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Industrial data: from visibility to business value

For more than a decade, industrial digital transformation centered on visibility. Manufacturers invested heavily in connected assets, historians, dashboards, sensors, and IIoT platforms to gain greater operational insight.

Today, visibility is no longer the primary bottleneck.

Walk any major industrial event, from Hannover Messe to Smart Manufacturing Week, and you will see it: nobody’s pitching “we’ll help you see your data” anymore. Most industrial organizations I talk to aren’t short on data. If anything, they are drowning in it. The real challenge has moved from collecting data to actually getting value out of it.

The challenge has shifted from collecting data to converting data into decisions, and decisions into measurable business outcomes. That shift is where AI and industrial operations begin to intersect in genuinely transformative ways.

The market’s biggest bet: industrial context layers

As AI matures, industrial systems are shifting from reporting what happened, to recommending what should happen next, to (increasingly) executing approved actions on their own. We are witnessing a gradual progression from automation toward autonomy.

My view, and one I continue to test in conversations across the market, is that much of the industry’s AI discussion remains overly focused on models themselves. Foundation models are becoming commoditized fast. What’s actually scarce is industrial context, the thing that lets those models do something useful.

An AI model can detect an anomaly, generate a recommendation, optimize a schedule. Fine. But without understanding asset relationships, maintenance history, engineering constraints, production targets, safety requirements, business objectives, that recommendation isn’t worth much on the shop floor. I saw this firsthand during a recent visit to Schneider Electric’s Le Vaudreuil plant in Normandy: the AI itself wasn’t the impressive part; it was the decades of engineering knowledge, process understanding, and operational context underpinning the system. Context is what turns raw data into intelligence, and intelligence into action.

Why AI context requires ongoing maintenance, not just setup

Context also needs upkeep. Let me share an instance I recently came across: a manufacturer brought in a consulting team to build an AI model that controlled fan sequencing inside a set of curing ovens, fixing a temperature gradient that had been causing product defects for years. It worked, and the consultants moved on. About three months later, the problem came back, worse than before. The model had drifted, and nobody was monitoring it closely enough to catch it.

The actual cause turned out to be almost trivial: in the summer, operators would prop open a back door to cool the plant, and the draft created a cold spot the model was never designed to account for. The people on the floor had always known to compensate for this manually, but once the AI model took over, that tacit knowledge quietly stopped being used, and nobody thought to feed it back in.

It’s a reminder that context isn’t a one-time input, it has to be actively maintained, or a model will keep confidently executing on a picture of the world that’s gone stale. Treat AI models like any quality system: audit them on a regular cadence, rerun the same inputs, and catch drift before it becomes a failure.

How industrial vendors are building AI context layers

The more conversations I have across the industrial software market, the more it feels like vendors are arriving at a similar conclusion. Whether through acquisitions, digital twin initiatives, semantic models, knowledge graphs, or industrial data fabrics, vendors are racing to build operational knowledge layers that sit beneath AI capabilities.

Schneider’s acquisition of Cognite. Siemens pursuing its vision through Intelligence Center X, which surfaced repeatedly in conversations at Realize LIVE as part of Siemens’ effort to connect industrial data, engineering knowledge, and AI into a unified operational intelligence layer. Bosch’s Manufacturing Co-Intelligence push, which Norbert Jung was framing to me in Berlin as a “third layer of intelligence” sitting above the shop floor. Autodesk buying MaintainX. Velotic forming. Different roads, same destination: everyone’s racing to build the contextualized data foundation, the semantic layer, the knowledge model, the digital twin underneath the AI.

The emerging battle is no longer simply about delivering AI capabilities. It is about owning the context layer that makes those capabilities useful.

AI assistants vs. AI agents in industrial software

One data point which is relevant here: AI assistants are already everywhere in enterprise software, IDC estimates over 60% of enterprise apps now have some assistant or advisory capability baked in, while roughly 20% are pushing further into actual agent territory, systems that can independently perceive, evaluate, and act. That difference is not small. Assistants help people do the work. Agents start doing the work themselves.

