions represent just a few of the dynamic developments redefining the enterprise IoT ecosystem. These trends, alongside other market data in the report, not only reflect the current state of the market but also provide a glimpse at future growth and innovation.

In navigating this landscape, it is crucial for businesses to stay informed and adaptable. With a projected CAGR of 17% until 2030, the potential for growth and transformation in the IoT sector is immense.

The post State of IoT Spring 2024: 10 emerging IoT trends driving market growth appeared first on IoT Business News.

2023: First time, massive decrease in cellular module market shipments. Inventory normalization expected in 2nd half 2024.

The cellular non-handset device market is expected to decline significantly by 11.5% YoY in 2023 due to the inventory adjustments after supply chain disruption, inflation and demand decrease in 2022-2023. It is assumed that the impact of inventory adjustments will continue until the first half of 2024.

Industrial cellular module market (excluding automotive telematics) is estimated to decrease by 14.3% for the bigger the inventory in the whole supply chain. This is the first time decrease since 2009, we started to follow cellular module market. Complex supply chain from IC, module, distributor, and device OEM/ODM, makes it difficult to predict the inventory situation. However, suppliers expect that inventory normalize at some point in 2nd half of 2024.

Among non-handset cellular modem applications, only cellular CPE and automotive telematics showed steady growth in 2023. However, for automotive, tier-1 and tier-2 suppliers start to adjust IC chip inventory in late 2023, 2024 module demand is expected to soften.

Cellular Industrial/Automotive Module Market Forecast by Vertical Application

The cellular industrial and automotive module market grew significantly in 2021-2022. Yet, it is estimated to decline significantly by 12% on YoY in 2023. From the second half of 2022 onward, the decline in demand due to the economic downturn and excess inventory in relation to supply chain disruptions had an impact. Inventory adjustments are expected to continue until 1H/2024, and the market growth rate is estimated to be flat to low single-digit growth in 2024. Industry expects the recovery in 2H2024, then back to high single-digit growth after 2025.

The main uses of industrial cellular module are as follows in descending order:
Smart Meter (Electric, Gas, Water Meter): 25% (2023)
Transportation (Before/After-market Telematics, Vehicle Tracking, Train, Marine) : 22% (2023)
GPS Asset/Life Tracking (Logistics, Heavy Machinery, Livestock, Human…) : 15% (2023)
Payment/Retail (Point of Sales (mPOS), QR Code Payment, ATM, Vending Machine…) : 14% (2023)
Security/ Automation (Security Camera, Security Robot, Gate System, Alarm, Sensor) : 8% (2023)
Healthcare (Patient Vital Monitoring, Medical device monitoring, Vaccine management, smart pill box…) : 5% (2023)

Among Cellular industrial IoT applications, smart utility meters (25%), GPS tracking (telematics + transportation + asset tracking) together accounts over 60% market share in shipment volume base.

Other main applications include sharing economy (bike, e-scooter…), industrial computer, PoC (Push Over Cellular) handset, EV charging pile, smartphone charging pile, remote environment monitoring, smart agriculture, and so on.

Cellular M2M Market Forecast by Standard

Many Industrial IoT applications do not require broadband connectivity. On the other hand, wide area coverage and stability in connectivity is important.

Looking at the industrial M2M module market in 2023 by standard,

• Cellular LPWA (LTE Cat.M and NB-IoT) decreased from 33% (2022) to just under 29% (2023).
• LTE Cat.1/Cat.1 bis expands from 35% (2022) to 39%.
• 5G for automotive increased slightly. Market share will increase from 2022: 0.3% to 2023: 0.6%.
• LTE will expand slightly from 21% (2022) to 23% (2023).

Trends after 2023 are as follows:

• 2G/3G to LTE Cat.1 bis migration in Europe and emerging markets
• Decrease of NB-IoT in Chinese market, migration to LTE Cat.1 bis
• Migration from LTE Cat.4/ Cat.1 bis to 5G RedCap in Chinese market
• 5G RedCap to be introduced in developed markets in 2026-2028
• Migration from LTE Cat.M to LTE Cat.1 bis in some applications in Europe, North America, etc.
• Migration from LTE to 5G in the automotive telematics

2G still has a market share of 7.6% in 2023. It is almost disappear in mature market, but still mainstream in South America, Africa, India and other emerging APAC countries. 2G will decrease to only 1% by 2029, migrate to LTE Cat.1 bis and LPWA.

LTE/ LTE-Advanced have a 23% share in 2023. It is widely used in the automotive, payment terminals, camera/video, industrial gateways, high end telematics, and so on. It will migrate to 5G eMBB/RedCap in the long run. The market share will decrease to just under 10% by 2029.

The market share of industrial 5G is only 0.6% in 2023. Private 5G/local 5G have been promoted in China and other developed markets, but they have not yet reached a large volume shipment. Excluding automotive telematics, the volume of 5G module shipments in 2023 is expected to be less than a million units. With the introduction of 5G RedCap, it is anticipated that the migration from LTE to 5G will progress in a wider range of applications. Thanks to the 5G RedCap market expansion, 5G will accounts about 17% of share in 2029.

LTE Cat.1 bis rapidly expanded in the Chinese market since 2021. In 2022-2023, Chinese low-cost LTE Cat.1 bis module has begun to be deployed in Europe, MEA, and APAC. It is expected that LTE Cat.1 bis will be introduced in Japan and USA from 2024. The market share of LTE Cat.1 + Cat.1 bis is estimated to increase to 39.3% in 2023 and exceed 50% in 2028-2029. Compared to LTE Cat.M/NB-IoT, the benefit of LTE Cat.1 bis is that it does not require base station upgrades. In the long term, LTE Cat.1 bis will migrate to 5G RedCap.

LTE Cat.M market is expected to see a decline first time in 2023 due to inventory adjustments. While demand is expected to decline in Europe and other countries, North America and Japan shows steady growth. The LTE Cat.M market share in 2022-2023 is approximately 10%. In 2029, it is estimated to grow to 11.5%. 5G eRedCap with 5MHz bandwidth operation is assumed to replace LTE-M in the long future.

The NB-IoT market turn to decline trend in 2023. In China market, competition between NB-IoT and LTE Cat.1 bis began in smart utility meter (gas, water) market in China. NB-IoT market is also expected to decrease outside China. Even in India, which is expected to become a large market for NB-IoT in the future, there is a possibility that LTE Cat.1 bis will take place of NB-IoT in the future.

Cellular Module & Modem Chip Market Share

In the industrial cellular module market, Quectel has about 39% of market share in 2023 based on shipments and 30% on a revenue basis. Quectel keeps top market share since 2017. Suppliers ranked second and below have a share of less than 10%, which is a big difference from Quectel. After Quectel, Telit-Cinterion, SunSea, Fibocom, ChinaMobile IoT, Neoway, ublox, Sierra Wireless, Rolling Wireless, MeiG accounts major share.

Since China market, which accounts over 50% of global cellular industrial module market, is occupied by local module suppliers, Chinese module suppliers counts about 83% of market share in shipment volume base. Among Chinese module suppliers, Quectel, Sunsea, Fibocom/Rolling Wireless also sells large number of cellular modules to overseas market.

The main module vendors by communication standard are as follows:

• LTE: Quectel, Fibocom, Rolling Wireless, SunSea, MeiG, LG Innotek, Telit-Cinterion, WNC
• 5G: Quectel, MeiG, Fibocom, Sierra Wireless, WNC
• LTE Cat.1 Global: Telit-Cinterion, Quectel, ublox、Sierra Wireless, Abit
• LTE Cat.1 bis China: Quectel, Fibocom, SunSea, Neoway, MeiG, MobileTek, Lierda
• LTE Cat.M: Telit-Cinterion, Quectel, u-blox, Sierra Wireless
• NB-IoT Module: Quectel, ChinaMobile IoT, Lierda, MobileTek, SunSea, Goldcard, Luat

For cellular modem chipset for cellular M2M module, Qualcomm, UNISOC, Eigencomm, and ASR have a large share, followed by Xinyi, MediaTek and Sony Israel. In shipment volume base, Qualcomm is by far the largest (2023: 34%), and the three major Chinese companies each have a market share of 14-15%. Chinese chip suppliers, all together accounts about 55% of share in 2023.

The major chipset suppliers by cellular standard are as follows:

• GSM: MediaTek, UNISOC
• 3G: Qualcomm
• LTE: Qualcomm, Intel, Sanechips, ASR, UNISOC
• 5G eMBB: Qualcomm, UNISOC, HiSilicon
• 5G RedCap: HiSilicon, Qualcomm
• LTE Cat.1 Qualcomm, Intel, Sequans
• LTE Cat.1 bis: ASR, UNISOC, Eigencomm, AICXTek
• LTE Cat.M: Qualcomm, Sony, ublox, Nordic, Sequans
• NB-IoT: Eigencomm, Xinyi, UNISOC, HiSilicon, M-Link

Qualcomm is strong in high end (5G, LTE-Advanced) cellular modem segment. In the LTE market, Qualcomm still has a large market share, but ASR, UNISOC, and Sanechips are growing in low cost segment. The LTE Cat.1 bis and NB-IoT chipset markets, which have expanded in the Chinese market, are dominated by Chinese IC suppliers. There are no Chinese chip suppliers in LTE Cat.M market, which is not used in China. The 5G RedCap chipset, which will be diffuse in the Chinese market first, other than Qualcomm, Chinese IC suppliers such as HiSilicon, UNISOC, ASR, Innobase, Eigencomm, and AICXTek are expected to take major market share in the early stages.

The post TSR Market Update: Cellular IoT Modules appeared first on IoT Business News.

IoT Analytics has published a new analysis focusing on smart meters.

It is derived from the comprehensive “Global Smart Meter Market Tracker”. The tracker includes installed base, shipments and shipment revenues for electricity, gas and water smart meters. The current analysis underscores the global adoption rate of smart electricity meters in 2024, providing an in-depth regional exploration and market forecast.

Key insights:

• By the end of 2023, 1.06 billion smart meters (electricity, water and gas) have been installed worldwide, according to IoT Analytics’ Global Smart Meter Market Tracker 2020–2030.
• Smart meters enable utility service providers across the world to digitalize their distribution infrastructure and services efficiently with near real-time data.
• North America has the most mature smart electricity meter market, with nearly 77% electricity meter market penetration, while Latin America has largely lagged in its adoption of the technology. Some European Union countries and the East Asian region, too, have high rates of smart electricity meter market penetration.
• South Asia, Latin America, and Africa represent a high-growth potential for smart meters, as some regional governments have become convinced of the need to update their aging grid infrastructure and are more actively engaging with smart grid industry stakeholders to develop regulatory policies to drive the adoption of smart meters.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “IoT-based smart electricity meters have become a reality in the world but adoption varies greatly by country and region with countries like Sweden, France, or Canada having completed or nearly completed their roll-outs while others like Germany are yet to start their initiative in a meaningful way. India is likely to see the largest roll-out in the coming years.”

Adarsh Krishnan, Principal Analyst at IoT Analytics, adds that: “Digital transformation is sweeping the utility industry as global service providers’ smart meter deployment, a foundational smart grid technology, exceeds installed base of 1 billion units. While advanced economies embrace feature-rich smart meters, emerging markets focus on more cost-effective solutions for their grid upgrades. Furthermore, future advancements in AI and edge computing will bring greater operational efficiencies and innovative consumer services, creating sustainable and resilient smart grids.”

By the end of 2023, Utility Service Providers (USPs) around the world will have installed over 1.06 billion smart (electricity, gas, and water) meters, according to IoT Analytics’ updated Global Smart Meter Market Tracker 2020–2030. As IoT devices, smart meters are enabling energy and water USPs to build resilience into their operations with near real-time data from their distribution networks. With sustainability and the digitalization of utilities gaining traction worldwide, the installed base of these devices is expected to exceed 1.75 billion by 2030 (CAGR 6%), making the smart meter market a market to watch closely.

Smart electricity meter adoption is far ahead of the adoption of smart gas and smart water meters at this point, though the picture could change by 2030, with smart gas and water meter adoption expected to grow at 10% and 16% CAGR, respectively.

While the tracker provides in-depth coverage of smart electricity, gas, and water meters across 52 countries and 5 regions—including installed base, shipments, revenue, market penetration, and connectivity technology—IoT Analytics plans to offer highlights for each smart meter submarket separately as its own article, starting here with smart electricity meters.

Global smart electricity meter market snapshot

As of late 2023, the smart electricity meter market achieved 43% penetration of the overall global electricity meter market, according to the market tracker.

Electricity grid modernization initiatives started in the late 2000s in Italy and the US and accelerated to national rollouts throughout the EU and APAC regions after 2010. Regulatory policies—supported by financial incentives from regional or national governments—have contributed to this growth, as these policies have encouraged utilities to replace mechanical electricity meters with smart meters to modernize their grid infrastructure.

However, as discussed below, not all parts of the world are modernizing their electricity infrastructure. According to the tracker, North America, Europe, and East Asia have had higher rates of smart electricity meter market penetration, but adoption rates still vary from country to country. Meanwhile, Latin America, Africa, and South Asia have been slow to initiate smart electricity meter projects across the board. Some countries have initiated large-scale smart electricity meter projects in recent years, though project implementation complexity, lack of regulatory policies, and cost hurdles have delayed rollouts in several countries.

Overall, the market for smart electricity meters looks promising, as the Smart Meter Market Tracker forecasts these IoT devices to achieve 54% adoption of the overall global electricity meter market by 2030.

Smart electricity meter market and adoption by region

While the smart meter market tracker shares market data down to the country level, the following are highlights about the smart electricity meter market at the regional level.

North America leads in smart electricity meter adoption

North America has the most mature smart electricity meter market, with nearly 77% electricity meter market penetration by the end of 2023.

In the US, smart electricity meters have 76% penetration in the overall electricity meter market as of 2023, driven by large-scale deployments from investor-owned utilities. Smart meter rollouts in the US are expected to slow down or plateau during the forecast period due to smart meter’s high penetration rate and long product life cycles. As municipalities with smaller budgets and cooperative-owned utilities replace their traditional electricity meters with smart meters, smart meter annual shipments in the US should see marginal growth through the rest of the decade.

Furthermore, the region will get a further boost in smart meter shipments, as Canadian utilities Fortis and Hydro One have announced plans in 2023 to replace their existing AMI with 2nd-generation smart meters.

The APAC region has the second most mature smart electricity meter market, driven by nationwide deployments in China and Japan.

Meanwhile, the APAC region has the largest addressable market for smart electricity meters, with over 1.1 billion electricity metering endpoints. In 2023, the APAC region accounted for almost 60% of the global smart meter installed base and more than 50% of annual smart meter shipments. In 2023, the region achieved a smart meter penetration rate of 49%, largely driven by successful nationwide rollouts in China and Japan. With planned nationwide deployments in Australia, South Korea, India, Indonesia, and Singapore, the region’s smart meter penetration is expected to reach 67% by the end of this decade.

Of note in this region, in 2021, India’s government set an ambitious goal of installing 250 million smart electricity meters by the end of 2025. To execute the implementation strategy, the government of India launched the Revamped Distribution Sector Scheme (RDSS) not only to help financially support regional USP smart meter deployment and maintenance but also to expand the domestic manufacturing capacity to produce smart meters within India. By the end of 2023, India had achieved less than 3% of this goal, making it unlikely for this goal to be met before 2030. That said, by 2030, India is on track to become the single largest market for smart electricity meters in terms of annual shipment and revenue.

Europe comes third in smart meter adoption, though adoption differs greatly by country

Europe had 47% smart electricity meter market penetration across the continent at the end of 2023. France, Spain, Italy, Netherlands, and the Scandinavian countries initiated nationwide rollouts in the last decade, while Greece, Hungary, Poland, and Romania only started their initiatives more recently.

Germany, with over 50 million electricity metering points, has largely lagged in its adoption rate, with under 10% of smart electricity meters deployed to date. However, in early 2023, the government of Germany revamped its 2016 Metering Point Operation Act to speed up smart meter deployments, targeting a complete rollout by 2032. The new law stipulates binding deadlines for USPs with a roadmap that includes 20% rollout by the end of 2025, 50% by the end of 2028, and 95% by the end of 2030 for residential and small business consumers, with targets extending to 2032 for large consumers. However, there is strong market skepticism around achieving these deadlines due to the need for clarity from the government around financial support for USPs, AMI technical specifications, data privacy, and security governance framework.

