IoT Analytics report: State of Enterprise 2026 Signals BIG Shift in utility
IoT Analytics State of Enterprise IoT 2026: From Connected Systems to Autonomous Operations
Synopsis- IoT Analytics reports that enterprise IoT has entered its final maturity phase, shifting toward agentic and physical AI-driven operations.
- Hardware, connectivity, and software architectures are evolving to support autonomous, cross-ecosystem decision-making.
- Enterprise value is increasingly defined by intelligence and orchestration rather than connectivity alone.
IoT Analytics says the enterprise Internet of Things market is entering what it describes as the agentic and AI phase of IoT maturity, as companies move beyond connectivity toward autonomous, cross-ecosystem-optimised operations. The firm’s 124-page State of Enterprise IoT 2026 report frames this transition as the final stage of an eight-step IoT value–maturity curve that tracks the evolution from isolated “dumb” devices to fully optimised, intelligent systems operating across organisational and industry boundaries.
According to the report, the enterprise IoT market grew 13% year-over-year in 2025 to reach $324 billion, with growth projected at 14% in 2026. This expansion is attributed to rising adoption of AI technologies and continued momentum in large-scale markets such as India and China. The installed base of connected IoT devices also expanded by 13% to 21.1 billion by the end of 2025, with enterprise use cases accounting for 45% of all connections.
While the scale of IoT continues to increase, IoT Analytics notes that executive attention has shifted elsewhere. Ongoing analysis of corporate earnings calls shows that IoT is discussed less frequently at the executive level, even as industrial AI and related themes have climbed higher on the CEO digital agenda. In the report, ABB leadership is cited as reflecting on this evolution.
“We talked 10 years ago, it was all about IoT, the Internet of Things. Then we talked about digitalization. Today, it’s all about AI,”
said Morten Wierod, Chief Executive Officer of ABB.
This shift aligns with the IoT value–maturity curve first published by IoT Analytics CEO Knud Lasse Lueth in 2015. The framework outlines eight stages of IoT development, culminating in cross-ecosystem-optimised systems. Based on findings in the State of Enterprise IoT 2026 report, IoT Analytics argues that many enterprises are now operating in the final stages of this curve, where connectivity is assumed and the focus moves to autonomous operations enabled by industrial and physical AI.
Lueth notes that while IoT has become a baseline capability for many organisations, truly intelligent devices remain rare. As of December 2025, IoT Analytics estimates that fewer than 1% of the 21.1 billion IoT connections included a true edge AI component, defined as dedicated AI accelerators such as GPUs or NPUs. However, the report characterises this as a rapidly changing baseline as the next wave of adoption accelerates.
From Pre-IoT Systems to Scaled Enterprise Connectivity
The report revisits the historical evolution of enterprise IoT to explain how the market arrived at its current inflection point.
In the pre-IoT era of the 1990s through the late 2000s, industrial systems were largely closed, on-premises environments with limited external connectivity. Devices relied on local serial or fieldbus protocols, and security was primarily achieved through physical and network isolation. IoT gained broader attention only in the early 2010s, when the Chinese government identified the Internet of Things as a strategic priority in its 12th Five-Year Plan and when Gartner highlighted IoT as an emerging technology phenomenon.
Between 2011 and 2015, maturing connectivity technologies and declining costs enabled enterprises to scale industrial connections with more predictable returns. During this period, key standards were established: MQTT 3.1.1 became an OASIS Standard in 2014, and the IETF published the Constrained Application Protocol as RFC 7252. At the network level, ultra-narrowband LPWAN technologies such as Sigfox began national rollouts in Europe, while the LoRa Alliance released the LoRaWAN 1.0 specification in early 2015.
This connectivity wave brought large numbers of industrial assets online. U.S. electric utilities operated approximately 64.7 million advanced metering infrastructure smart meters by 2015. Commercial fleets in North America deployed about 4.7 million active telematics systems by the end of 2014. Equipment manufacturers also began embedding remote monitoring into machinery, such as Atlas Copco’s SMARTLINK system for compressed-air equipment.