And that has real implications for how enterprise software gets built. For decades, applications were designed around humans: log in, navigate a workflow, move data from one system to another. In an agentic world, that whole model starts to break down.

Why orchestration, not automation, is the real disruption

Instead of opening five dashboards, a user might just say: “optimize tomorrow’s production schedule while minimizing energy costs and avoiding maintenance conflicts.” An orchestration layer then pulls together specialized agents across scheduling, asset management, supply chain, and energy to actually get it done. It’s roughly the vision I kept hearing sketched out on the show floor at Smart Manufacturing Week and other industry events, just from different vendors, each convinced they’d own the orchestration layer.

So no, I don’t think the disruption here is automation. Industry has automated processes for decades already. The disruption is orchestration. Agents are becoming the connective tissue between applications, processes, and outcomes.

That doesn’t mean SaaS is going away, not even close. ERP, MES, EAM, APM, historians, supply chain systems, these stay critical because they are what agents read from and act through. But the value is migrating upward, away from the application itself and toward the orchestration and context layers sitting above it.

This is basically why I don’t buy the “SaaSpocalypse” narrative that’s been making the rounds. What’s actually happening looks less like the death of software and more like software evolving from a system of record into a system of intelligence.

For industrial software vendors, that raises an uncomfortable strategic question: if everyone can plug into the same foundation models, where’s the differentiation going to come from?

My take: Domain intelligence. Process models. Engineering expertise. Decades of operational history. Digital twins. Openness and interoperability. Contextualized industrial knowledge that’s been built up over years, not scraped off the internet.

Key questions for the future of industrial AI

The last decade was about digitizing operations. The next one is likely going to be about operationalizing intelligence. The data already exists. The models are becoming available to everyone. Context is emerging as the industry’s most defensible and strategically valuable asset.

Which leaves me with a few open questions I’d genuinely love to hear other views on:

  • Will the next generation of industrial platform leaders be the ones with the best AI, or the ones with the richest operational context?
  • Does the real control point end up living inside the application, or in the orchestration layer above it?
  • As agents become the primary “users” of software, how should vendors be rethinking product design and pricing?
  • Will industrial organizations trust a single vendor’s context layer, or will neutral data foundations end up winning out?
  • And perhaps the biggest one: as we move from automation to autonomy, who actually owns the decision?

Got a question? Drop it in here.

Gunjan Bassi

Gunjan Bassi - Research Manager

Analyst Profile
Gunjan Bassi has more than 14 years' experience working in the logistics and transportation sector. Before joining IDC, she worked with Transport Intelligence (Ti), a transportation and logistics research firm based in Bath, England, where she was responsible for vertical…

The headlines from InfoComm 2026 said the industry moved “from rooms to experiences.” We’ve been observing this transition for a while. But what’s more relevant to anyone who buys, runs, or just sits in meeting rooms is that the room has become another device on the corporate network, and that changes who is in charge of it. 

For years, the meeting room was the thing on the wall you hoped would just work. InfoComm 2026 in Las Vegas, the pro AV industry’s big annual show, made the case that those days are over. The room is no longer a fixed installation. Yes, it is becoming an experience, but it also bears a striking resemblance to something IT knows all too well, a textbook case of AV/IT convergence: a managed device, bought like an endpoint, run like part of a fleet, and increasingly able to collect data about the people inside it. 

The industry has agreed on the theme: outcomes, not spec sheets, now drive what companies spend on collaboration technology. Buyers want rooms that simply work and that prove their value. But what’s worth exploring is how they get there. What is different in how a meeting room is bought, run, and governed? And who ends up operating it? 

Let’s look at some numbers. Attendance at InfoComm 2026 fell about 9% year on year. What rose was the share of buyers in the room. AVIXA (the Audiovisual and Integrated Experience Association, which owns and produces InfoComm) reported that end users made up 37% of attendees, a record, up from 35% last year and 29% in 2024. So fewer people came, but more of them were the ones who sign the purchase order. What they found was a transitioning market: value is continuously moving away from hardware and installation labor toward software, cloud, and AI. 