Saudi Arabia and the UAE lead in the Middle East and Africa region

In the Middle East and Africa region, Saudi Arabia and UAE are leading the way in the implementation of smart meters for electricity. In 2022, Saudi Arabia’s state-owned USP Saudi Electricity Company (SEC) announced the successful deployment of approximately 11 million smart meters over three years. Meanwhile, the UAE, which already has 1.6 million smart electricity meters installed, is expected to complete its nationwide rollout by the end of 2029.

Latin America lags in smart electricity meter adoption

Finally, Latin America has seen the slowest smart electricity meter deployment, largely due to regulatory indecisiveness delaying project rollouts. Uruguay was the first country in the region to mandate a nationwide smart meter rollout, aiming for completion in 2026.

Analyst’s outlook on the electricity smart meter market

Though regional variations persist—with energy USPs in North America, Europe, and East Asia boasting much more mature markets than their counterparts—the regions of Southern Asia, Latin America, and Africa represent a high-growth potential for smart meters. Some key considerations for various stakeholders are as follows:

• Market saturation and marginal growth in advanced economies: The implementation of more advanced and feature-rich 2nd-generation smart meters is already underway or in the advanced planning stages in countries such as Sweden, Italy, Finland, and Canada. This is likely to marginally drive up the average selling price of smart electricity meters.
• Cost sensitivity in emerging markets: In regions such as South Asia, Latin America, and Africa, where penetration rates are lower, some national governments are convinced of the need to upgrade their aging grid infrastructure and are actively engaging with smart grid industry stakeholders to develop regulatory policies and standards to drive the adoption of smart meters. However, these are also cost-sensitive markets where low-cost smart meters are more likely to be successful.
• Smart meter supply chain diversification: Several countries (e.g., Saudi Arabia, Mexico, Brazil, India, and Indonesia) that are initiating large-scale rollouts are stipulating that smart meter OEMs localize the manufacturing of 40% or more of the smart meter demand.
• Regulatory policy uncertainties: Policy indecisiveness creates complex and uncertain environments for smart meter stakeholders, hindering innovation and investments that subsequently delay smart meter deployments, as seen in countries such as Brazil, India, Mexico, and South Africa.
• Future innovations and market trends: Innovations in ICs, edge computing, and AI (TinyML), as seen in 2nd-generation smart meters, may help reduce strain on communication networks, improve real-time responses to grid fluctuations, build resilience, and enhance data security and privacy.

Based on the Global Smart Meter Market Tracker 2020–2030, the traditional USP industry, once considered a laggard in adopting new technology innovations, is leading the digital transformation market with more than a billion smart meters and accelerating its digital footprint.

IoT Analytics will closely monitor this evolving USP industry and technology landscape to provide in-depth analysis and actionable insights into this market. Its next report on energy utilities (expected in Q2 2024) will provide a deep dive assessment of USPs in 10 countries to identify key trends in smart grid programs, such as distribution automation, green energy integration, and EV charging infrastructure.

The post Smart electricity meter market 2024: Global adoption landscape appeared first on IoT Business News.

The combination of generative AI (GenAI) and the Internet of Things (IoT) holds the potential to reshape the future of technology and drive unprecedented innovation.

GenAI promises to revolutionize the IoT ecosystem by enhancing security, personalization, anomaly detection, on-device machine learning, and network management.

As these areas continue to evolve, businesses and individuals are ready to benefit from the innovative applications of GenAI. In this article, we’ll explore the many ways GenAI is beginning to transform the IoT landscape and examine the future possibilities of this powerful alliance.

Creating Synthetic Data for Machine Learning

One of the main challenges in developing machine learning models for IoT devices revolves around collecting and labeling massive amounts of data. However, GenAI solves this problem by creating synthetic data to train these models.

This synthetic data can be used to simulate a variety of scenarios, including machine failures. For example, a manufacturing company can use GenAI to create synthetic data that represents different machine failure scenarios. The company can then use this data to train machine learning models to anticipate potential problems in advance, enabling predictive maintenance.

Crafting Personalized Experiences

GenAI enables IoT devices to deliver personalized experiences to users by leveraging its ability to generate new and original content. Smart home systems can use GenAI algorithms to create personalized lighting and temperature settings for individual users, enhancing comfort and convenience.

And wearable devices can use GenAI to offer tailored workout recommendations based on an individual’s fitness goals and preferences. By leveraging user data from surveys, interactions, and sensor inputs, GenAI can create unique and personalized experiences tailored to the specific needs of each user.

Improving Anomaly Detection

Anomaly detection is key to ensuring the reliability and security of IoT networks. GenAI has the ability to greatly improve anomaly detection by creating synthetic data that accurately simulates normal operating conditions. By training machine learning models on this synthetic data, IoT devices can effectively identify and flag irregular events in real time.

For example, an operator of a power grid can use GenAI to generate synthetic data that mirrors typical power consumption patterns. The power grid operator can then use that data to train a machine learning model that can detect sudden spikes or irregularities in power consumption, enabling the operator to take proactive measures to prevent potential failures or security breaches.

Enabling On-Device Machine Learning

The combination of GenAI and IoT introduces exciting prospects for on-device machine learning. GenAI tackles the issue of limited computing resources in IoT devices by creating smaller and more effective machine learning models.

Anomaly detection models, for instance, can be optimized and implemented directly on IoT devices, enabling real-time analysis and decision-making without depending on cloud resources. This reduces latency as well as strengthens data privacy and security by reducing the need to send data to external servers.

Automating Network Management

Managing large-scale IoT networks requires intelligent automation. GenAI can be instrumental in automating different areas of network management, including configuring devices and optimizing network traffic. With its generative abilities, GenAI can automatically set up new devices as they join the network, simplifying the onboarding process.

Additionally, GenAI can enhance network traffic by intelligently directing data through the most efficient routes, reducing latency and maximizing the use of available bandwidth. This automation lessens the workload of network administrators and boosts the performance and efficiency of the network.

Potential Future IoT Applications

As GenAI continues to evolve, the possibilities for its integration with IoT are endless. Some potential applications include:

Creating New Types of IoT Devices: GenAI can facilitate the development of innovative IoT devices, such as smart assistants with natural language processing capabilities. These devices will be able to understand and respond to human commands and queries, revolutionizing the way we interact with technology.

Enhancing User Interactions: GenAI can enable new ways to interact with IoT devices, including gesture recognition and voice commands. This will make technology more intuitive and accessible, improving the user experience.

Improving Security and Reliability: GenAI can assist in developing advanced security measures for IoT networks, effectively mitigating cyber threats and ensuring data privacy. By generating synthetic data to train anomaly detection models, GenAI can help identify and prevent security breaches in real-time.

Democratizing IoT Access: GenAI has the potential to help bridge the digital divide by making IoT devices more affordable and accessible and allow more people to benefit from the advantages of intelligent connectivity. Democratizing IoT access will allow businesses and individuals to take advantage of IoT technologies for various applications and industries.

Conclusion

The integration of GenAI and IoT has the potential to totally transform how we interact with and benefit from intelligent connectivity. By leveraging GenAI, businesses and individuals can unlock a wide array of applications, ranging from improved anomaly detection and personalized experiences to on-device machine learning and network management automation.

As the field continues to evolve, it’s vital that we embrace the possibilities presented by GenAI and explore its potential to transform IoT. The future of GenAI in IoT is bright, promising a new era of intelligent connectivity and unprecedented opportunities for innovation.

The post Bringing the Power of GenAI to IoT appeared first on IoT Business News.

IoT Analytics published an analysis based on the “IoT Commercialization & Business Model Adoption Report 2024” report highlighting 8 insights from OEMs with business models that are considered more successful.

Key insights:

• Many equipment manufacturers (OEMs) have significantly advanced their IoT strategies, introducing innovative software and services, and revamping their business models. This evolution has enabled some to expand their IoT deployments to millions of devices successfully.
• Connected products are now the norm – It is expected that by 2026 more than 50% of products sold by OEMs will be IoT connected.
• IoT Analytics’ 206-page IoT Commercialization & Business Model Adoption Report 2024 delves into OEMs’ approaches to IoT business models. It highlights key factors that distinguish the more successful OEMs from less successful ones, such as acting on customer equipment usage behavior.

Key quotes:

• Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Our 2024 IoT Commercialization & Business Model Adoption Report, reveals pivotal insights into what differentiates successful IoT implementations among OEMs. A standout finding is the projection that over 50% of products sold by OEMs will be IoT-connected by 2026. The report also highlights the significance of leveraging customer equipment usage data as a cornerstone for innovation, enabling OEMs to offer tailored solutions that significantly enhance customer experiences and operational efficiencies. This report is a clarion call to OEMs everywhere: the path to IoT success is through deep customer insights and innovative business models. It takes years to get there but early innovators show that the journey is worth it.”

• Dimitris Paraskevopoulos, Senior Analyst at IoT Analytics, adds that “From the question of ‘Should I build connected IoT products?’ in 2014 to ‘How should I build my (next) smart connected products?’ in 2024, the shift in OEMs’ approach to IoT is evident. With over 16 billion active connected IoT devices globally, the transformation is not just about sales or specific partnerships. It’s about understanding customer behavior, analyzing it, and better serving their current and future needs.”

How to create a successful IoT business model

Less than 10 years ago, in November 2014, Michael Porter (one of the world’s most influential management thinkers and professor at Harvard Business School) and Jim Heppelman (former CEO at PTC) published a widely recognized article in Harvard Business Review titled “How Smart, Connected Products Are Transforming Competition.” In it, they argued that IoT-connected products would alter traditional industry structures, business models, and the nature of competition in many industries.

While the change has not been as quick as expected, 10 years later, we do have over 16 billion active connected IoT devices globally as of 2023, including consumer devices (e.g., smart homes and watches) and enterprise equipment (e.g., connected factory machinery, electrical equipment, and commercial vehicles). Most large OEMs have a connected product roadmap and, with it, a software and servitization strategy.

In 2014, when the article came out, many OEMs that IoT Analytics spoke with asked, “Should I also build connected IoT products?” This has since dramatically shifted; now, in 2024, the questions OEMs are asking are, “How should I build my (next) smart connected products?” and related to that, “What should the business model look like?”

Prominent examples of OEMs that have innovated their business model and subsequently scaled to hundreds of thousands or even millions of connected devices at this point include:

• BMW, with over 20 million connected vehicles on the road worldwide.
• John Deere, with over 500,000 connected agriculture- and construction-industry machines in the field.
• Schindler, with over 500,000 connected elevators around the world.

Even smaller OEMs are reaching impressive numbers with their connected devices. Take the example of Italy-based professional kitchen machinery manufacturer UNOX, a company with approximately 1,200 employees. Unox started its connected product proof-of-concept stage in 2015 and has since connected more than 30,000 ovens and introduced new revenue streams with it.

There are thousands of other examples of smart product/IoT business models that are scaling to these numbers of connected devices, and the resulting business implications should not be taken lightly—they are often core to the company strategy. Take, for example, US machinery giant Caterpillar, which has set a target of $28 billion in service sales by 2026. The data coming from smart connected IoT trucks, excavators, and wheel loaders play a crucial role in achieving that target.

“Our confidence is increasing that we will achieve our $28 billion services target by 2026. Through tools like the new Cat® Central and SIS2GO apps and insights from data on our more than 1.4 million connected assets, we are creating a superior customer experience as we help customers minimize downtime, improve utilization and extend product life.” – Jim Umpleby, Chairman and CEO at Caterpillar (2022)

In our research for the 206-page IoT Commercialization & Business Model Adoption Report 2024 (published February 2024), we looked at 100 OEMs like Caterpillar to understand: What are the takeaways and best practices these companies have developed as they are scaling their connected products to the thousands or millions?

The data are based on surveys with participants from these OEMs who have knowledge of and can speak to their respective OEM’s IoT business model.

Components of a successful IoT implementation

There are many tradeoffs when bringing a smart connected product to market, for example:

• Which features should we focus on developing?
• Do we monetizethe hardware, the software, a service, or the data? Or perhaps a combination of those?
• Do we charge once, monthly, or perhaps even per usage (pay-per-use)?
• Do we offer some features for free?
• Do we source the tech stack via an external vendor, develop it in-house, or find an open-source solution?

The report provides answers and viewpoints on each of these tradeoffs and highlights which IoT business models are considered to be more successful. This article does not go into the same depth as the report, but it highlights 8 insights that were uncovered during the analysis.

We split our analysis of IoT business models into 4 parts:

1. Making the case for connected equipment (e.g., determining the revenue contribution, outlining key benefits, and highlighting key beneficiaries of connected equipment)
2. Developing the IoT product (e.g., budgeting, sourcing parts, time to market, and developing the features)
3. Developing the business model (e.g., market positioning, key use cases/features, value chain, and revenue model)
4. Commercializing the IoT product (e.g., determining ways to monetize, developing measures to drive adoption)

1. Making the case for connected equipment

Insight 1: 40% of products sold by OEMs are connected.

The survey participants reported that, on average, connected products accounted for 40% of the product mix that was sold in 2023. The participants expect this average to rise to 54% by 2026, though OEMs in APAC are already seeing over 50% of products sold being connected.

Machinery OEMs and electrical equipment makers were the forerunners in this regard as of Q4 2023. However, respondents from OEMs in other major industries are expected to see their connected products take more of the share of the total products sold over the next three years and match those two industries. For example, according to the survey participants, ~34% of products sold by automotive OEMs in 2023 were connected. But by 2026, the participants from that industry expect that the share of connected products for their OEMs will reach 54%, the biggest expected increase among other major industries.

Insight 2: Gaining deep insights into customer usage is the single most valuable feature of connected products.

The research found signs that the core value of connected devices is to drive OEMs and customers closer together. 67% of the survey participants reported that generating deep insights into customer usage of their products and services is either extremely or very useful for their organization—the highest ranked in terms of benefits from connected products. Second to this was better management of customer needs, which 61% of OEMs reported as extremely or very useful.

However, it is not simply about sales or focusing on specific partnerships. Instead, companies find this information more valuable because they can understand customer behavior, analyze it, see how their product is broadly used, and better serve their current and future customers.

“What is happening on a single press might not be valid on a global scale. Machine data helps us to understand what is going on for certain press formats or applications. Since we have all the data from the market now, we do see regional shifts and shifts in applications. That helps us focus our company on what is most important for our customers.” – Thomas Göcke, head of digitalization, König & Bauer

2. Developing the IoT product

Insight 3: OEMs need 41 months to bring their connected products to market.

The research found that the survey participants’ OEMs average 41 months from project kick-off to their first sale (time to market), with 43% of them reporting time to market taking more than 45 months to reach their first sale. Participants in the automotive industry reported the slowest overall time-to-market, with an average of 53 months from project start to the first paying customer. Meanwhile, participants from electrical equipment OEMs reported the fastest, with an average of 33 months.

Insight 4: Microsoft, Cisco, and AWS are the three most mentioned vendors across the tech stack

According to the survey participants, OEMs appear to frequently outsource aspects of their tech stack. 150 unique vendors were mentioned by the 100 OEMs surveyed for this research. The top outsourced parts of the tech stack include connectivity services (e.g., cellular services), connectivity hardware (e.g., modems and gateways), and cloud-based applications.

The most mentioned vendors that the survey participants reported are Microsoft (mentioned in all 12 tech stack categories that we queried), AWS (mentioned in 11 out of the 12 categories), and Cisco (mentioned in 10 out of the 12 categories).

3. Developing the business model

Insight 5: Successful OEMs help their customers optimize workflows.

61% of survey participants from successful companies—those with an amortization time of 24 months or less for their connected product—shared that workflow optimization was crucial or of high value for their customers, while only 21% of less successful companies stated the same—a 40 percentage point gap. This gap, the largest when looking at how successful and less successful OEMs assess the value of the software or service to their customers, reflects that successful OEMs help their customers optimize their workflow.