Platforms, Scale, and the Rise of AI Interfaces
From 2016 to 2020, the focus shifted from connectivity to platforms. Cloud infrastructure became the default environment for storing and processing industrial data, and hundreds of IoT platforms competed to offer end-to-end application enablement. While platforms such as ThingWorx had launched earlier, this period saw rapid proliferation, including offerings like Cumulocity IoT.
By 2019, IoT Analytics counted more than 620 IoT platforms, many of them cloud-centric, with AWS IoT Core and Azure IoT Hub emerging as leading options. As the market matured, however, buyers increasingly questioned the return on investment of generic horizontal platforms, leading to consolidation and market exits.
The subsequent phase, from 2021 to 2025, focused on enterprise-scale usability and AI interfaces. Several countries reached full smart electricity meter adoption, including Canada, China, Saudi Arabia, and France. Large industrial players crossed significant milestones: General Motors surpassed 16 million connected vehicles in 2021, Toyota exceeded 10 million, Caterpillar passed 1.5 million connected assets in 2024, and Hapag-Lloyd connected more than one million dry containers.
During this period, AI—particularly generative AI—emerged as a dominant interface layer. Following the public release of OpenAI’s ChatGPT in late 2022, vendors began embedding AI capabilities into industrial HMIs, IoT platforms, and engineering tools. At Hannover Messe 2025, for example, KPMG and SoftServe demonstrated a generative AI industrial assistant that monitored CNC machine performance and displayed real-time overall equipment effectiveness and availability.
The Agentic and Physical AI Wave
IoT Analytics argues the market is now entering the agentic and physical AI wave, where intelligence increasingly moves from the cloud to the edge. Autonomous operations require real-time decision-making that cloud architectures often cannot support due to latency and bandwidth constraints. As a result, chipmakers are embedding AI accelerators directly into microcontrollers to enable on-device intelligence.
The report highlights Qualcomm’s acquisitions of Foundries.io, Edge Impulse, and Arduino as part of a broader strategy to build an edge AI development ecosystem. At the same time, connectivity continues to evolve quietly. Technologies such as 5G RedCap and LTE Cat-1 bis are positioned as pragmatic options for mid-speed industrial applications, while satellite connectivity is being integrated into cellular modules to ensure always-on links in remote environments.
On the software side, industrial systems are transitioning from passive AI assistants to active agents capable of orchestrating workflows and triggering physical actions. IoT Analytics cites Microsoft’s framing of this evolution from assistants to collaborators and orchestrators, Hitachi’s use of agents to monitor and maintain 30,000 industrial assets, and Siemens’ commitment of more than €1 billion to build a unified data fabric under its ONE Tech Company strategy.
Despite this momentum, the report cautions that most agentic AI deployments remain early-stage. Observations from major industrial trade fairs suggest it will take several more years before autonomous operations become mainstream across industries.
Security and the New Operational Risk
As AI systems move from recommending actions to executing them, IoT Analytics warns that security principles must evolve. Autonomous systems require guardrails that limit agent behaviour, action-level permissions beyond traditional user access controls, and end-to-end traceability so automated decisions can be explained, verified, and reversed if necessary.
The report concludes that while connectivity laid the foundation for enterprise IoT, intelligence and orchestration will define its future value.
Source: IoT Analytics – Have a Story? Address it to the Editor and submit it here
About IoT Analytics
IoT Analytics is a Germany-based provider of market insights and strategic intelligence focused on the Internet of Things, artificial intelligence, cloud computing, edge technologies, and Industry 4.0. Founded in 2014, the firm produces research covering IoT applications, platforms and software, connectivity and hardware, and industrial IoT. IoT Analytics works with more than 1,000 corporate partners worldwide, including leading software, telecommunications, consulting, semiconductor, and industrial companies. Its research portfolio includes market reports, forecasts, and analyst commentary designed to support enterprise decision-making. The company publishes a research blog and newsletter to track developments shaping IoT and adjacent technology markets. Founder and CEO Knud Lasse Lueth has authored or co-authored more than 100 reports and positions the firm’s work around delivering data-driven, independent analysis for global technology and industrial stakeholders.
Featured image Source: IoT World Today
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