Three groups of announcements at the show summarize this: one each for how a room is bought, how it is run, and how it is governed. 

Your next room upgrade starts with the chip that runs on-device AI 

For years, a room upgrade meant better cameras, microphones, and displays. InfoComm 2026 reframed it as a decision about processing power. HP introduced its Poly Studio Room Compute, and Cisco showed new endpoints, built on an operating system called RoomOS 26 that it developed with NVIDIA, that run AI agents directly on the device rather than in the cloud, an approach some now call edge AI. 

Cisco leaned hardest on this point, and the argument threaded through the entire show: the features that do real work in a meeting, not just record it, need on-device AI processing, so a room built on incompatible or aging hardware simply cannot run them. In other words, AI in the meeting room is no longer only a software question. It’s about hardware. Perhaps the more uncomfortable part for most buyers is that a lot of the equipment bought in the last few years may not run the features now being sold, which turns a routine refresh into a spending decision that requires a business case. 

Enter the agentic AI 

Think about what happens today when a meeting room misbehaves. Someone files a ticket and waits for a technician. Well, according to InfoComm 2026, you will soon be asking an AI assistant to sort it out. 

Several vendors connected their room systems to AI assistants using the Model Context Protocol, the open standard that companies are adopting to let AI act on their other software. Neat’s version runs on your own network and works with assistants like Claude or Cursor, so the assistant can see what is wrong in a room, change the settings, and resolve common problems without specialist knowledge or a site visit. Others are taking their own routes to the same idea: Shure, the show’s headline partner, is moving beyond hardware with ShureCloud, which manages devices centrally and adds an AI assistant for troubleshooting and support, while Cisco has connected Microsoft’s Copilot to Webex. Related tools now watch over rooms from many brands, and across Teams, Zoom, Google Meet, and Webex, from one place. 

Why should anyone outside AV care? Because this is the same move toward AI agents that is happening across the workplace, only now reaching the meeting room, and the payoff is both money and time saved: fewer call-outs, fewer help-desk tickets. It also pushes the firms that install AV toward ongoing service instead of one-off projects. However, it is still early. Most of this was shown as a capability, not a full rollout, and whether “agentic” lives up to the label is still unproven. 

So the room is now collecting data. But who governs it? 

The moment a room becomes “intelligent”, it starts producing data: who was in it, who spoke, and how the space was used. That makes it interesting to a lot more people. HR, legal, and security now have a stake in it, alongside IT, and each comes at it from a different angle. 

HR sees a room that can log who attended, who spoke, and how much, and wants to reassure employees that this isn’t being used against them and retain their trust. Legal sees the recordings and transcripts as personal data: information that carries consent, retention, and residency obligations, and that can be pulled into discovery if a dispute arises. Security sees a networked device sitting in on confidential conversations and asks the obvious questions, namely who can access that data, where it is stored, and how much bigger a target the room has become. IT, which used to own the room outright, now runs it on behalf of all three. And there is still no security standard written specifically for AI in these systems, and general ones like NIST cover only part of it. 

The industry’s early answer is to keep the processing and the data inside the room rather than sending it to the cloud, which helps with privacy and with rules about where data is allowed to live. Those questions are climbing the data governance priority list for IT and security teams this year, and writing the policy before you roll rooms out widely saves you the harder job of untangling it later. 

The room reads you now. Time to read the room. 

The meeting room is now bought like any other endpoint, run as part of your device fleet, and governed as a data source. That is a long way from the box on the conference-room wall. 

What to do before your next upgrade:  

  1. Ask which AI features run on the hardware you already own, and what you would need to buy for the rest.  
  1. Before you believe any “agentic” pitch, run a small pilot and hold it to a measurable result, such as fewer support tickets.  
  1. And before you scale, decide who owns the data the rooms collect.  

Do that, and the answer to who operates your meeting rooms, AV, IT, or an AI agent, becomes all three, working alongside. The companies that treat the room that way will get the most out of what vendors are now building. 