A notable example of this from the report is German industrial machine manufacturing company Trumpf. Its Oseon software is a workflow optimization tool for sheet metal processors, with features including digital order management, traceability of materials and stock, and optimization of the overall order flow. Trumpf designed Oseon to help improve each step of the sheet metal production process across the workflow of the average sheet metal processor.

Insight 6: Upselling software based on customer usage is the most successful business model innovation.

When it comes to business innovations, OEMs have several options to explore and try, as shown in the preceding table. However, the best-performing innovation, according to the survey participants, is upselling software/services based on actual product usage, where OEMs observe customer product usage and offer relative add-ons. Of the 67 respondents who said their OEM tried this innovation, 60 (or 90%) of them shared that it was successful.

The most tried innovation is offering specific performance guarantees to the customers (e.g., specific uptime guarantees). This also had the second highest success rate at 59%; however, it also comes with more risk, as OEMs must be ready to stand by the promise and be prepared to address issues quickly.

4. Commercializing the IoT product

Insight 7: IT and data security concerns have not left the customers’ minds.

According to the survey participants, on average, the three biggest concerns/roadblocks that customers report when adopting new IoT-based digital services and software are:

• #1: IT/data security concerns
• #2: issues with integrating the product into legacy systems
• #3: lack of budget

Most notable in this statistic is that IT and data security concerns remained the top roadblock since 2020, when IoT Analytics last released a report on IoT business models for OEMs. These concerns are understandable since high-profile security breaches in connected products can remain fresh in the minds of many. For example, in 2021, hackers gained access to over 150,000 cameras produced by US-based building security solutions vendor Verkada, compromising customer data and giving video access to hospitals, jails, schools, and even Tesla cars.

Insight 8: Privacy and regulations are hindering the abilities of OEMs.

Along with customer concerns related to security, regulations aimed at protecting customer data and cyber security standards appear to be hampering European OEMs’ ability to make the most of their connected products. According to the survey respondents, on average, 71% of European OEMs felt that privacy and security laws were limiting their ability to make the most of their connected product solutions. Europe was the only region to increase in this regard from similar research in 2020; North America and Asia decreased by 22% and 18%, respectively, though 56% of North American OEMs expressed feeling similar limitations.

As this sentiment of hindrance by North American OEMs decreased, it is notable that 59% of North American OEMs stated that they own the data generated by their customers, surpassing both European and Asian OEMs by 17 percentage points. That said, 72% of North American OEMs reported that the customer has a say in whether the generated data is shared with the OEM.

Analyst takeaways and outlook

Since Michael Porter and Jim Heppelman’s paper in 2014, most OEMs have developed a business model strategy, finding what works best for them and their products. However, now that OEMs are looking to scale, adapting their existing business models to this growth presents new challenges.

The research in the IoT Commercialization & Business Model Adoption Report 2024 shows many successful commercialization models are scaling, but it shows a fair share of non-successful ones as well. A key question from this is, “Why are some connected IoT product OEMs more successful than others?”

Overall, it appears to come down to OEMs putting the focus squarely on the customer—but behind the scenes, this is more complex than it sounds. While many OEMs claim that they are getting better at putting themselves in their customers’ shoes, there is clearly still room for improvement. The IoT Analytics’ team, for example, struggled to find a good set of OEM webpages that have a clear and well-articulated IoT value proposition that is geared toward real-world customer problems.

Another struggle for OEMs is making the revenue from connected products meaningful. With some equipment costing hundreds of thousands of dollars and the related software available for only a fraction of that cost, many OEMs still struggle to make the business of connected products meaningful enough to the company’s top and bottom line.

One approach to addressing this is the equipment-as-a-service model. Of the various innovative business models in the IoT Commercialization & Business Model Adoption Report 2024, this model is the most innovative and, in turn, the most complex. It is designed around charging for either some or all of the equipment based on usage.

Of the various innovative business models in the IoT Commercialization & Business Model Adoption Report 2024, EaaS is the most innovative and, in turn, the most complex. It is designed around charging for either some or all of the equipment based on usage.

What it means for OEMs

7 key questions that OEM executives should ask themselves based on the insights in this article:

1. Using IoT for customer-centricity: How well do we understand our customers’ usage of our products, and are we leveraging this data to enhance their experience and address their specific needs?

2. Offering workflow optimization: In what ways can our IoT products help customers optimize their workflow, and are we communicating this value effectively in our sales and marketing efforts?

3. Business model innovation: How can we innovate our business model, perhaps through upselling based on customer usage or offering performance guarantees, to enhance profitability and customer satisfaction?

4. Pricing and monetization strategy: What is the most effective pricing strategy for our IoT products? Should we consider a pay-per-use model, subscription-based services, or a combination of different pricing models?

5. Security and privacy concerns: How are we addressing IT and data security concerns in our IoT products, and are we compliant with the latest privacy and cybersecurity regulations, especially in different geographical markets? Can we prove this to our customers and communicate it effectively?

6. Equipment-as-a-Service (EaaS) model: Could the EaaS model be applicable to our products, and how can we structure it to provide clear value propositions and strong customer service?

7. Scaling challenges: As we scale, what are the key challenges we need to prepare for, particularly in terms of adapting our business model and maintaining a customer-focused approach?

The post How to create a successful IoT business model – Insights from successful OEMs appeared first on IoT Business News.

As we kick off 2024, the IoT Analytics team has again evaluated last year’s main IoT developments in the global “Internet of Things” arena.

This article highlights some general observations and our top 10 IoT stories from 2023, a year characterized by multi-decade high interest rates, a challenging macroeconomic environment, and, of course, the advent and excitement of generative AI (gen AI).

General IoT 2023 market

2023 was a year of surprises—both positive and negative. The U.S. and several other Western countries proved highly resilient in the face of higher interest rates and elevated inflation, and they avoided a much-anticipated recession. The 2023 global GDP growth of 3.0% ended up more solid than many had expected at the beginning of the year but still trailed the historic average by 0.8 percentage points.

The Nasdaq Composite, one of the key indices for technology companies, rose 43% in 2023 after dropping 33% in 2022. Not only did investors celebrate the potential peak in interest rates, but they also saw new opportunities with the hype around gen AI. Chipmaker Nvidia (ticker symbol NVDA) gained 246% in 2023, Amazon (AMZN) gained 77%, Microsoft (MSFT) gained 58%, and Alphabet (GOOG) gained 57%—all outshining the Nasdaq.

Against this backdrop, IoT 2023 markets held up steadily, with the number of connected IoT devices growing to approximately 16.7 billion (exact update coming in a few weeks) with roughly $235 billion in IoT enterprise spending (IoT Analytics will publish the 2023 IoT spending later in Q1).

The public relevance of the term “IoT,” which peaked in Q1 2022, continued to see strong interest, holding steady at around 10–20% below the Q1 2022 peak despite the renewed focus on AI (see Google trend graph in the lead image of this article). We did notice, however, that the use of the term “IoT” in corporate earnings calls declined 16% from Q4 2022 to Q4 2023.

With many organizations now managing millions of IoT devices (case in point: in Q4 2023, consumer giant Nestlé announced 2.8 million connected devices through the AWS IoT platform), do not assume that IoT is fading in importance. Quite the opposite: IoT is scaling for many organizations. Our take: IoT is not the “cool” term it used to be. In 2023, companies loved talking about the AI opportunity instead, but at the same time, IoT is quietly scaling.

Top 10 notable IoT 2023 developments

Throughout 2023, we monitored significant developments regarding IoT technology as part of our continued coverage of the field. In our opinion, these are the top 10 notable developments of IoT in 2023 (in chronological order of the leading stories we highlight).

1. Most notable IoT-related regulation: The EU’s NIS2 cybersecurity directive

In January 2023, the EU’s second Network and Information Security Directive (NIS2) became active. This cybersecurity directive comes as a follow-up to the first NIS directive (introduced in 2016) to address shortcomings from the first version, namely inconsistent implementation across member states in terms of what organizations were considered essential.

The new version enforces requirements for cyber risk management and incident reporting across 15 sectors. The intent of NIS2 is to clearly define the organizations meant to comply and to force them to deeply consider their cybersecurity posture, ideally protecting citizens and essential services from cyber threats.

Each EU member state has until October 2024 to adopt laws in compliance with NIS2 by 17 October 2024, giving companies time to look ahead and start compliance without much pressure now. However, as that deadline approaches, companies will begin to feel the compliance pressure, as failure to comply with its measures can cost companies up to €10 million or 2% of their annual global revenue (whichever is higher), as well as possible sanctions and audits.

The specific covered sector in the NIS2 that impacts IoT is digital infrastructure, which covers telecoms, DNS/TLD services, data centers, trust services, and cloud services. The EU projects the annual revenue of this sector to be €85.4 billion and notes that dependence on digital infrastructure opens companies to various cybersecurity risks.

In addition to NIS2, the EU is also expected to start enforcing its Cyber Resilience Act in early 2024. This legislation targets hardware and software products sold within the EU market. Once enforced, manufacturers will have 36 months to start applying the act’s guidelines.

Other notable IoT-related regulations in 2023 included:

2. Most surprising market destabilization: Tech layoffs

In January 2023, Microsoft announced it planned to lay off 10,000 employees between January and March 2023. Most notably for us, the third wave in March saw the largest cut for IoT-, AI-, and supply chain-focused personnel across various levels, functions, teams, and regions.

Microsoft did not have the most tech-industry layoffs—that unfortunate honor appears to belong to Amazon—but it was the most direct hit at IoT and related fields, especially from a company whose IoT services appear to be expanding, at least in the cloud (more on this below).

Google also experienced more layoffs than Microsoft. Though most of its layoffs appeared to be across the board, at times focused on their human resources and recruitment sections, Google shuttered its IoT Core service in August 2023 (also more on this below), meaning roles associated with that service either got moved elsewhere or scrapped altogether.

The fact that three companies seemingly at the forefront of the biggest tech trend in 2023, AI, laid off parts of their team shook the markets and created a lot of uncertainty. The IoT Analytics team noted that some of the laid-off people included high-performers who enjoyed industry-wide recognition, adding to the overall uncertainty of what was happening.

While the thousands of big tech layoffs represented only a small percentage of the respective company employee baseline, it was the startup scene that was most affected by the layoffs. For example, in December 2023, Israel-based grid-computing software startup Incredibuild, known for its accelerated software development technology, including for embedded IoT systems, laid off 40 employees, or 20% of its workforce (75% of which were based in the company’s HQ).

3. Most innovative IoT 2023 connectivity technology development: 5G in space

In July 2023, Spain-based low-Earth orbit (LEO) constellation satellite operator Sateliot and Spanish multinational telecommunications company Telefónica announced the success of their end-to-end test of a roaming 5G cellular network in space. The test process involved an IoT cellular device with a regular SIM card provisioned on Telefónica Tech’s Kite platform—all of this following 3GPP Release 17 non-terrestrial network (NTN) standards and leveraging narrowband IoT (broadly referred to as NB-IoT) communication technology. The device was able to switch between Telefónica’s terrestrial network and Sateliot’s non-terrestrial, low-earth-orbit (LEO) satellite network, demonstrating the integration of both network types using GSMA roaming.

The test, which the European Space Agency supervised, also involved Sateliot’s “Store & Forward” mode, a two-step authentication method developed by Sateliot to store information on a satellite when it is not in a position to connect with a ground station, forwarding the information when it enters coverage range.

IoT solution providers working with sectors that can often experience intermittent connectivity, such as transportation, logistics, or rural agriculture, will see applications to keep their customers connected when between terrestrial 5G network node ranges. Interestingly, in February 2023, Sateliot partnered with space and IoT hardware company GOSPACE LABS to provide 5G NTN NB-IoT connectivity to GOSPACE LABS’ MERATCH water management solution in the US, including water wells in rural areas, and in April 2023, Sateliot applied to the US Federal Communications Commission to bring its space-based 5G NB-IoT technology to the US market.

4. Most accelerated driver of IoT 2023 initiatives: Sustainability and ESG

Amit Kohli, Sr. Solution Director and Sustainability Leader, Orange Business:

“Gone are the [days] of greenwashing. Things are getting more serious in terms of reporting… [It’s a less] casual outlook [than] in the past.”

Europe, and international companies doing business in the EU, witnessed a wave of sustainability directives enter into effect. This is not just one news story but a series of stories that have been on the radar of many for several years.

In January 2023, the Corporate Sustainability Reporting Directive (CSRD) entered into force, enacted as a legal framework that requires all EU companies, except micro-enterprises, to submit annual sustainability reports starting in 2024. Then, on July 31, 2023, the European Commission adopted the first set of European Sustainability Reporting Standards (ESRS), which act as the roadmap for CSRD compliance and require large companies and listed companies to publish regular reports on the social and environmental risks they face. The ESRS became law on 1 January 2024 and now applies directly in all 27 EU member states. Large corporations now must report various IoT-type data, including pollution levels, GHG emissions, and resource use (e.g., water and energy consumption).

Additionally, in November 2023, the EU’s Renewable Energy Directive (Revised Directive EU/2023/2413) became enforceable across all member states. The member states have 18 months to transpose the directive’s provisions into their own national laws, with some provisions having a deadline of July 2024.

With strict adherence guidelines like these, it was no surprise to find increasing emphasis and prioritization on sustainability and energy management at the Smart Production Solutions (SPS) Fair 2023 in Nuremberg, Germany, in November. Coinciding with this, we have noted sustainability and environmental concerns as key topics during CEO earnings calls throughout 2023.

Other notable sustainability ESG regulations in 2023 helping drive IoT initiatives included:

5. Largest IoT-related acquisition: Renesas acquires Sequans

In August 2023, Japan-based semiconductor manufacturer Renesas Electronics agreed to buy Sequans Communications, a France-based cellular IoT chipmaker, for $249 million. The deal, expected to close in early 2024, is poised to expand Renesas’ foray into the IoT sector. The electronics company plans to integrate Sequan’s cellular IoT products into its microcontrollers and other products, enhancing its WAN market reach.

A few months prior, in June 2023, Renesas completed its all-cash acquisition of Panthronics AG, an Austrian-based fabless semiconductor company specializing in wireless products. The deal was originally made in March 2023 for approximately $95 million, and in its announcement of the completed acquisition, Renesas released 13 designs leveraging Panthronics’ NFC technology, showcasing the “embedded processing, power, connectivity, and analog portfolios” of both companies and what customers may be able to look forward to in the near future.

These are just the latest IoT-focused acquisitions by Renesas, but they are by no means the largest by the company financially. The following is a breakdown of its other IoT-oriented acquisitions in recent years:

• 2017: Renesas began its IoT expansion journey by acquiring Intersil, a provider of power management and analog solutions, for approximately $3.2 billion, targeting larger IoT, automotive, and industrial market opportunities.
• 2019: Renesas acquired US-based mixed-signal semiconductor manufacturer Integrated Device Technology, Inc. (IDT) for approximately $6.7 billion.
• 2021: Renesas acquired UK-based Dialog Semiconductor in a nearly $6-billion deal. Dialog had been one of Apple’s major chip suppliers, and this deal sought to expand Renesas’ reach into the IoT, power management, and connectivity solutions market.
• 2022: Renesas acquired Stradian, an India-based manufacturer of 4D imaging radars, for approximately $44 million, aiming to boost its automotive and industrial sensing solution offerings.

Other notable IoT-related acquisition announcements of 2023 included:

6. Most notable software developments: IoT cloud wars

The leading cloud providers, Google, AWS, and Microsoft, all recorded a strong slowdown in cloud revenue growth in 2023 as many organizations started to optimize their cloud spending. AWS, for example, grew by 40% in late 2021 but slowed to 12% growth in late 2023.

On the back of slowing growth, in August 2023, Google made its shock announcement from 2022 a reality and shut down its IoT Core service. The company seemingly now redirects its customers to partners such as Litmus Automation, KORE Wireless, or SoftServe to get the job done (Google’s IoT Core site lists these and other partners on its website to “meet the needs of IoT customers”)

How would Microsoft and AWS react in 2023?

