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Device Management Presents Barrier to IoT at Scale

Pete Goldin
APMdigest

Management of thousands or millions of Internet connected devices is posing a major obstacle to the success of the Internet of Things (IoT), according to DevicePilot (previously 1248).

These concerns are reflected in DevicePilot's survey of 50 companies planning to deploy IoT applications at scale across different industry sectors including environmental and industrial monitoring, elderly care/wellness, smart homes and cities, energy management, refrigeration, retail and public services. The survey ranked "risk to growth" as the most worrying consequence of failing to manage devices, followed by "risk to revenue" and "risk to brand".

This may be one of the reasons why some of the ambitious predictions for IoT devices have not yet been borne out, according to DevicePilot. While 12% of respondents had deployed a million or more devices in the field, 82% had deployed only 1,000 devices or less. However, respondents to the Device Management Survey expect these numbers to grow, with 70% of companies predicting an eventual market size of at least millions of devices and 20% predicting that they will reach the billions level.

“It is clear that most IoT companies are currently managing their connected products manually or by a mixture of manual and automatic processes,” said Pilgrim Beart, CEO at DevicePilot. “But as projects move from pilot to deployment at scale, the time and operational cost of manually logging-in to each device to perform an upgrade or check if it is working becomes a major barrier. Automatic asset management, monitoring and lifetime support are essential for the long term success of the IoT.”

Summary of key survey findings:

■ 61% of companies anticipate 10x growth over the coming year

■ 70% estimate their addressable market to be in the millions of devices - and 9% in the billions

■ The most common business model is a combination of up-front fee plus ongoing service fee

■ Only 18% of companies describe their device management as “highly automated and slick”

■ The biggest perceived risk of not managing devices well is risk to the growth of the company

■ 86% of companies say that as far as managing devices is concerned, they’re either already in trouble, or expect to be within 12 months

Cees Links, veteran of the world of connected devices and currently CEO of GreenPeak Technologies commented, "It sometimes surprises me how many device companies don't even know how many of their devices have been deployed, let alone how many are working. As the IoT matures, users' expectations of service quality are rapidly increasing, and you really have to keep on top of this stuff. When it comes to the smart home we expect all devices to be connected and providing useful information for owners and manufacturers on usage, diagnostics, need for refurbishment and replacement."

"The answer to device management is automation,” added Chris Wright, CTO of Moixa, a business deploying a solar energy storage product. “We need to be connected for multiple reasons including remote management, demand response and performance reporting; and if the product isn’t working or has lost connection, then we can’t bill.”

Pete Goldin is Editor and Publisher of APMdigest

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Device Management Presents Barrier to IoT at Scale

Pete Goldin
APMdigest

Management of thousands or millions of Internet connected devices is posing a major obstacle to the success of the Internet of Things (IoT), according to DevicePilot (previously 1248).

These concerns are reflected in DevicePilot's survey of 50 companies planning to deploy IoT applications at scale across different industry sectors including environmental and industrial monitoring, elderly care/wellness, smart homes and cities, energy management, refrigeration, retail and public services. The survey ranked "risk to growth" as the most worrying consequence of failing to manage devices, followed by "risk to revenue" and "risk to brand".

This may be one of the reasons why some of the ambitious predictions for IoT devices have not yet been borne out, according to DevicePilot. While 12% of respondents had deployed a million or more devices in the field, 82% had deployed only 1,000 devices or less. However, respondents to the Device Management Survey expect these numbers to grow, with 70% of companies predicting an eventual market size of at least millions of devices and 20% predicting that they will reach the billions level.

“It is clear that most IoT companies are currently managing their connected products manually or by a mixture of manual and automatic processes,” said Pilgrim Beart, CEO at DevicePilot. “But as projects move from pilot to deployment at scale, the time and operational cost of manually logging-in to each device to perform an upgrade or check if it is working becomes a major barrier. Automatic asset management, monitoring and lifetime support are essential for the long term success of the IoT.”

Summary of key survey findings:

■ 61% of companies anticipate 10x growth over the coming year

■ 70% estimate their addressable market to be in the millions of devices - and 9% in the billions

■ The most common business model is a combination of up-front fee plus ongoing service fee

■ Only 18% of companies describe their device management as “highly automated and slick”

■ The biggest perceived risk of not managing devices well is risk to the growth of the company

■ 86% of companies say that as far as managing devices is concerned, they’re either already in trouble, or expect to be within 12 months

Cees Links, veteran of the world of connected devices and currently CEO of GreenPeak Technologies commented, "It sometimes surprises me how many device companies don't even know how many of their devices have been deployed, let alone how many are working. As the IoT matures, users' expectations of service quality are rapidly increasing, and you really have to keep on top of this stuff. When it comes to the smart home we expect all devices to be connected and providing useful information for owners and manufacturers on usage, diagnostics, need for refurbishment and replacement."

"The answer to device management is automation,” added Chris Wright, CTO of Moixa, a business deploying a solar energy storage product. “We need to be connected for multiple reasons including remote management, demand response and performance reporting; and if the product isn’t working or has lost connection, then we can’t bill.”

Pete Goldin is Editor and Publisher of APMdigest

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The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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