Navigate your next workplace technology decision with the evidence to back it. Explore IDC’s Intelligent Workplace research, forecasts, and analyst guidance, or speak with our analysts. 

Gala Spasova

Gala Spasova - Senior Research Manager, Europe Smart Office and EMEA Content & Knowledge Management Strategies

Analyst Profile
Gala Spasova is a senior research manager in IDC's Future of Workplace & Imaging team. Her research focus is on Hybrid working, Smart Office technology and Content & Knowledge Management Strategies in EMEA.  Spasova is also part of the European…

IDC recently brought together 20 senior technology executives for an invitation-only dinner with analysts Carla Arend, Andrew Buss, Duncan Brown, and Rahiel Nasir to discuss digital sovereignty in Europe. Here’s what came out of the room.

Sovereignty is real. The conversation around it isn’t.

IDC opened with a provocation: the word “sovereignty” is doing more harm than good. It’s politically loaded, definitionally contested, and vendors have been guilty of “sovereign washing”. Meanwhile, IT departments struggle to translate the concept into something their internal stakeholders actually care about.

What European organisations do care about is entirely concrete: protection against extra-territorial data requests, regulatory compliance, and supply chain resilience. According to IDC research, these are operational risk priorities, not political statements. The vendors making progress in this space have figured out how to speak to that gap. Those still foisting their own definitions of sovereignty on to the market and/or offering nothing more than so-called solutions for data localisation/residency largely haven’t.

The cloud strategy picture is more nuanced than the headlines suggest

Europe is re-assessing its options for cloud and technology providers. Global hyperscalers remain part of the picture, but how they are used is increasingly open to question. IDC’s data points to a clear shift toward layered architectures that combine global scale with local control. A specific model is emerging as the dominant pattern, and the vendors positioned within it are seeing very different conversations than those sitting outside it.

The regulatory picture adds another layer of complexity. NIS2, DORA, the AI Act: each creates compliance obligations that directly shape how organisations think about their technology infrastructure and provider relationships. Navigating that landscape without a clear positioning is increasingly difficult.

Private cloud is not the safe harbour it looks like at first glance

IDC commonly emphasizes that private cloud is the ultimate sovereign cloud, and this remains strongly the case as very few companies wish to exit all their datacenters and move wholesale to the public cloud. As adoption of private cloud has grown and evolved, it has moved from bespoke private cloud implementations towards being built on end-to-end private cloud stacks from major providers, with popular options being Microsoft Azure Local, Google Distributed Cloud, AWS Outposts, or VMware Cloud Foundation. This has resulted in unprecedented capability for enterprises running their own applications and services – but with this has also come a co-dependency on external providers for the ongoing operations of the control plane of the private cloud.

Should any serious technology or political issues arise that interrupts the connection between the public cloud based control plane and the private cloud, services deployed and delivered on the private cloud infrastructure may remain static, degrade over time, or even stop working. The end result is a bought and paid for sovereign physical infrastructure that is unable to operate effectively due to a non-sovereign operations management dependency – and this is a major risk today that a few years ago seemed unthinkable.

European customers have been providing forceful feedback to private cloud stack providers that this public cloud control plane dependency is untenable, and the market is beginning to respond. Most, but not all, providers of private cloud stacks have begun to offer an on-premises approach to the control plane, allowing fully disconnected management of applications or digital services deployment and operations, as well as of licencing tracking and billing, or updates and patching from offline sources.  The big challenge though is that these disconnected options are often limited when it comes to go to market, with vendors limiting access to the largest companies or critical national infrastructure providers or the defense industrial complex. While this may be acceptable initially as solutions come to market and are proven, for the longer-term vendors will need to make disconnected operations a core part of their value proposition across the whole customer base.

AI sovereignty: the new frontier

AI sovereignty has been part of the digital sovereignty debate for some time. But it has now emerged as the new frontier: the question of who controls the models, the data used to train them, and the inference infrastructure is becoming as contested as data residency was five years ago. The general read in the room: AI sovereignty is harder to achieve than data or infrastructure sovereignty, and the messaging across the industry remains inconsistent.