AWS and Microsoft Azure did not follow suit but instead expanded their IoT cloud services in 2023:

Microsoft most notably announced Azure IoT Operations in November 2023, an expansion of its Azure IoT portfolio enabled by Azure Arc. It aims to enable “a cloud to edge data plane with local data processing and analytics to transfer clean, useful data to hyperscale cloud services such as Microsoft Fabric for unified data governance and analytics.”

AWS also announced several IoT extensions to its cloud platform services portfolio in 2023, including AWS IoT FleetWise vision system data and AWS IoT SiteWise Edge on Siemens Industrial Edge B2B marketplace (both in November 2023) as well as a new open-source, no-code IoT dashboard application, aimed at allowing users to visualize and interact with data from its AWS IoT SiteWise service.

7. Largest IoT-related funding round: Pragmatic

UK-based circuits manufacturer Pragmatic Semiconductor raised $389.3 million in 2023. Its latest funding round, Series D, closed on 6 December 2023 and raised the largest venture funding round for a European chipmaker at $206 million.

Pragmatic Semiconductor manufactures flexible, ultra-thin integrated circuits—thinner than a human hair—by leveraging thin-film semiconductors and polymers rather than silicon. The company aims to use the technology to bring intelligence to low-cost items as part of IoT applications, including smart packaging, recycling and reuse, traceability, and product authentication.

Very notable in this latest funding round was that the UK Infrastructure Bank led in investments (alongside M&G Catalyst). While the US and EU worked to establish an early warning system for semiconductor supply chain disruptions and increase investment and trade between the two on this technological front, the UK pursued its own national semiconductor strategy. The strategy aims to support UK leadership in the research, design, and advancement of chip manufacturing, and the UK Infrastructure Bank made the direct equity investment to support this effort.

Other notable IoT-related funding rounds of 2023 included:

8. Best performing IoT 2023 stock: Samsara

After making our IoT 2022 in review list of underperforming IoT company stocks, US-based IoT solutions company Samsara, Inc. (ticker symbol “IOT”), best known for its fleet management and telematics solutions, witnessed 180% growth in its stock in 2023, rising from $11.92 on 3 January to $33.38 on 29 December. Bolstering its climb were three better-than-expected quarterly earnings reports in March, June, and November.

Founded in 2015 by Sanjit Biswas and John Bicket, Samsara specializes in telematics, or “the convergence of telecommunications and information processing,” as it defines it. However, it has expanded its portfolio in the past few years to offer a more holistic connected operations platform and target other industries, such as utilities, manufacturing, and retail.

In 2021, we listed Samsara’s IOT stock as the biggest IoT-related IPO of that year.

9. Most notable IoT 2023 project: 250 million smart meters in India

250 million (or 1.5%) of the current 16.6 billion global IoT devices could soon come from one initiative alone: the ambitious national smart meter roll-out in India. However, though approved in 2021, the project has generally sputtered along, largely due to low domestic production means for the meters while trying to cover a whole subcontinent.

This year, to help spur the project and control the costs, the Indian government opened the projects to a total expenditure (TOTEX) approach, whereby the government can issue $40 billion in grants on completion of the projects and pay per meter. The project’s goal and financial approach have also opened the project to international support. For example, in June 2023, the US International Development Finance Corporation (DFC) announced a formalized $49.5 million investment to India-based smart meter manufacturer Genus Power Infrastructures, aimed at helping the company expand its production of smart meters.

Nonetheless, capacity remained low by the end of 2023, and only 8 million smart meter installations have taken place. As a result, it appears unlikely that India will meet this goal by the end of 2025, but with the spurred investment, the goal could be met not too long after. According to the government, 99 million of the 250 million smart meter contracts had already been rewarded at the end of 2023.

10. Generative AI and IoT breakthrough: None yet

How can recent advances in gen AI, which is mostly text- or image-based, be combined with IoT data, which is mostly based on time-series sensor data? This was one of the top questions we received in 2023.

The answer is … it is complicated.

However, throughout 2023, we have seen several developments on this front, but none have reached scale just yet. Nonetheless, the following are a few initial steps in gen AI and IoT convergence that caught our attention, some of which are discussed in our Generative AI Market Report 2023–2023 (released December 2023).

a) Using national language (gen AI) to query operational data (IoT)

In June 2023, Norway-based industrial software company Cognite launched Cognite AI, a generative AI solution designed for industrial operational data. Built within Gognite Data Fusion, it is meant to enable more tailored, business-specific data retrieval and contextualization in a private, secure manner. Siemens presented a similar example in November 2023, together with Schaeffler, at the SPS fair—which we covered in our SPS 2023 report.

b) Providing guided repair or operations (gen AI) based on operational data (IoT)

In September 2023, Google’s Cloud team shared a video demonstrating their gen AI solution alerting train maintenance operators to potential train issues and proactively providing possible causes and solutions based on manuals and past issue/repair reports.

In November 2023, Microsoft announced Copilot in Microsoft Dynamics 365 Guides, a solution that combines gen AI and mixed reality to assist frontline workers in their tasks. Paired with HoloLens 2 and IoT sensors, Microsoft claims that operators can pinpoint and identify specific assets and access relevant information about them—such as operational conditions or troubleshooting guides—in real time.

c) Using generated images (gen AI) to train vision systems (IoT)

In December 2023, Germany-based engineering and technology company Bosch announced it is piloting gen AI models in manufacturing, whereby they use synthetic, gen AI-created images to develop and scale AI solutions for optical inspection and optimizing existing AI models.

d) Using natural language (gen AI) to teach and control robots with vision systems (IoT)

In February 2023, Microsoft’s Autonomous Systems and Robotics Group released a paper entitled “ChatGPT for Robotics: Design Principles and Model Abilities” (Microsoft is a major backer of ChatGPT’s parent company, OpenAI). In this paper, the research team leveraged ChatGPT’s intuitive language capabilities to control multiple robotic platforms, including arms, drones, and home assist robots. Soon after, in April 2023, Microsoft published another paper, “ChatGPT empowered long-step robot control in various environments: A case application” (last updated in August 2023), in which they demonstrate a specific example of how ChatGPT could be used to convert natural language instructions into robotic actions.

In July 2023, Germany-based AI software and robotics company Sereact announced the release of PickGPT, a gen AI transformer that combines LLMs with computer vision. By combining these capabilities, users can use simple language to instruct a robot to sense conditions or identify objects, offering many potential use cases for remote sensing and control.

Others

We also noted a startup that, in May 2023, announced a revolutionary gen AI solution that would use gen AI to produce synthetical IoT sensor data that could then be used to train AI algorithms. The company has since deleted all references to the announcement.

The post IoT 2023 in review: The 10 most relevant IoT developments of the year appeared first on IoT Business News.

2023 was an eventful year in the tech sector, where AI went mainstream with the explosion of language learning models.

As we progress into 2024, the integration and evolution of artificial intelligence in various domains are not just changing; they are set to revolutionise the way we approach design, development, and deployment in these sectors.

With advancements in artificial intelligence accelerating at an unprecedented pace, we stand at the cusp of a new era where AI’s influence extends beyond mere automation to become a cornerstone of innovation and efficiency.

The engineering team at ByteSnap Design has been reflecting on the future of AI in the technology and electronics design industries.

Here is the team’s forecast for the pivotal AI trends likely to emerge during 2024 to redefine industry standards and drive forward a new age of technological excellence.

AI to trigger a battle of the Smart Assistants?

AI assistant tools will continue to be integrated into existing tools to make tasks easier. An example of this happening this year is Zoom’s AI companion which can summarise meetings into notes.

Expect to see development tools such as an IDE integration which can generate GIT commit notes, and release notes automatically during 2024.

We also predict companies will be trying to increase their profits with more monetisation from smart assistants such as Alexa and this will drive techy people towards open source alternatives, such as Home Assistant which run locally.

Rise of the Robotaxi

We anticipate that the first un-geofenced electric Robotaxis will become operational and start accepting paying customers over the coming months.

This is likely to scale steadily over the next few years to replace Uber as the transport medium of choice, providing legal issues can be overcome.

Ultimately, this will make car ownership a questionable decision because travelling this way could be cheaper than running a car.

Apple to enter the Generative AI race

We look forward to Apple unveiling a product to join the AI arms race with their own large language model. Other companies are embracing AI faster and already implementing it; for example, Bard into Google Assistant, and Microsoft’s push for AI in their Office 365 products. Nevertheless, Apple have a stronger than most in-house development philosophy, and it’s hard to see them allowing these products to go unchallenged.

Expect announcements in late 2024 from Apple around its generative AI offering.

Further AI disruption for the Smart Home market

Expect to see more innovation in the smart home market as consumers continue to look for ways to reduce their energy bills, with smart thermostats and TRVs becoming ever more popular. Nest are apparently trying to use AI to help understand consumers behaviour around energy consumption and we anticipate that this trend will continue.

With Apple releasing their Pro Vision headset in 2024, we also expect to see some manufacturers trying to compete with a cheaper product. Apple are excellent at design and are sometimes seen as a trend setter, but in this case are quite late to the party With Meta already well-established leaders. However, Apple have a history of knocking out incumbent leaders so this could be an interesting space to watch.

How much of a consumer appetite there is for this type of technology, however, remains open to question.

AI-enabled Integrated Circuits

We’re likely to see the greater emergence of AI on integrated circuits from companies such as Altered Carbon.

Computer chip manufacturers, Intel, for example, are incorporating AI cores into their CPUs.

AI algorithms in our view are mostly used for detection/categorisation. The classic example is using AI to detect whether an image contains a cat or a dog. However, even the way that the likes of Tesla use AI is similar – detecting images of signs for speed limits, or an image of the lines of the road – but the output is different in that it translates it into braking, accelerating or turning.

One of the projects we’ve worked on at ByteSnap sent accelerometer data into the cloud to detect people falling over. We see a scenario where a fall detector algorithm could be generated by AI and embedded within the sensor device, so that the huge amount of data does not need to be sent, allowing the product to consume less power.

Greater AI in Supply Chain Management

AI-powered forecasting is providing businesses with intelligence to prevent mishaps in the future, overcoming demand-supply mismatches to prevent overstock or understock of inventory.
This minimises costs and improves customer experience. We expect to see more of this across 2024. Additionally, AI-based algorithms are automating goods retrieval from warehouses for smooth order fulfilment, and AI-powered autonomous vehicles are reducing driver costs for delivery.

AI in software and electronics design

Software development and electronics design are both areas that AI vendors are targeting, as developers are expensive and timescales can be long. We can see initially the AI could be best at optimising PCB layouts in the hardware side and writing generic functions within software, albeit with dubious copyright infringement.

The work of translating very abstract requirements into real electronics still seems a very long way off though. This is partly due to lack of freely available models to train against in what is a fast-moving industry and little way for the circuits that are available to be assessed.

In addition, electronic engineering is actually quite a person-centred job; dealing with suppliers, customers, manufacturers, colleagues. Software AI trainers have raided github and ChatGPT was able to train linguistic models against the huge wealth of the World Wide Web.

However, for electronics, it will take another generation of AI development before engineering jobs are threatened.

The post ByteSnap Electronic Industry Predictions for 2024 appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Generative AI Market Report 2023–2030” report and highlights the landscape with its top players in the data center GPU, foundational model and platform, and generative AI services markets.

Key insights:

• The generative AI market went from nearly nothing to a hot market within a year, as shown by IoT Analytics’ latest research report.
• IoT Analytics analyzed 3 interconnected markets for generative AI: 1) data center GPUs, 2) foundational models and platforms, and 3) generative AI services. Each has distinct aspects and market players.
• NVIDIA leads the data center GPU segment with a 92% market share, while OpenAI and Microsoft have a combined share of 69% in the foundational models and platforms market. The services market is more fragmented, with Accenture currently seen as the leader with a 6% market share.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “The speed of generative AI innovation with new offerings coming on the market on a weekly basis is fascinating monitor. Nvidia with 92% market share for data center GPUs as well as Microsoft and OpenAI with a combined 69% market share in the models and platforms segment are firmly in the lead in their respective market segments. With hyperscalers developing their own data center chips, with the availability of powerful open-source models and with giants like AWS and Google looking to differentiate with their new offerings, it will be interesting to watch how much the early lead is worth for the current market leaders. I personally do expect both Microsoft and Nvidia to maintain their strong positions in the coming years but the gap to the competition will likely close a bit.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“The Generative AI market is rapidly evolving, with established leaders and a growing number of startups. In 2024, it’s a make-or-break year for Gen AI vendors, as they navigate a crowded field of competitors.”

The leading generative AI companies

The rise of generative AI

Following its release of ChatGPT in 2022, OpenAI experienced an impressive one-year, zero-to-$1 billion revenue bump—surpassed only by US-based chipmaker NVIDIA, which managed to increase its data center GPU sales from $3.6 billion in Q4 2022 to an expected $16 billion in Q4 2023. When it comes to generative AI companies, these two stand out.

The generative AI foundational models and platforms market is expected to reach nearly 5% of global software spending by 2030

According to IoT Analytics’ Generative AI Market Report 2023–2030 (published December 2023), the generative AI software and services market reached $6.2 billion in 2023. Although it is still very early to forecast where things are going from here, the IoT Analytics research team expects the generative AI foundational models and platforms market to make up nearly 5% of global software spending by 2030 due to its disruptive nature and tremendous value potential.

However, this does not include the market for individual generative AI solutions. The team believes generative AI will become standard within most software in the near future. This also does not include the hardware market, such as for data center GPUs, since this market is looked at separately from software but is discussed below.

In this article, we dive into the data center GPU, generative AI foundational model and platform, and generative AI services markets, discussing what aspects of the generative AI field make up each market and highlighting the leading generative AI companies within them.

Market segment 1: Data center GPU market

a.) Market overview

The data center GPUs market refers to specialized GPUs designed to handle the extensive computation demands of modern data centers, which are the backbone of generative AI. Originally designed for rendering graphics, GPUs excel at parallel processing, which is fundamental for deep learning computations used in generative AI.

The report shows the data center GPUs market reached $49 billion in 2023—a booming increase from 2022 (+182%), mostly driven by one company alone: NVIDIA. Although the market for data center GPUs has seen steep price increases and is undergoing severe supply constraints, there is currently no reason to believe demand will decline in the next two years.

b.) Leading data center GPU companies

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

1. NVIDIA

NVIDIA leads the data center GPU market by a long shot, owning 92% of the market share. In 2023, the company’s quarterly revenue jumped 272%, from $4.3 billion in Q1 to a forecasted $16 billion in Q4.

The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon.

2. AMD

The Data Center segment of US-based semiconductor AMD player, NVIDIA’s first real GPU challenger, grew by 21% from Q2 2023 to Q3 2023 and shared 3% of the market. However, AMD has big ambitions in 2024 to eat into NVIDIA’s market share. In early December 2023, it announced the release of its Instinct MI300 Series accelerators, which are cheaper than NVIDIA’s comparable accelerators and, as AMD claims, faster. AMD’s CEO, Dr. Lisa Su, forecasted at least $1 billion in revenue in 2024 through this chip alone, and Microsoft, Meta, and OpenAI stated they would use the Instinct MI300X in their data centers. AMD also recently launched ROCm 6.0 to provide developers with an ecosystem that is equally attractive to CUDA.

3. Intel and others

US-based chipmaker Intel, the traditional competitor to NVIDIA and AMD, has lagged behind on the data center GPU front. In May 2022, Intel’s Habana Labs released its second generation of AI processors, Gaudi 2, for training and inferencing. Though not as fast as NVIDIA’s popular H100 GPU, it is considered a viable alternative when considering price to performance.

Meanwhile, in July 2023, startup chipmaker Cerebras announced it had built its first of nine AI supercomputers in an effort to provide alternatives to systems using NVIDIA technology. Cerebras built the system, Condor Galaxy 1, in partnership with the UAE, which has invested in AI research in recent years.

Market segment 2: Generative AI foundational models and platforms market

a) Market overview

The foundational models and platforms market comprises two related areas. Foundational models are large-scale, pre-trained models that can be adapted to various tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.

Generative AI platforms, in turn, refer to software that enables the management of generative AI-related activities outside of foundational models. Notably, IoT Analytics identified six platform types: 1) development, 2) data management/databases, 3) AI IaaS/GPU as-a-service, 4) middleware & integration, 5) MLOps, and 6) user interface and experience (UI/UX).

The foundational models and platforms market exploded with the public release of ChatGPT in late 2022, reaching $3.0 billion in 2023. This is substantial growth over 2022, which saw next to nil in terms of revenue. IoT Analytics’ analysis projects strong market growth in the coming years as enterprises invest billions in—and report real value from—generative AI implementations and continuous improvements.

b.) Leading generative AI foundational model and platform companies

Unsurprisingly, the foundational model and platform market are currently led by OpenAI, with several well-known technology companies trying to catch up.

1. OpenAI

With the November 2022 launch and subsequent success of ChatGPT, OpenAI leads in the share of the foundational model and platform vendors market with 39%. Since the release of ChatGPT, OpenAI’s generative pre-trained transformer (GPT) models went from GPT-3.5 to GPT-4 to GPT-4 Turbo, showcasing the continued development of the model. OpenAI’s models continue to impress in independent model assessments and rankings—often coming out in the top three of all tested models. Although many experts expect the foundational model space to become a commodity over time, at this point, OpenAI’s flagship models remain the top foundational model on the most common benchmarks.

According to IoT Analytics’ What CEOs Talked About series, in 2023, ChatGPT skyrocketed in boardroom discussions in Q1, but as other foundational models and generative AI applications became available, mentions of ChatGPT steadily declined as “generative AI” separated and continued to rise. (The What CEOs Talked About in Q4 2023 report and blog is expected to be released mid-December 2023.)

2. Microsoft

On OpenAI’s heels at 30% market share is Microsoft, its largest shareholder. Microsoft’s platform, Azure AI, offers Azure OpenAI, which uses OpenAI’s LLMs but goes beyond the public ChatGPT offering by promising greater data security and custom AI apps. This is suited for enterprises who want to secure their proprietary data when leveraging the benefits of generative AI since ChatGPT’s terms of use state that they can store and use content (both input and output) to improve their services. In November 2023, Microsoft reported over 20,000 active paying customers for its Azure AI platform, adding that 85% of Fortune 100 companies used it in the past year.

Despite Microsoft’s strong partnership with OpenAI, Microsoft also heavily promotes the usage of other models, such as Llama 2, via its platform, thereby enabling customers to freely choose and test different models and providers. Another key priority for Microsoft is integrating AI capabilities into its existing product portfolio, such as Azure, Microsoft/Office 365, and Bing.

3. AWS

AWS has an 8% share of this market. Its Bedrock service, publicly released in September 2023, provides access to models from several AI companies, such as Anthropic, AI21 labs, and Cohere (each with a 2% share of this market), and combines them with developer toolsets to help customers build and scale generative AI applications.

AWS has quickly claimed the third spot in this market because the company is the market leader in public cloud services and quickly got its existing customer base excited about its differentiated approach to Generative AI. In contrast to Google and Microsoft, AWS Bedrock focuses on providing a platform service that gives users access to a number of both general and domain-specific foundational models from a variety of vendors—providing choice, flexibility, and independence.

4. Google

In 2022, most experts credited Google as being the one tech company at the forefront of AI. Many experts interviewed by the IoT Analytics team continuously praised Google for its AI and its data products and innovations. In 2023, the picture is different, and Google is fighting to defend its position as an AI leader.

Vertex AI is Google Cloud’splatform focused on machine learning (ML) ops. It is integrated with other Google Cloud services, such as BigQuery and Dataproc, and offers a Jupyter-based environment for ML tasks. In early December 2023, Google released a preview version of its new multi-modal flagship model, Gemini. The related technical report states that the largest of the Gemini family outperformed other existing models in 30 out of 32 common ML benchmarks. Initially, the announcement of Gemini was widely received as positive, but a popular demo video released by Google later turned out to be staged.

Market segment 3: Generative AI services market

a) Market overview

The generative AI services market represents a specialized segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate generative AI capabilities. With generative AI having risen as one of the top discussion points in boardrooms, services companies are sensing a large opportunity in helping companies formulate their generative AI strategies (e.g., what use cases to implement), advising them on technical architecture choices (e.g., which models to use) and helping them implement and build individual solutions.

IoT Analytics assesses that the generative AI services sector’s opportunity is now. Due to the novelty of generative AI, organizations often lack skills and experience, and the only option is to look for professional services firms that have or are in the process of building up the required expertise.

b) Leading generative AI services companies

The generative AI services market is more dispersed than the other two markets highlighted here.

1. Accenture

Accenture is estimated to have the largest generative AI services market share at 6%. The company announced in June 2023 that it is investing $3 billion in data and AI practice over three years to double its AI talent and develop new capabilities. Additionally, Accenture disclosed in its Q4 2023 earnings press release that its revenue for generative AI projects grew to $300 million for 2023.

In November 2023, Accenture announced plans to launch a network of generative AI studios in North America where companies can explore ways to integrate generative AI applications. These studies are expected to be at Accenture Innovation Hubs in Chicago, Houston, New York, San Francisco, Toronto, and Washington, DC.

2. IBM

US-based technology corporation IBM makes up 2% of this market. To position itself for the opportunities that generative AI brings, the company announced it had established a “Center of Excellence (CoE) for generative AI,” which as of May 2023, already had over 1,000 consultants specialized in generative AI. The CoE operates alongside IBM’s AI and Automation practice, which includes over 21,000 data and AI consultants.

3. Capgemini

France-based IT services company Capgemini also has a 2% share in this market, offering consulting services intended to help clients adopt key technologies such as the cloud and AI. In July 2023, Capgemini announced the launch of a portfolio of generative AI services, including in the following areas:

• Strategy
• Customer experience
• Software engineering
• Custom solutions for enterprise

One of Capgemini’s current customers is London Heathrow Airport which aims to improve traveler experiences through its “Generative AI for Customer Experience” offer. Heathrow’s Director of Marketing and Digital, Pete Burns, stated that the project is intended to “assist, empower and delight passengers” with tailored customer service solutions.

4. The many others

Past this point, the remaining 86% of the market becomes a cornucopia of specialized generative AI services providers and larger general consulting and system integration companies, each taking a bite of the rapidly growing segment.

As an example, in April 2023, UK-based professional services company PwC announced plans to invest $1 billion over three years to not only grow its AI offerings but also transform how it works by using generative AI. Additionally, in July 2023, global consultancy firm McKinsey & Company announced it partnered with AI startup Cohere to provide customized AI solutions to its enterprise clients.

Generative AI company landscape outlook

The enterprise generative AI market is roughly a year old, and already, the generative AI companies landscape appears vast.

IoT Analytics released its first generative AI report, the Generative AI Trend Report, in March 2023. Since then, more foundational models and platforms have emerged, e.g., OpenAI’s GPT4 Turbo, Google’s Gemini, or Microsoft’s Phi-2. At the same time, the demand for data center GPUs exploded, which is also mirrored in NVIDIA’s stock performance (+231% year-to-date as of 14 December 2023). Finally, consulting giants have made investments to position themselves in the generative AI services market, such as Accenture’s $3B investment in AI and its pledge to double “AI talent.”

As part of this research, we talked to 30+ experts in the field and gathered information on 270+ generative AI projects and analyzed which industries and departments are fastest to adopt and which vendors are most selected today.
The coming months will reveal how many of those projects will deliver value besides just being a marketing coup or how many of those currently in the proof-of-concept stage will move forward. Most companies are only now forging their generative AI strategies and considering whether to build foundational models from scratch based on industry-specific data, use an out-of-the-box propriety model, or fine-tune open-source models. All of this comes as generative AI companies release new products at unprecedented speed.

There is still a lot of movement in the generative AI company landscape, and there will be more in the foreseeable future. IoT Analytics will stay on top of this space, with a follow-up report expected in 2024.

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The Internet of Things (IoT) landscape in 2024 is set for transformative changes, driven by advancements in cybersecurity, artificial intelligence (AI), and a plethora of emerging technologies, as IoT systems become increasingly integrated into critical infrastructure.

In this article, I shall delve into the various aspects of this transformation, exploring the impact of AI and machine learning (ML) in creating intelligent IoT systems, the rise of edge computing, the integration of blockchain for enhanced security, the introduction of ultra-thin smart shipping labels, the incorporation of the SGP.32 standard, and IoT’s burgeoning role in sustainability.

Increased focus on IoT cybersecurity

In 2024, the integration of IoT devices into vital systems like Smart Cities, coupled with the increased adoption of technologies such as 5G, eSIM, iSIM, and satellite connectivity, has emphasised the importance of robust cybersecurity measures. These advancements have made IoT devices more versatile and efficient, but they also necessitate enhanced focus on safeguarding data integrity and device security.

To address these needs, there’s a growing emphasis on deploying advanced encryption and rigorous security protocols. These measures ensure the protection of data transmitted between IoT devices and central systems. Additionally, continuous monitoring and real-time threat detection, powered by AI and ML, may well become standard practices. They help in promptly identifying and responding to potential security breaches, maintaining the integrity and reliability of IoT networks.

AI and ML enabling intelligent IoT systems

AI and ML are revolutionising almost everything, including IoT. By analysing massive amounts of data instantaneously, AI enhances IoT applications such as predictive maintenance and energy management. This synergy, combined with centralised IoT management platforms, leads to unparalleled operational efficiency.

In 2024, the integration of AI and ML will become much more embedded in IoT infrastructures. The blend of AI’s analytical capabilities with IoT’s data collection and monitoring functions creates an ecosystem where operational insights are gathered more efficiently and effectively, leading to smarter, more responsive IoT systems.

Edge computing enhancing IoT performance

Edge computing is revolutionising IoT performance by processing data closer to its source. This method significantly reduces latency, crucial for real-time applications such as autonomous vehicles, industrial automation, and augmented reality. These advancements are particularly pertinent in smart cities, healthcare, manufacturing, and retail, where they facilitate immediate data analysis and improve service quality.

Looking forward, the integration of AI and machine learning with edge computing is expected to increase, enabling edge devices to independently make complex decisions. The expansion of 5G networks will enhance communication between these devices, promoting faster, more efficient data processing. Furthermore, edge computing’s role in reducing energy consumption and carbon emissions underscores its significance in fostering a more sustainable IoT ecosystem.

Blockchain for IoT security

As IoT devices increasingly handle sensitive data, the role of blockchain in bolstering IoT security is becoming more prominent. Blockchain’s decentralised nature offers enhanced data integrity, making it a key player in protecting against the growing cybersecurity threats in the IoT landscape. Its integration with AI and ML is particularly noteworthy, representing a significant leap forward in building a resilient IoT infrastructure.

This combination promises to shape a stronger, more secure IoT ecosystem for 2024 and beyond, especially as the attack surface of IoT expands. Blockchain’s ability to ensure the authenticity and security of data transactions across the network is vital in this context, presenting a robust solution to the evolving challenges in IoT security.

Ultra-thin, low-power smart shipping labels

The ultra-thin, low-power smart shipping labels, first seen in early 2023 with our very own Smart Shipping Label, which is equipped with a printed, eco-friendly battery, features an eSIM, and supports up to 1000 messages across LTE-M, NB-IoT, and 2G networks.

Such labels will become much more prolific in 2024, due to their function as advanced tracking devices for items both large and small. They are capable of real-time monitoring of location, temperature, and package integrity, ensuring secure and efficient transit.

Thanks to their adaptability for various logistical needs, from tracking small documents to larger assets, these smart labels not only enhance supply chain efficiency but also align with sustainability goals, representing a significant advancement in IoT-driven asset management.

Integrating SGP.32 into the IoT ecosystem

The integration of the SGP.32 standard into the IoT ecosystem in 2024 heralds a significant advancement in device capabilities and application efficiency. SGP.32 is pivotal for use cases that demand high location accuracy, like precision agriculture, by providing superior geolocation services.

Moreover, the incorporation of SGP.32 plays a key role in the expanded use of eSIMs within IoT devices. This is particularly beneficial for global IoT deployments, as it simplifies the complexities associated with device management across different regions. Features like remote provisioning and profile swapping inherent in eSIM technology are instrumental in enhancing operational efficiency.

This development is not just a technological leap; it’s a strategic enabler for more efficient, globally connected, and responsive IoT ecosystems. The impact of integrating SGP.32 will be felt across various sectors, significantly contributing to the overall evolution and effectiveness of IoT applications.

IoT’s sustainability drive intensifies

Finally, in 2024 IoT will continue playing its pivotal role in driving sustainability across various sectors. Advanced, energy-efficient sensors, coupled with AI, are revolutionising resource management by enabling precise monitoring and control. This technological synergy is significantly reducing waste and optimising energy use.

In industries like manufacturing, IoT adoption is being accelerated by tightening global regulations, which are mandating more sustainable practices and better ecological footprints. IoT technologies are not only enhancing operational efficiencies but also promoting environmental stewardship. The implementation of smart systems in areas such as energy management and waste reduction are evidence of IoT’s growing influence in creating a more sustainable future.

As the world grapples with environmental challenges, the integration of IoT in sustainability efforts is becoming increasingly crucial, marking a new era where technology and ecology harmoniously intersect.

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In the face of escalating climate challenges, technology has emerged as a beacon of hope. The Internet of Things (IoT) stands out as a particularly powerful tool in the global effort to promote environmental sustainability. With its network of interconnected devices and sensors, IoT offers innovative solutions to monitor, understand, and address environmental issues, contributing significantly to the fight against climate change.

IoT: A Game-Changer for Climate Monitoring

Climate change is a complex beast, with a multitude of variables that must be tracked and analyzed. IoT technologies offer unprecedented granularity in environmental monitoring, with sensors capable of providing real-time data on everything from atmospheric CO2 levels to the health of ocean ecosystems. This data is invaluable for researchers and policymakers alike, offering up-to-the-minute insights that can inform responsive and effective environmental policy.

Energizing Renewables with IoT

Renewable energy sources like solar and wind power are crucial in the transition away from fossil fuels. IoT is instrumental in optimizing the performance of these energy sources. Smart sensors can track wind patterns and sunlight exposure, adjusting the positioning of turbines and solar panels to maximize energy capture. Moreover, IoT systems help in predicting maintenance needs, reducing downtime, and enhancing the overall efficiency of renewable energy infrastructures.

Smart Agriculture: Growing More with Less

Agriculture consumes a vast amount of our planet’s resources, but IoT is helping to change that. Precision farming techniques, underpinned by IoT, enable farmers to monitor soil moisture levels and crop health with pinpoint accuracy, leading to more judicious use of water and pesticides. This not only helps in conserving precious resources but also results in higher yields and better-quality produce.

Waste Not: IoT for Waste Reduction

Waste management is another area where IoT shines. Smart waste bins can signal when they are full, optimizing collection routes and frequencies. IoT systems also play a crucial role in the recycling industry, where they can sort materials more efficiently and identify contaminants that can hinder the recycling process.

The Smart Grid: An IoT-Enabled Energy Network

One of the most significant applications of IoT in sustainability is the development of smart grids. These intelligent energy distribution networks can balance supply and demand in real time, reduce energy wastage, and integrate a higher percentage of renewable energy sources. Consumers can play an active role in energy conservation through smart meters that provide real-time feedback on energy consumption, encouraging more responsible usage patterns.

Challenges to Overcome

Despite its vast potential, the widespread adoption of IoT for environmental sustainability is not without challenges. The energy consumption of IoT devices themselves is a concern; thus, it is imperative that these devices are designed to be as energy-efficient as possible. Additionally, the production of IoT devices must become greener, employing sustainable materials and minimizing waste.

Data privacy and security are also critical issues. The vast amounts of data collected by IoT devices must be kept secure to protect against breaches that could undermine public trust in these technologies.

Policy Implications and the Path Forward

To fully harness the potential of IoT for environmental sustainability, collaborative efforts are needed. Policymakers must create frameworks that encourage the development and deployment of sustainable IoT solutions. This includes investing in infrastructure, funding research and development, and setting industry standards that prioritize sustainability.

Cross-sector partnerships are equally important. The technology sector must work with environmental scientists, urban planners, and agricultural experts to create IoT solutions that are both technologically advanced and environmentally sound.