Dig deeper into the research

The dinner was one part of a broader IDC programme on digital sovereignty across Europe. If the themes above are relevant to your positioning or go-to-market strategy, here is where to go next.

Digital Sovereignty Beyond the Label – IDC’s Strategic Guide cuts through the definitional noise and explains what buyers actually evaluate when assessing sovereign solutions and providers. Download free.

From Sovereignty Claims to Credible Positioning – A customer case study on how technology providers are turning sovereignty into a commercially viable proposition. No form required.

Missed the webinar? Rahiel Nasir and Duncan Brown covered buyer expectations, sovereign washing, and practical go-to-market guidance on June 18. The on-demand recording is available here.

IDC’s Digital Sovereignty research covers cloud strategy, data governance, regulatory compliance, and infrastructure sovereignty across European markets. Research presented at the dinner was drawn from IDC’s European Digital Sovereignty Survey and the Semiannual Public Cloud Services Tracker.

Rahiel Nasir

Rahiel Nasir - Research Director, Cloud and Datacenters, Enterprise Infrastructure

Analyst Profile
Rahiel Nasir is Research Director within IDC’s enterprise infrastructure global research domain and part of the Cloud and Datacenters subdomain. Rahiel is IDC’s global lead on digital, cloud, and AI sovereignty. In this capacity, he covers emerging sovereignty initiatives and…
Andrew Buss

Andrew Buss - Senior Research Director, Cloud and Datacenters, Enterprise Infrastructure

Analyst Profile
Andrew Buss is Senior Research Director within IDC’s enterprise infrastructure global research domain and part of the cloud and datacenters subdomain. He leads IDC’s worldwide datacenter research, in which he and his team provide qualitative and quantitative insights into the…
Duncan Brown

Duncan Brown - Group Vice President, Worldwide Security Products, Worldwide Sustainability

Analyst Profile
Duncan Brown leads IDC’s worldwide security products research, covering endpoint, network, identity, cloud, application security. His analysis and opinions on security, cyber-resiliency, sovereignty and AI governance are widely sought by industry leaders and investors, while his comments on industry trends…

In two earlier posts I argued that SaaS is being disrupted rather than killed, and that the per-seat revenue model is on its way out. The dramatic decline in market capitalization of SaaS vendors over the past 9 months, sometimes referred to as the SaaS-pocalypse, is partly about investors pricing in the fear that SaaS applications will recede behind an AI agent layer. This would make the SaaS applications become invisible “featureware,” with the agent capturing the user relationship, the workflow, and, eventually, the budget. 

This post will show that enterprise buyers now expect their software vendors to supply the agents, and to serve as the trusted source of data and context for custom-built and third-party agents. This is the natural next progression for SaaS vendors and represents an attractive market opportunity. 

To avoid confusion, here are IDC’s AI-related definitions. An AI assistant is conversational and works as a tool for a human. An AI agent is autonomous, uses other tools, and carries memory and context across tasks. An agentic workflow is a business process that an agent executes end-to-end with limited human intervention. 

SaaS vendors are faced with a dual threat, hence SaaS-pocalypse 

The first threat is that organizations will simply write their own applications instead of buying standard SaaS applications. This threat triggered the dramatic devaluation of SaaS stock in February as Anthropic released its Code capabilities. Conversations with CIOs in enterprises partly validate this fear. While they are not contemplating creating core accounting, HRIS or workforce management applications, they might build rather than buy auxiliary applications in areas such as planning, performance management, compensation management, logistics planning, etc. Application areas where customer requirements vary widely and where legal complexity is low are moving gradually from buy to build. 

The second threat is that agentic overlays will increasingly handle the user interaction while AI agents access SaaS applications via API interfaces to carry out transactions on behalf of the human user. The implication is key price justifiers of SaaS applications, such as the interface, the feature breadth, and the brand gradually disappears from the users. This also implies that the per-seat user pricing stop making sense, when humans are no longer the primary users of SaaS applications. 