Conclusion

IoT offers a powerful arsenal of tools in the fight against climate change, from optimizing renewable energy to enabling smarter agriculture and waste management. However, the journey to a sustainable future requires more than just technology; it demands a collective commitment to innovation, responsible usage, and global cooperation. As we continue to harness the potential of IoT, we move closer to a more sustainable world where technology and the environment exist in harmony, combating climate change one smart solution at a time.

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The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

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IoT Analytics published an analysis based on the “Predictive Maintenance & Asset Performance Market Report 2023–2028” report and highlights 5 key insights related to the $5.5 billion predictive maintenance market.

Key insights:

• The global predictive maintenance market grew to $5.5 billion in 2022–a growth of 11% from 2021—with an estimated CAGR of 17% by 2028, according to the Predictive Maintenance and Asset Performance Market Report 2023–2028.
• With median unplanned downtime costs larger than $100,000 per hour, the importance of accurately predicting failures of large assets has never been higher.
• This article shares 5 key highlights of the predictive maintenance market: 1) The market is valued at $5.5 billion, 2) there are 3 different types of predictive maintenance, 3) predictive maintenance software tools share 6 features, 4) predictive maintenance is commonly being worked into the maintenance workflow, and 5) successful standalone solutions vendors specialize in an industry or asset.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Predictive Maintenance continues to be one of the leading use cases for Industry 4.0 and digital transformation, especially in process industries where asset failures can quickly go into the hundreds of thousands of dollars. It is great to see that the market is moving ahead with AI integration into existing APM and CMMS systems and that prediction accuracies are improving. Nonetheless, we still have a long way to go as false alerts remain commonplace.”

Fernando Brügge, Senior Analyst at IoT Analytics, adds that “Predictive maintenance is reaching new heights of maturity and sophistication thanks to the rapid advancements in artificial intelligence, hardware, and data engineering. We are at the point where these technologies enable us to collect, process, and analyze massive amounts of data from multiple sources, and use them to build more accurate and reliable models of machine health and behavior, as well as to determine potential courses of action to fix machine issues. In this way, predictive maintenance is not only a smart way to optimize equipment performance and lifecycle, but also a strategic way to enhance operational efficiency and competitiveness in a rapidly evolving industrial space.”

Predictive maintenance market: 5 highlights for 2024 and beyond

One accurately predicted failure of a large asset is worth more than $100,000 in many industries.

Our latest research highlights, among many other things, that the median unplanned downtime cost across 11 industries is approximately $125,000 per hour. With critical unplanned outages in facilities in industries such as oil and gas, chemicals, or metals occurring several times a year, an investment into predictive maintenance can amortize with the first correct prediction.

Unfortunately, there is a flip side: the accuracy of many predictive maintenance solutions is lower than 50%. This creates headaches for maintenance organizations that often run to an asset to find it is perfectly fine, eroding trust in the entire solution.

That said, vendors have been making strides to increase prediction accuracy, with more data sources and better analysis methods becoming available, including AI-driven analysis. There are positive signs that this determination for better prediction accuracy is helping end users: our research indicates that 95% of predictive maintenance adopters reported a positive ROI, with 27% of these reporting amortization in less than a year.

General search interest in predictive maintenance and related concepts has been on the rise for the last 12 years. Online searches for the term have grown nearly threefold since we initiated coverage on the topic in 2017 and have outgrown condition-based maintenance and asset performance management (APM) related searches.

Indeed, predictive maintenance appears to be well on track to be the must-have killer application we made it out to be in 2021.

In this fourth installment of our predictive maintenance market coverage, we look at 5 important highlights to note about the market going into 2024:

1. The predictive maintenance market is valued at $5.5 billion (2022)
2. There are 3 different types of predictive maintenance, with anomaly detection on the rise
3. Predictive maintenance software tools share 6 common features
4. Integration into the maintenance workflow is becoming important
5. Successful standalone solutions vendors specialize in an industry or asset

Highlight 1: The predictive maintenance market is valued at $5.5 billion

The predictive maintenance market reached $5.5 billion in 2022. Uncertain economic conditions and other manufacturing priorities in the last 2 years resulted in 11% market growth between 2021 and 2022. With companies reinvesting in efficiency, safety, and operational performance, we expect the market for predictive maintenance to grow to 17% per year until 2028.

Our research indicates that industries with heavy assets and high downtime costs are driving the adoption of predictive maintenance solutions (e.g., oil & gas, chemicals, mining & metals).

Highlight 2: There are 3 different types of predictive maintenance, with anomaly detection on the rise

As the market has evolved, 3 noticeable predictive maintenance types have developed:

1. Indirect failure prediction
2. Anomaly detection
3. Remaining useful life (RUL)

The difference between these largely comes down to the objectives, methods of data analysis, and type of output/information they provide. RUL is the hardest to achieve due to resource demands and environmental factors that make it difficult to scale. Indirect failure prediction has been the most used approach, but our research indicates that anomaly detection is on the rise.

1. Indirect failure prediction

The indirect failure prediction approach generally takes a machine health score approach based on a function of maintenance requirements, operating conditions, and running history. This approach often relies on general analysis to yield this score, though supervised machine learning methods can be used if a significant amount of data is available.

Benefits:

• Scalability – Indirect failure prediction can be more easily scaled since they rely on equipment manufacturers’ specifications that are more or less the same across machines of the same type.
• Cost effective – Indirect failure prediction can use existing sensors and data, reducing the need for additional instrumentation.

Limitations:

• Failure time-window accuracy – Indirect failure prediction does not give a timeline of when machines will fail. This can be a problem for organizations with very costly downtimes (e.g., heavy equipment industries).
• Dependent on historical data – Indirect failure prediction’s effectiveness relies on the availability of extensive historical data for accurate modeling.

2. Anomaly detection

Anomaly detection is the process of finding and identifying irregularities in the data (i.e., data points that deviate from the usual patterns or trends). While the indirect failure prediction and RUL approaches use failure data to predict future failures, anomaly detection uses the “normal” asset profile to detect deviations from the norm. These deviations can indicate potential problems, such as faults, errors, defects, or malfunctions, that need to be detected and addressed before they cause serious damage or downtime.
This approach makes it easier when there is not a good repository of failure data, and it often relies on unsupervised machine learning.

Benefits

• Low data and hardware requirements – Anomaly detection models can identify issues without being trained on failure data. Further, since these models need less data, they do not demand high computing power.
• High scalability and model transferability – Anomaly detection models are trained on normal operation data, so they can easily be applied to different machines without retraining or adaptation.

Limitations

• Failure time-window accuracy – As with indirect failure prediction, anomaly detection models do not give a timeline of when machines will fail, which can be a problem for organizations with very costly downtimes.
• Presence of false positives – While most solutions in the market can distinguish between critical and noncritical anomalies, the choice of unsupervised machine learning models is still important as it can affect how well this distinction can be made (e.g., autoencoders and generative adversarial networks do not capture the complexity of normal operations).

3. Remaining useful life (RUL)

RUL is the expected machine life or usage time remaining before the machine requires repair or replacement. Life or usage time is defined in terms of whatever quantity is used to measure system life (e.g., distance traveled, repetition cycles performed, or the time since the start of operation).

This approach relies on condition indicators extracted from sensor data—that is, as a system degrades in a predictable way, data from the sensors match the expected degradation values. A condition indicator can be any factor useful for distinguishing normal operations from faulty ones. These indicators are extracted from system data taken under known conditions to train a model that can diagnose or predict the condition of a system based on new data taken under unknown conditions.

Predictions from these RUL models are statistical estimates with associated uncertainty, resulting in a probability distribution.

Benefits

• Failure prediction time-window – RUL is especially useful for industries where maintenance is very costly and needs advanced planning, such as heavy-equipment industries.
• Output robustness – Since RUL estimates rely on high-quality and detailed data, they tend to be more robust and reliable.

Limitations

• Resource demand – Training large models requires powerful computing hardware, especially if done on-premises.
• Model transferability and scalability – Different environments and usage patterns can cause different failure modes for the same type of equipment. This means the model needs to be retrained for each specific case, reducing its scalability and generalizability.

Highlight 3: Predictive maintenance software tools have 6 common features

Software is the largest segment of the predictive maintenance tech stack, making up 44% of the predictive maintenance market in 2022.

Our report shows that even though most successful predictive maintenance software vendors specialize in industries or assets, there are 6 common features between their various solution software suites:

1. Data collection
2. Analytics and model development
3. Pre-trained models
4. Status visualization, alerting, and user feedback
5. Third-party integration
6. Prescriptive actions

We will delve further into these features and offer an example snapshot for each from various software vendors. The examples are to help readers understand some approaches to these common features.

Feature 1: Data collection

Data collection tools within predictive maintenance software collect, normalize, and store data on asset health/condition parameters. They also collect other data types needed to identify and predict upcoming issues, such as business and process data.

Snapshot:

US-based predictive maintenance software vendor Predictronics offers PDX DAQ, an application that allows users to synchronize data collection from multiple sources for any given period of time. The solution creates a database that harmonizes all the timestamps from different sensors, which Predictronics claims yields the necessary information for analysis and producing real-time, impactful results.

Feature 2: Analytics and model development

Analytics and model development tools within Predictive Maintenance software analyze, interpret, and communicate data patterns, including analytics discovery (e.g., RCA, AD modules) and modeling (e.g., feature engineering and model selection and testing).

Snapshot:

US-based predictive maintenance software vendor Falkonry (recently acquired by IFS) offers Workbench within its Time Series AI platform, a low-code ML-based solution aiming to help users—specifically, operational practitioners, including production, equipment, or manufacturing engineers—discover patterns such as early warning or stages of deterioration in complex physical systems. It also aims to enable users to analyze large amounts of data and build predictive models.

Feature 3: Pre-trained models

Pre-trained models are just that: ready-to-use models typically designed for specific assets in specific industries. These models include capabilities and references for specific assets or failure modes (e.g., fouling for heat exchangers, wear and corrosion for fans, or valve leakage for compressors). These are meant to help end users see examples of models so they can build on them or develop custom predictive maintenance algorithms.

Snapshot:

US-based asset management software vendor AspenTech (recently acquired by Emerson), offers Mtell, an application that includes pre-populated, industry-specific asset templates to help users select sensors for common asset categories and AI functionality to create and deploy models quickly for PdM applications (e.g., for specific compressors, turbines, and blowers).

Feature 4: Status visualization, alerting, and user feedback

Status visualization, alerting, and user feedback tools within predictive maintenance software automatically communicate asset-related data/insights for various personas. These insights often include status dashboards and automatic alerts that trigger work orders or corrective actions, maintenance planning, and optimization. These tools also enable users to provide feedback concerning the accuracy of alerts.

Snapshot:

US-based analytics software vendor SAS Institute offersAsset Performance Analytics, which includes status dashboards and automatic alerts intended to notify operations staff and managers of impending failure so that organizations have time to identify and fix issues before they become costly problems.

Feature 5: Third-party integration

Third-party integration enables users to connect their predictive maintenance software to other software systems and workflow management tools, such as ERP, MES, CMMS, APM (more on APM integration in Highlight 4), and Field Service.

Snapshot:

SKF, a Swedish bearing and seal manufacturing company also offering maintenance products, offers a condition monitoring and predictive maintenance solution that interfaces with existing plant control systems (e.g., MES or SCADA) and other external dashboards (e.g., ERP). It also provides insights to operators in the field via alarms and visualization on handheld devices.

Feature 6: Prescriptive actions

Prescriptive action features typically suggest the optimal actions to take in case of an (upcoming) failure. These actions are typically prioritized by criteria that are set when the algorithm is designed.

The actions that are prescribed by the software vary depending on the nature and urgency of the issue. They may require multiple steps or interventions. For instance, some actions may involve automatically adjusting the equipment parameters or informing the maintenance and operation teams about the necessary procedures to ensure equipment efficiency.

Snapshot:

Marathon, a predictive maintenance software solution from Norway-based Arundo, provides a feature known as Investigations that aims to provide the workflow and instructions to resolve equipment problems according to prescribed corporate standards.

Highlight 4: Integration into the maintenance workflow is becoming important

In its early days, predictive maintenance was mostly a standalone solution developed by startups to address specific customer needs. However, our report highlights a notable trend of sophisticated predictive maintenance solutions integrating into larger APM and computerized maintenance (CMMS) solutions.

APM is a strategic equipment management approach designed to help optimize the performance and maintenance efficiency of individual assets and entire plants or fleets. APM aims to improve asset efficiency, availability, reliability, maintainability, and overall life cycle value.

Various APM vendors are introducing predictive maintenance software tools within their APM offerings. The solutions aim to tie the different capabilities into 1 thread:

• Knowing when a machine will fail and mapping how failures could affect production or output
• Estimating how much fixing or preventing an issue will cost
• Making recommendations on whether it is worth fixing or preventing a problem

By including a sophisticated predictive maintenance solution in an end-to-end asset flow, APM players are trying to become the main partners for their customers’ digitalization journeys.

Our report lists 9 key components of APM:

1. Asset health monitoring
2. Maintenance optimization
3. Reliability analysis
4. Integrity management
5. Performance optimization
6. Failure prediction <- Predictive maintenance resides here 7. Digital asset twin 8. Sustainability management 9. Energy optimization

We assess in our report that improving the failure prediction module of APM solutions is currently one of the key initiatives of leading APM vendors.

Highlight 5: Successful standalone solutions vendors specialize in an industry or asset

Our research found that 30% of predictive maintenance vendors offer standalone, industry- or asset-specific solutions. By tailoring their efforts to specific niches in which they have acquired domain knowledge, they can discern the types of equipment and industries in which their solutions offer the most end-user benefits.

Snapshot:

Israel-based data science company ShiraTech Knowtion uses its equipment expertise in its offering of Predicto, an industrial IoT platform focused on industrial maintenance teams. The platform enables reading and processing of sensor data from production plants, ideally based on its own multisensing devices (iCOMOX). The company has developed specific offerings for motors, pumps, conveyors, and pipes. These asset-tailored offerings enable the company to scale.

6 considerations for predictive maintenance vendors

Six questions that predictive maintenance vendors should ask themselves based on insights in this article:

1. Market growth and strategy: Given the market’s growth to $5.5 billion and the projected increase to $14.3 billion by 2028, how can our company align its strategy to capitalize on this market expansion?
2. Accuracy improvement: Considering the current lower-than-50% accuracy of many predictive maintenance solutions, what innovative approaches or technologies can we adopt to enhance the accuracy of our predictions?
3. ROI communication: How can we better communicate the positive ROI of predictive maintenance to potential customers, especially those who are skeptical due to past experiences with inaccurate solutions?
4. Industry specialization: Given that the most successful vendors are specialized in specific industries, assets, or use cases, should we consider narrowing our focus, and if so, in which areas?
5. Data collection and integration: Are we effectively collecting the right kinds of data (including business and process data) and integrating it into the right IT systems for optimal predictive maintenance?
6. Software tool features: Do our software tools encompass the 6 common features identified in the report (data collection, analytics and model development, pre-trained models, status visualization, third-party integration, prescriptive actions), and are they competitive in the current market?

8 considerations for those looking to adopt or update predictive maintenance solutions

Eight questions that those looking to adopt or update predictive maintenance solutions should ask themselves based on insights in this article:

1. Solution type suitability: Which type of predictive maintenance solution (indirect failure prediction, anomaly detection, or RUL) best aligns with our specific maintenance needs and operational goals?
2. Integration with existing systems: How easily can predictive maintenance solutions integrate into our existing maintenance workflows and asset management systems?
3. Vendor specialization: Should we look for a vendor specialized in our industry, specific assets, or use cases, and how would that benefit us over a generalist provider?
4. Data collection and analysis: Do we have the necessary infrastructure for effective data collection and analysis to support a predictive maintenance system?
5. Accuracy and trustworthiness: How can we evaluate and ensure the accuracy of the predictive maintenance solution to build trust within our maintenance team?
6. Scalability and future growth: How scalable are the predictive maintenance solutions, and can they accommodate our future growth?
7. Software features and functionality: Do the software tools offered by vendors have all the key features we need, such as data collection, analytics, and third-party integration?
8. Market trends and innovation: Given the evolving nature of the predictive maintenance market, how can we stay informed about the latest innovations and ensure that our solution remains cutting-edge?

The post Predictive maintenance market: 5 highlights for 2024 and beyond appeared first on IoT Business News.

Edge computing has emerged as a transformative technology for the Internet of Things (IoT), fundamentally altering how data is processed and managed within IoT ecosystems. By enabling data processing closer to the source, edge computing significantly enhances IoT infrastructure, leading to improved efficiency, reduced latency, and enhanced security. This article delves into the intricacies of edge computing in the IoT domain, exploring its impact and the potential it holds for the future of IoT.

Introduction to Edge Computing in IoT

The Internet of Things, a network of interconnected devices capable of collecting and exchanging data, has seen exponential growth in recent years. IoT devices range from simple sensors to complex industrial machines. Traditionally, IoT devices would send all collected data to centralized cloud-based services for processing and analysis. However, this approach often leads to high latency and increased bandwidth usage, which can be detrimental in scenarios requiring real-time data processing. This is where edge computing comes into play.

Edge computing refers to data processing at or near the source of data generation, rather than relying solely on a central data-processing warehouse. This means that data can be processed by the device itself or by a local computer or server, which is located close to the IoT device.

Enhanced Efficiency and Reduced Latency

One of the primary advantages of edge computing in IoT is the significant reduction in latency. By processing data locally, the need to send all data to a central cloud for processing is eliminated, thereby reducing the time it takes for the data to be processed and the response to be sent back. This is particularly crucial in applications where real-time processing is essential, such as autonomous vehicles, industrial automation, and smart grids.

Moreover, edge computing reduces the bandwidth required for data transmission, which is particularly important given the growing number of IoT devices and the massive volume of data they generate. By processing data locally and only sending relevant or processed data to the cloud, edge computing alleviates the strain on network bandwidth.

Improved Security and Privacy

Another critical aspect of edge computing in IoT is the enhancement of security and privacy. By processing data locally, sensitive information does not have to travel over the network to a centralized cloud, reducing the exposure to potential security breaches during transmission. Local data processing also means that in the event of a network breach, not all data is compromised, as some of it remains on the local device or edge server.

Furthermore, edge computing allows for better compliance with data privacy regulations, as data can be processed and stored locally, adhering to the legal requirements of the region in which the IoT device is located.

Enabling Advanced IoT Applications

Edge computing unlocks the potential for more advanced IoT applications. For instance, in the field of healthcare, wearable devices can monitor patient health data in real-time, processing and analyzing data on the spot to provide immediate feedback or alert healthcare providers in case of an emergency. In industrial settings, edge computing allows for predictive maintenance of machinery, where sensors can process data on the machine’s performance and predict failures before they occur.

Challenges and Considerations

Despite its advantages, implementing edge computing in IoT comes with its own set of challenges. One of the primary concerns is the management and maintenance of edge computing nodes. Unlike centralized cloud servers, edge devices are distributed and may be located in remote or hard-to-reach areas, making management and maintenance more challenging.

Additionally, ensuring the security of edge computing devices is crucial, as these devices could become targets for cyber-attacks. Unlike centralized data centers, which typically have robust security measures in place, edge devices may not have the same level of security, making them vulnerable.

The Future of Edge Computing in IoT

Looking ahead, the future of edge computing in IoT appears promising. With advancements in technology, edge devices are becoming more powerful, capable of handling more complex data processing tasks. This evolution is expected to drive further adoption of edge computing in various sectors.

In conclusion, edge computing represents a paradigm shift in how data is processed within IoT infrastructures. By enabling data processing closer to the source, it addresses the challenges of latency, bandwidth usage, and security. Although there are challenges in implementing edge computing, its benefits are significant, paving the way for more efficient, secure, and advanced IoT applications. As technology continues to evolve, edge computing is set to play an increasingly pivotal role in the IoT landscape, driving innovation and enabling new possibilities.

The post The Impact of Edge Computing on Data Processing and IoT Infrastructures appeared first on IoT Business News.

IoT Analytics released a new analysis, based on the ’Industrial IoT & Industry 4.0 Case Study Report 2023’.

Key insights:

• Digitalization has become essential for industrial companies worldwide, as IoT Analytics expects the industrial IoT market to reach $145 billion in 2023.
• The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves into 22 case studies, exploring their project objectives, technologies deployed, lessons learned, challenges, and outcomes.
• In this article, we share five learnings from the report: 1) Upgrading the ERP is often the first step in digital transformation, 2) cloud-native data storage and streaming are coming up, 3) first successful implementations of private 5G use cases, 4) digitalization becoming a prerequisite to achieving sustainability, and 5) the continued journey toward predictive maintenance.

Key quotes:

Rajini Nair, Analyst at IoT Analytics, remarks: “Upon analyzing the case studies outlined in the report, it becomes evident that digitalization is a key catalyst driving technological progress in industrial settings. This includes the enhancement of ERP systems and the migration of data to cloud platforms equipped with real-time streaming capabilities. Furthermore, digitalization proves essential in harmonizing companies with their sustainability objectives. Strategies such as the implementation of predictive maintenance algorithms and the adoption of private industrial 5G use cases are leveraged to improve operational efficiency. Looking forward, the technological landscape is transforming with the rise of AI, poised to shape the future of manufacturing.”

Digitalization has become crucial to manufacturers globally

Digitalization has become crucial for manufacturers in their respective competitive landscapes. IoT Analytics estimates the industrial IoT market to reach $145B in 2023, with a CAGR of 17.9% until 2030, as more and more companies undertake digital transformation initiatives.

For many, digitalization has already become a game changer:

• Carmaker Mercedes has achieved 25% greater efficiency in its S-class assembly after optimizing the value chain and introducing innovative technologies at its Germany-based Factory 56 facility.
• Energy giant TotalEnergies aims to generate $1.5 billion annually in savings with its digital solutions by 2025.
• Chemical company Covestro increased efficiency and reduced unnecessary downtime by shifting from calendar-based to condition-based maintenance.

These are just some of the benefits of the digital transformation initiatives our research uncovered as part of the 255-page Industrial IoT and Industry 4.0 Case Studies Report 2023. The report delves into 22 recent industrial digital transformation case studies, looking at initiatives related to digital transformation, data architecture, predictive maintenance, AI, and industrial 5G.

Benefits of case studies for digitalization journeys

Case studies by peers or companies in other industries are a great way to learn about digitalization, identify common challenges, develop a view of best practices, and understand how companies manage to scale.

The 22 case studies in our report offer readers a diverse set of manufacturing examples of current IIoT and Industry 4.0 projects, along with project objectives and takeaways from each. Our analysis of these takeaways yielded many trends in these companies’ digital transformation journeys, five of which we will delve into in this article (including selected highlights from the report):

1. ERP: An upgraded ERP is often the first step to digital transformation.
2. Cloud: Cloud-native data storage and streaming are increasingly accepted.
3. 5G: First manufacturers have successfully implemented private 5G use cases.
4. Sustainability: Digitalization is becoming a prerequisite to achieving sustainability objectives.
5. Maintenance: The journey toward predictive maintenance and remote monitoring continues.

Learning 1: An upgraded ERP is often the first step to digital transformation

Our analysis found that many manufacturers elect to prioritize upgrading their ERP systems to ensure their various data sources are connected before prioritizing other digital transformation initiatives. Since ERP systems are often the central nervous system of a business, prioritizing an updated ERP allows different departments to share and operate on the same data, reducing errors and improving efficiency.

Selected highlight: Celanese

In recent years, US-based chemical manufacturer Celanese has acquired several businesses or divisions from other companies. Celanese had been operating on a legacy SAP ERP system, and the acquired assets had different legacy ERP environments, making cross-section integration difficult. Celanese’s global CIO, Sameer Purao, did not want the company to invest in integrating the new acquisitions into older technology. Given this situation and Celanese’s adoption of a strategic, long-term approach to a scalable digital transformation plan, an ERP upgrade became necessary.

“Our previous ERP system had been a backbone, but it was close to 20 years old. Given its age, we didn’t want to invest in transforming until we upgraded that piece first. The acquisitions underscored that it wouldn’t make sense to invest in integrating them into older technology, so we opted to upgrade.” – Sameer Purao, senior vice president and global CIO, Celanese Corporation

In May 2023, Celanese announced that it had completed its upgrade to SAP S/4HANA. Further, since Celanese acquired DuPont’s Mobility & Materials (M&M) business just six months prior, it quickly cutover to upgrading M&M’s legacy ERP system as well, which is expected to be completed in the first half of 2024.

While there are other major aspects of Celanese’s digital transformation journey detailed in our report, this upgrade to its ERP provided a stronger backbone from which other digitalization solutions could be built. Another benefit of this upgrade is improved visibility and collaboration, enhancing transparency and teamwork and allowing for efficient data access across the enterprise.

Learning 2: Cloud-native data storage and streaming are increasingly accepted

The cloud market nearly doubled between 2020 and 2022, growing from $109 billion to $206 billion, based on our analysis of global cloud projects. While the COVID-19 pandemic certainly played a major role, it was not the only growth factor. Our analysis found that large-scale enterprise digitalization efforts and strong SaaS adoption also helped fuel this growth.

Cloud storage and data streaming allow companies to centralize and share their data with a smaller footprint than running their own on-premises servers, which comes with footprint and maintenance costs. Moving these services to the cloud also allows companies to scale without the need for significant capital investment in physical hardware.

Selected highlight: Michelin

In 2019, tire manufacturer Michelin started using Apache’s Kafka event streaming platform on-premises in its data centers to gain real-time insights and process data as continuous streams. However, as its operational footprint expanded, so did the resources it had to dedicate to maintaining the solution. By Q4 2019, Michelin’s IT department initiated its migration to the cloud, with Microsoft Azure as the cloud partner.

“One of the challenges with [streaming technology] Kafka was its operational complexity, especially as the footprint expanded across our organization. It’s a complex, distributed system, so we had to allocate a lot of our valuable technical resources and expertise to babysit it and keep it running.” – – Olivier Jauze, now CTO of Experiences Business Line, Michelin

By 2021, Michelin migrated its services to Confluent Cloud for Azure, a Kafka-based platform, to support its multi-cloud environment. Soon after, the company began exploring use case projects and has since migrated one of its most critical projects, online order management, to the cloud—replacing its on-premises orchestrator. By 2023, Michelin expanded its cloud-based event streaming architecture into several departments, including supply chain management, customer services, manufacturing, and R&D.

Through its adoption of cloud-native data storage and streaming, Michelin achieved the following benefits (among other things):

• Cost savings: Estimated 35% in cost savings in the cloud compared to on-premises operations
• Improved uptime: 99.99% uptime

Learning 3: First manufacturers have successfully implemented private 5G use cases

As 5G continues its public rollout globally, some manufacturers have successfully deployed private 5G networks to enable new use cases within their facilities. While faster speeds and lower latency may seem like key adoption drivers, our analysis found that improved reliability over Wi-Fi, enhanced cybersecurity, and the ability to access data locally are the core motivating factors.

Our analysis also found that during the public rollout of 5G, some companies did not simply dive into integrating 5G-specific technology. Instead, many integrated robust LTE solutions that were upgradable to 5G with relative ease (or so-called 4.9G solutions) once the technology evolved or became approved for industrial use.

Selected highlight: Airbus

To increase aircraft production and validation efficiency, European multinational aerospace corporation Airbus partnered with Ericsson, a Swedish multinational telecommunications company, in 2021 to implement private industrial 5G networks at 11 aircraft assembly manufacturing sites in Europe. The approach began with implementing 4G networks that either already had 5G capabilities or could seamlessly upgrade to 5G.

However, Airbus is not limiting this deployment to its European facilities. During a Q&A at the 5G Manufacturing Forum in November 2022, Hakim Achouri, the 5G and IoT solutions expert for digital aviation at Airbus, noted, “Airbus is going way beyond 11 networks at 11 sites, expanding beyond its core European manufacturing bases in France and Germany, to also deploy private 5G in Canada, China, Spain, the UK, and the US.”

With its implementation of private 5G networks at its production and assembly facilities, Airbus has realized the following benefits:

• Ability to implement advanced use cases: This includes site surveillance, efficient flight-to-ground data offloads, quality inspections, and the operation of automated guided vehicles (AGVs).
• Enhanced user experience: With increased speed, bandwidth, and reliability, employees at the production sites have access to more data, making operations smoother, more efficient, and more secure.
• Scalability through reusability: By developing a pattern in its strategy, Airbus was able to roll out private 4G/5G networks across its many sites with consistent quality and performance.

Learning 4: Digitalization is becoming a prerequisite to achieving sustainability objectives

We recently noted a trend of companies deploying digital twins to help realize their sustainability goals. But it is not simply digital twins assisting companies on this front—digitalization projects overall are helping companies monitor energy consumption, optimize resource usage, and reduce their environmental footprint in the manufacturing process.
Backing this awareness and trend toward sustainability are data points from our latest What CEOs Talked About report, where “sustainability” and related terms remained among the most discussed topics in boardrooms.

Selected highlight: TotalEnergies

French multinational energy and petroleum company TotalEnergies has publicly declared its ambition to achieve carbon neutrality by 2050. To meet this goal, the energy company has leveraged digital solutions to advance the implementation of sustainability measures on its offshore platforms.

For instance, TotalEnergies retrofitted their pipes with LoRaWAN-connected temperature sensors to detect gas leaks along their flare networks. As hydrocarbons are released, the temperature of the pipes significantly changes. When this change is detected, operators are alerted via emails for immediate action. This not only helps limit the release of hydrocarbons but also saves TotalEnergies money by reducing the loss of product.

Learning 5: The journey toward predictive maintenance and remote monitoring continues

According to our Predictive Maintenance and Asset Performance Market Report 2023–2028 (published in November 2023), the predictive maintenance market reached $5.5 billion in 2022. While the report notes several tailwinds supporting this interest and market growth, such as skill shortages and interest in reducing energy usage and CO2 emissions, costs are a major driver, as noted in our case studies report as well.

Equipment failure, especially during core operational hours, reduces productivity and adds repair expenses. To avoid these costs, companies often use preventative maintenance procedures, such as time-based inspections and repairs or condition criteria from sensors or physical measurements to trigger preventative intervention. However, intervening based on time can be inefficient since the equipment may not be in need of repair at that time, and data collection/monitoring requires personnel to conduct these tasks.

By implementing digital solutions, companies can remotely monitor the condition of critical equipment and establish conditions in which intervention is actually needed well before failure occurs.

Selected highlight: Battalion Oil Corp

US-based Battalion Oil Corp partnered with Novity, a US-based predictive maintenance solutions company, to pilot a predictive maintenance solution to detect valve leaks within their compressors and reduce unexpected compressor downtime. Initially, Battalion would sporadically measure valve cap temperatures using handheld devices to identify potential gradual leaks that could lead to a failure. While the checks were intended to be conducted daily, varying daily maintenance tasks and priorities often disrupted these important checks.

“Predictive automation is a game-changer for the oil and gas industry. By analyzing data in real-time and making accurate predictions about future events, drilling companies can optimize their operations to maximize efficiency, reduce costs, and improve safety. This technology has the potential to transform the way we do business and stay competitive in today’s market.” – John Smith, CEO of Oil and Gas Exploration Company

An initial step in the solution was to use a crank angle sensor and pressure transducers. However, physical crank angle sensors are usually the most difficult and expensive sensors to install, so the engineers developed a virtual crank angle sensor based on physics-based and data-driven methods using data from the pressure sensors.

After validating that the rotational position calculated by the virtual sensors matched the position provided by the physical sensors, engineers applied prognostic methods to the data from the virtual crank angle sensor and physical pressure sensors. The result was predicted gradual valve failures several weeks in advance—five to seven days on average before temperature checks indicated a gradual leak.

The digital transformation journey carries on

The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves further into the above-mentioned and 18 other case studies of ongoing digital transformation projections. While these companies and many others are advancing in their digital transformation journeys, there is still a long road ahead for many companies, some of which still rely on analog, pen-and-paper methods in their facilities. Even still, many companies are already experiencing real value, e.g., Mercedes’ achieving 25% greater efficiency and Battalion observing signs of gradual valve failures several weeks in advance.