AI agent adoption is already past the tipping point 

In IDC’s April 2026 Future Enterprise Resiliency and Spending survey of organizations with 500 or more employees, 74% had already deployed at least one agent, 15% were piloting, and only 1% reported no use and no plans. The same respondents expect the number of agent types in production to roughly triple, from 24 in March 2026 to 62 by 2027. Buyers are not evaluating whether to enter the agentic era. They are deciding which parts of their operation to hand over first, and operational and core business data are their top target. 

The big question is who will deliver these AI agents. Most organizations have deployed standard AI tools and run pilots, but few have redesigned core processes to use AI at scale. The survey results shows that most organizations deploy ready-made agents wherever they can, rather than build their own. 

Where vendor-supplied agents win 

IDC distinguishes four kinds of agent by who builds them and how the buyer obtains them. In-application agents come packaged inside the application and the buyer simply adopts them. Low-code / no-code agents are configured by the buyer in a visual builder the vendor provides. Standalone agents are third-party products the buyer implements alongside existing applications. Custom-built agents are assembled by internal teams using full-stack orchestration frameworks. 

The survey data, which focuses on AI agent quantities as opposed to spend, points in one direction. The two types that vendors supply directly, in-application agents and low-code or no-code builders, are growing fastest in numbers and from the largest installed base. Custom-built agents show the slowest growth, because the orchestration they require is more difficult and require more inhouse skills to build. Buyers prefer to adopt or configure an agent that already understands their data and respects their permissions over building one from scratch. 

SaaS applications as trusted data and context for custom-built agents 

IDC also sees massive demand for custom-built AI agents, especially for core business processes unique to an industry or organization. Standalone products and custom-built agents will operate inside the same enterprise, and they will need data and context that lives inside your application. 

A custom-built or third-party agent that accesses data “from anywhere” still needs a place where the data is correct, the process is compliant, the permissions are enforced, and the transaction is guaranteed to execute. A SaaS vendor can offer vetted business processes, governed data, and audit trails for custom AI agent consumption. This is, in IDC’s view, a key future role for today’s SaaS applications. 

What the AI-pivoted SaaS application looks like 

The interface stops being a single screen and becomes several modes serving the same processes: the traditional UI, a conversational UI, a flow-of-work UI embedded where the user already operates, and machine interfaces so external agents can call the application directly and safely. 

The AI-pivot requires SaaS vendors to rethink the workflow from the ground up, which touches the full stack: foundation models, an embedding layer, a vector database, retrieval-augmented generation, an orchestration layer, guardrails, monitoring, and version management. The vendor also has to give buyers an agent toolkit of their own, so that the low-code and no-code configuration buyers increasingly demand happens inside the vendor’s governed environment rather than outside it. 

European vendors carry an additional set of requirements that, handled well, become a selling advantage. Compliance with GDPR, NIS2, and the EU AI Act (still there despite the recent delay), data residency and sovereign cloud guarantees, genuine multi-language model performance, and transparency in how the AI reaches a decision are all conditions of sale to compliance-sensitive European buyers. 

SaaS is not dead, and the incumbents are not doomed. But the asset that justifies a vendor’s existence is shifting from the screen the user looks at to the agents the vendor supplies and the governed data those and other agents rely on. Your customers already expect you to be their agent supplier. The only question is whether you are ahead of that shift or reacting to it. 

So, what do you actually do with this? There are three concrete moves software vendors need to make in the near term. They are specific, they are sequenced, and the window to move first is closing. I will walk through all three in a focused 25-minute webcast, grounded in IDC survey data from more than 1,000 enterprise organisations: where your customers sit on the AI maturity curve, why vendor-supplied agents will dominate enterprise deployments through 2030, and which ERP and SaaS processes are attracting the most AI investment right now. Secure your spot here. 

Bo Lykkegaard

Bo Lykkegaard - Associate VP for Software Research Europe

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis,…