Digitalization has become more than a nice-to-have for manufacturers today—it has become crucial for them in their respective competitive landscapes. The market reflects this assessment: according to our enterprise IoT market dashboard, the IIoT market size in 2023 is approximately $145 billion, with a forecasted CAGR of 17.9% between 2023 and 2030.

Looking ahead, AI continues to become a major theme in companies’ digital transformation initiatives. According to our continual series What CEOs Talked About, the topic and its related terms have already been of high and growing interest in boardrooms throughout 2023. We see a plethora of generative AI projects across the board, even in the industrial space (which we will report on soon). We will continue to monitor this space and highlight interesting case studies from adopters.

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IoT Business News: Can you explain how the transition from ‘scan it’ to ‘sense it’ technologies will impact global retailers and supply chains? How will these changes impact end-users/ consumers?

Steve Statler: For global retailers and their employees, the transition from ‘scan it’ to ‘sense it’ will save precious resources. The traditional ‘scanning’ method retailers have used is implementing handheld scanners. This takes up employee time, a scarce resource that could be better spent elsewhere. Additionally, manual scanning is inherently inaccurate given its reliance on workers – if workers are not performing perfectly, which is typically the case in the real world, these results break down – and upon fixed infrastructure, which limits information to one moment or location. By using ambient IoT, radios don’t require expensive people or expensive readers. Ambient sensors are already pervasive and visibility is comprehensive across a number of buildings and delivery vehicles, meaning it has a low barrier of entry for global retailers.

What role will the ambient IoT play in this transition?

For the transition to a ‘sense it’ culture, the ambient IoT is essential. The technology builds on the learnings and automations used in 1st generation UHF RFID, adding cloud intelligence and utilizing commodity radios that are already pervasive. The pervasive nature of the UHF RFID, in combination with the ever emerging standards, will allow for a smooth transition.

How can businesses successfully transition their business from manual inventory scanning to sensor technology?

For businesses to successfully transition their business, it will require adoption of battery free Bluetooth tags, readers, and cloud applications that are designed and built for a serialized, digital product passport. A benefit of the transition is that there is hardly any transition labor for employees, as they do not need to be retrained for scanning.

How does ambient IoT improve upon traditional methods of supply chain management?

Ambient IoT improves upon traditional methods of supply chain management in a number of ways. The technology enables a transition to real-time visibility that is end to end across all spaces, including items in transit. This alone is an improvement given that traditional ‘scanning’ methods of supply chain management lack the opportunity to provide real time information. Ambient IoT allows for much higher accuracy. Simultaneously, ambient IoT allows for sensing of additional conditions, such as humidity levels.

What are the larger implications for business owners when implementing ambient IoT?

For business owners, improvements in visibility from ambient IoT allow for optimized business models. The technology allows for lower labor costs, given the lack of employees scanning and less capital tied up in inventory. Additionally, the technology allows for better intelligence and insights, meaning less out-of-stocks, better service levels, and higher sales. Finally, the product can allow for smaller store footprints, as it limits the necessity to order high stock of items.

In addition to these labor and inventory benefits, businesses that have already implemented the technology can capitalize upon the opportunity to transition to product as a service as well. Finally, for all companies, the tech allows for a better ability to comply with upcoming regulations that require visibility across supply chains for customers.

Are there any other use cases for ambient IoT within supply chain management?

As we transition from supply chains to demand chains, the ambient IoT will show, in real time, demand signals from stores or even homes. As sustainability becomes top of mind for many companies, the tech allows for circular product usage, meaning businesses can reuse and resell products in the aftermarket. With better tracking and a source of authentication and provenance for resellers, resellers can sell second hand products for higher costs with proof of validity, and producers can participate in the revenue from resale.

The post The role of Ambient IoT in the transition to a ‘Sense it’ culture – Interview with Steve Statler (Wiliot) appeared first on IoT Business News.

Since the founding of our company, SAM has welcomed efforts by government agencies and regulators worldwide to raise consumer awareness about cybersecurity in the IoT space. These efforts benefit both consumers and the network operators connecting them to the digital world. Consumers benefit by being better informed about an IoT product’s security attributes at the “point of sale” and operators benefit as this increased awareness amongst consumers will make it easier to develop and sell new network-based security services.

The latest development comes from the United States, where the White House has introduced the “Cyber Trust Mark” program. This program aims to certify IoT devices bearing the label, ensuring they meet essential security attributes safeguarding consumers’ networks and device data. While voluntary, this initiative, led by the Federal Communications Commission, is set to begin implementation in 2024. This is part of an initiative that includes a collaboration between the White House and the National Institute of Standards and Technology (NIST) to establish cybersecurity standards tailored to routers.

These moves will have a positive impact on the IoT ecosystem on a variety of levels. Yet, while product labels will increase consumer awareness and education, they cannot address the ongoing evolution and fragmentation of IoT devices. Thousands seemingly hit the market each year, making “constant” security unattainable. Even a seemingly secure device could falter over time without proper software updates, which in reality, the average consumer does not do.

This fact is part of a trend that has led to a situation where most home and small business devices and networks lack adequate protection. This vulnerability arises due to various reasons, including the widespread use of consumer electronics devices that have become connected IoT devices through home routers. While some vulnerabilities may only be an inconvenience for some users, other can open the door to malicious activities. One of the most pressing challenges in the realm of IoT is the sluggish discovery-to-patching process by firmware vendors, leaving users exposed indefinitely. This issue highlights a critical gap in home security, where the timely resolution of IoT vulnerabilities should be a requirement, not a “luxury.”

However, for consumer electronics in general, it takes time to create a fix, to test it in the field and then to distribute it. And for IoT devices, it’s a different matter altogether, as numerous devices have minimal security and no ongoing security patch program. Or the devices are no longer on the market at all. This condition creates a significant window of opportunity for hackers who are well aware of these vulnerabilities and often have ample time to exploit them before the vendors issue a remedy, leaving end users vulnerable to attacks. Even when the patch is ready for deployment, there is still the question of how it will be deployed onto the users’ devices. Some devices can be updated via the corresponding app on the smartphone. Others, however, need to be updated manually – a lengthy and quite complicated process for even those who are tech savvy.

Katherine Gronberg, Head of Government Services at NightDragon, who works frequently with NIST and the White House on matters relating to IoT security, has commented: “With the explosion of IoT devices available from a wide variety source, consumers have until now not had any help in deciding what to buy or even to be mindful of security. The Cyber Trust Mark will allow consumers to identify products that have been designed and manufactured according to secure development guidelines and that offer some basic security features, most of which will likely not require any actions by the device user. While this program doesn’t apply to IoT devices that are already in use today, it will create a more informed customer and may make other parties in the ecosystem such as retailers or ISPs more conscious of the problem and might motivate them to take action.”

One action that the industry has seen recently is a renewed focus on routers, as seen in a recent security advisory issued by the US NSA, in which one of its recommendations was for consumers to exchange ISP-issued routers for ones they would purchase themselves. And there is another router-focused technique that more and more ISPs are using to help their customers with IoT network security, namely the “hot patching” measure, which uses a router-based software agent to provide protection for the router itself and every device connected to it.

Hot patching is designed as a “one stop” protection program in which an ISP would download an agent to a router to provide constant real-time monitoring and alerts. Hot patching is based on what is known as “deep packet inspection,” or DPI, which is a well-known and long-standing technique wherein the payload of packets traversing a data network is inspected and analyzed. The result empowers consumers with comprehensive router and device security, eliminating vulnerability monitoring and patching complexities.

While security labeling undoubtedly enhances consumer awareness and overall IoT security, the quest for constant security calls for a gateway-based solution. Such a solution can act as the ultimate backstop to industry and government initiatives, securing IoT devices and the connecting network.

Therefore, we believe the “Cyber Trust Mark” program will certainly be a great benefit for the consumer or “end user” and the increased awareness about IoT security it will raise gives ISPs an excellent opportunity to play a more proactive role that will be welcomed by their customers and which will increase IoT network security in meaningful ways.

The post Nurturing IoT’s Safety Net: Can the ‘Cyber Trust Mark’ Weather the Fragmented Storm? appeared first on IoT Business News.

In the ever-evolving landscape of technology, the Internet of Things (IoT) and Digital Twin technology stand out as pivotal innovations. IoT, with its network of interconnected devices, and Digital Twins, which are virtual replicas of physical systems, are revolutionizing industries from manufacturing to urban planning.

This article presents a comprehensive exploration of IoT and Digital Twin technology, detailing their individual characteristics, the benefits of their integration, and the challenges faced. It offers insights into current trends and future directions, providing a well-rounded perspective on these transformative technologies.

The Convergence of IoT and Digital Twin Technology

IoT’s network of sensors and devices captures real-time data from the physical world. This data is crucial for creating Digital Twins, which are dynamic virtual models of physical objects or systems. By integrating IoT data, Digital Twins can simulate real-world conditions, predict outcomes, and optimize processes.

Understanding IoT

IoT involves a network of physical objects (‘things’) embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet. These devices range from ordinary household items to sophisticated industrial tools.

1. IoT in Industry: In industrial settings, IoT devices monitor and optimize manufacturing processes, improve supply chain logistics, and enhance product lifecycle management.

2. Consumer IoT: In the consumer segment, IoT encompasses smart home devices, wearable health monitors, and connected vehicles.

Digital Twin Technology Explained

A Digital Twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. It is used for simulation, analysis, and control.

1. Application in Manufacturing: In manufacturing, Digital Twins are used to create and test prototypes, predict equipment failure, and optimize production lines.

2. Urban Planning and Infrastructure: Digital Twins simulate entire cities or infrastructure systems, aiding in urban planning and management.

The Synergy of IoT and Digital Twins

1. Real-Time Data and Analysis: IoT feeds real-time data into Digital Twins, allowing for accurate simulations and analyses. This integration helps in predictive maintenance, reducing downtime in industrial settings.

2. Enhanced Decision-Making: By leveraging IoT data, Digital Twins enable better decision-making. They provide insights into system performance, potential failures, and maintenance needs.

3. Customization and Innovation: The combination allows for customization of products and services, driving innovation in various sectors like healthcare, automotive, and aerospace.

Benefits of IoT and Digital Twin Integration

1. Operational Efficiency: This integration enhances operational efficiency by enabling real-time monitoring and predictive maintenance.

2. Cost Reduction: It reduces costs by identifying potential issues before they become critical, minimizing downtime.

3. Sustainability: It promotes sustainability by optimizing resource usage and reducing waste.

Challenges and Solutions

1. Data Security and Privacy: The vast amount of data generated poses security and privacy risks. Implementing robust cybersecurity measures is essential.

2. Interoperability and Standardization: Ensuring interoperability among diverse IoT devices and systems is challenging. Developing universal standards and protocols is key.

3. Complexity and Scalability: Managing the complexity and ensuring the scalability of these systems require continuous innovation and investment.

Future Prospects and Emerging Trends

1. Advancements in AI and Machine Learning: Integrating AI with IoT and Digital Twins will enable more advanced predictive analytics and automation.

2. 5G Integration: The rollout of 5G will enhance IoT capabilities, offering faster, more reliable connections for Digital Twins.

3. Sustainable Development: There’s an increasing focus on using IoT and Digital Twins for sustainable development, particularly in smart city projects and environmental monitoring.

Conclusion

The integration of IoT and Digital Twin technology is a formidable force driving innovation and efficiency across multiple industries. While challenges exist, the potential benefits are immense. As these technologies continue to evolve, their combined impact on industry, urban development, and sustainability will be profound, marking a new era of digital transformation and innovation.

The post IoT and Digital Twin Technology: Shaping the Future of Industry and Innovation appeared first on IoT Business News.

The advent of the Internet of Things (IoT) has revolutionized how we interact with technology, integrating the physical and digital worlds in unprecedented ways. However, the proliferation of IoT devices also brings challenges, particularly in managing their lifecycle effectively. IoT Device Lifecycle Management (DLM) is crucial for the efficient and secure operation of these devices. This article delves into the various stages of IoT DLM, the challenges involved, and the strategies for effective management.

Understanding IoT Device Lifecycle Management

IoT DLM refers to the processes and methodologies used to manage an IoT device from its inception to retirement. It encompasses the entire spectrum of an IoT device’s existence, including design, development, deployment, operation, maintenance, and eventual decommissioning. Effective DLM is vital for ensuring device security, functionality, and overall network health.

1. Design and Development Stage

The lifecycle of an IoT device begins with its design and development. This stage involves defining the device’s purpose, functionality, and the environment in which it will operate. Key considerations include:

• Security by Design: Implementing security measures at the design phase is critical. This includes hardware and software security features, data encryption, and secure communication protocols.
• Scalability and Compatibility: The device should be designed to be scalable and compatible with different platforms and technologies.
• Energy Efficiency: Many IoT devices are deployed in locations where power sources are limited, making energy efficiency a crucial design factor.

2. Manufacturing and Deployment

Once designed and developed, the next stage involves manufacturing and deploying these devices. This phase must ensure that the devices are built according to the specified design and that they are deployed correctly in their intended environment.

• Quality Assurance: Rigorous testing for quality and compliance with standards is essential.
• Deployment Strategy: The deployment should be planned to minimize disruptions and ensure seamless integration with existing systems.

3. Operation and Maintenance

This is the longest phase in the lifecycle of an IoT device. Key activities include:

• Monitoring and Management: Continuous monitoring for performance and security is vital. IoT devices generate vast amounts of data, and their performance must be consistently managed.
• Software Updates and Patch Management: Regular software updates and patches are essential to address security vulnerabilities and enhance functionality.
• Remote Troubleshooting and Support: Given the often remote and distributed nature of IoT devices, remote troubleshooting capabilities are crucial.

4. Data Management and Analysis

IoT devices collect and transmit data, necessitating effective data management strategies.

• Data Storage and Analysis: Efficient storage solutions and advanced analytics capabilities are required to derive meaningful insights from the data collected.
• Data Privacy and Compliance: Adhering to data protection regulations and ensuring user privacy is paramount.

5. Decommissioning and End-of-Life Management

Eventually, IoT devices reach their end of life, whether due to technological obsolescence, wear and tear, or other factors. This stage involves:

• Safe Decommissioning: Ensuring that devices are decommissioned safely, without posing risks to the environment or data security.
• Data Sanitization: Properly erasing stored data to prevent unauthorized access or data breaches.
• Recycling and Disposal: Adhering to environmental standards for recycling and disposing of electronic waste.

Challenges in IoT Device Lifecycle Management

Managing the lifecycle of IoT devices presents various challenges:

• Scalability: As IoT networks grow, managing an increasing number of devices becomes complex.
• Diverse Device Ecosystem: IoT encompasses a wide range of devices with different functionalities, requiring diverse management strategies.
• Security Risks: IoT devices are often targeted by cyberattacks, making security a continuous concern.
• Regulatory Compliance: Compliance with various regional and industry-specific regulations can be challenging.

Strategies for Effective IoT Device Lifecycle Management

To address these challenges, several strategies can be employed:

• Implementing Standardized Protocols: Adopting industry-standard protocols can help in managing devices efficiently.
• Automated Tools and Platforms: Leveraging automation for device management can significantly reduce the complexity and improve efficiency.
• Regular Security Audits: Conducting regular security audits and assessments can help in identifying and mitigating risks.
• Training and Awareness: Ensuring that staff are trained and aware of best practices in IoT DLM is crucial for its success.

The Future of IoT Device Lifecycle Management

Looking ahead, IoT DLM is set to become more complex and crucial. The integration of AI and machine learning can provide more intelligent and automated management solutions. The adoption of edge computing can also enhance the efficiency of IoT operations. Moreover, as IoT continues to evolve, there will be a greater emphasis on sustainable practices in device lifecycle management, focusing on minimizing environmental impact.

Conclusion

Effective IoT Device Lifecycle Management is critical in ensuring the efficient, secure, and sustainable operation of IoT devices. As IoT continues to grow and permeate various sectors, the challenges in managing these devices will

also increase. However, with the right strategies, tools, and awareness, these challenges can be addressed, leading to more robust, efficient, and secure IoT ecosystems. The future of IoT DLM lies in its ability to adapt to evolving technologies and requirements, ensuring that IoT devices continue to be a driving force in the connected world.

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