Logo
Become a Client

Resource Regions: Western Europe

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…

At the IDC Quanta launch webinar, Jamie Fiorda, SVP of Product Marketing at IDC, laid out why the gap between how fast markets move and how fast organizations understand them is becoming the defining challenge for companies trying to act before conditions shift again. He also laid out where AI-powered intelligence tools fit into closing it.

Fiorda’s starting point was access: who actually gets to use the intelligence a company already has. In most organizations, the market intelligence a company already invests in tends to serve a narrow slice of the business (a research team, maybe a strategy analyst or two), while the insights that could sharpen decisions elsewhere in the company never reach the people making them.

That’s the pattern Fiorda described: intelligence that exists, but sits in a portal most of the organization has no reason to open. The problem isn’t the quality of the research. It’s that the value of it is being rationed by who has access, right as every function in the business is under pressure to move faster on planning that used to have more runway.

Positioning AI as an Intelligence Layer, Not Another Tool

Fiorda’s case for IDC Quanta wasn’t framed as a new subscription or a new dashboard to learn. It was framed as infrastructure: a layer that sits beneath the tools an organization already uses, making the intelligence within them actionable and defensible.

Organizations already have plenty of AI tools. What most of them can’t do is prove their answers are trustworthy. That gap shows up largest in finance: nearly a third of global enterprises still run their finance analytics function with no formal structure at all, department by department, which is exactly where an M&A assumption or a market-sizing input gets challenged first by a board, an investor, or an auditor. Fiorda’s argument centers on embedding intelligence directly into the workflows where strategy is developed, backed by a source a leadership team can point to when that challenge arises.

What This Looks Like Across a Business

Fiorda walked through what that shift means function by function, not as a feature list, but as a picture of how fast an organization can move when intelligence isn’t gated to one team.

A market intelligence team doesn’t change what it does, just how quickly it does it: sizing markets and benchmarking with intelligence flowing directly into the tools they already use.

Finance picks up a use case that’s often entirely new: market sizing inside their own models, risk analysis, revenue validation, all backed by third-party data at the point of decision. IDC research points to real upside here: cognitive technologies applied across due diligence and predictive analytics are projected to drive a 25% increase in M&A returns by 2027, largely because AI-enabled screening surfaces viable targets faster than traditional methods can.

Product gains competitive benchmarking and roadmap validation the same way. Marketing builds segmentation and messaging grounded in actual demand trends instead of assumptions a real shift for CMOs navigating today’s pressure to justify every dollar: IDC research found 52% are already leaning harder into scenario-based planning and 41% report increased pressure to justify marketing ROI, both signs that assumption-driven messaging is no longer good enough. And sales walks into a room with validated, current talking points instead of a battlecard built last quarter.

The strategic point is that all five are pulling from the same source of truth, at the same time, without waiting on each other.

The Question Leaders Must Consider

Fiorda’s close reframed the pitch as a single question for leadership to sit with: if the intelligence an organization already values could reach five functions instead of one, what would that be worth? It’s worth sitting with the scale of that gap: IDC research shows nearly 60% of Chief Data Officers say their organizations need to rethink how they use analytics in decision-making, and only about a third of executives are active users of the intelligence tools already in place. That’s not just a research quality problem. It’s strategic value sitting untouched, simply because of where information lives.

In a landscape changing this quickly, the organizations that move fastest will be the ones where that research actually reaches every team that needs it.

Learn More

Organizations already working with IDC can talk to their account team about which teams could get access next. Those exploring IDC Quanta for the first time can request a demo at idc.com/quanta to see where this fits for your team.

Ryan Smith - Content Marketing Director - IDC

Ryan Smith is the Director of Content Marketing at IDC, where he leads brand-level content and social media strategy, aligning research insights with compelling storytelling to engage technology decision-makers. With a background in both IT and marketing, Ryan brings a unique blend of technical understanding and creative strategy to his work. He’s also a seasoned storyteller, speaker, and podcast host who believes the right message, told the right way, can drive both trust and transformation.

Today we launched IDC Quanta. I led the strategy behind it, working closely with our product, research, and engineering teams to turn a point of view into something real. I want to share what building it taught me about the state of AI adoption, not just at IDC, but everywhere.

The number that started it

By 2029, there will be a billion AI agents running inside enterprises worldwide. That translates into an enterprise running thousands of agents. The investments to prepare for that future are already taking place. Hyperscalers are increasing AI infrastructure spend from $54 billion in 2023 to $800 billion by 2029 to create inference capacity at that scale. Enterprises are spending $400 billion on AI platforms, apps and services this year, headed toward a trillion by 2029.

Most enterprises can’t orchestrate at that scale today. Most vendors can’t fully support it yet either. A billion agents means the entire IT industry, vendors and enterprises together, has a massive amount of infrastructure, governance, and orchestration work still ahead of it before that number is something to be excited about instead of something to be worried about. We didn’t want IDC standing outside that work, measuring it from a distance. We wanted to build the intelligence layer that helps our clients get through it. That’s the thinking behind Quanta.

The gap we kept running into

Earlier this year, we ran our global AI maturity benchmark. In the U.S., the largest single group of organizations, 39%, sits at what we call the AI Pivot stage. They’ve moved past ad hoc experimentation. They have momentum and intent. But they’re still reacting to use cases as they surface instead of executing against one enterprise strategy.

That gap comes down to a problem our team designed against from the start: islands of AI. Fragmented experimentation across functions that makes enterprise-level orchestration nearly impossible. Half of organizations have an AI roadmap defined at the functional level. Finance has one. IT has one. Marketing has one. They don’t connect. There’s no shared prioritization and no way to see where one function’s work could accelerate another’s.

The reason this is more than a coordination problem is because agents don’t respect functional boundaries. A customer service agent needs data from CRM, from order management, from your knowledge base. An operations agent touches supply chain, finance, and procurement. The moment you deploy agents that work across functions, a fragmented roadmap becomes an architectural blocker. It’s why 42% of CEOs plan to hire a Chief AI Officer in the next year. They’re looking for someone who can see the whole board, not just their own function’s piece of it.

We had our own version of this problem to solve. For decades, IDC’s model was research in one place and data in another, and clients had to hunt across both to get a full picture. Quanta brings our research and our data together in a single platform, so that fragmentation stops being something you have to solve every time you come to us.

The curve nobody wants to admit they’re on

For the past three and a half years, enterprises have struggled to prove the ROI on their AI use cases. Now we have runaway token costs arriving at the exact moment everyone is lining up to deploy agents.

IDC recently published a report on effective agent cost management. In the report, the team showed cost per action spikes early in almost every deployment, before value catches up. We call that phase High Anxiety. Value climbs slowly the whole time, crossing cost at what we call the Strategic Alignment phase. Past that point, cost keeps falling and value keeps climbing. That’s the payoff phase.

Most organizations in this industry are still on the wrong side of that curve. The token economy conversation isn’t about whether AI is worth the spend. It’s about how long it takes you to get through the High Anxiety phase. The organizations pulling ahead are the ones treating ROI as a discipline, not a one-time calculation. A cost model that only counts inference will undercount true total cost of ownership by 30 to 60%. The shift that matters is treating tokens like a raw material, the way a factory tracks cost per unit, instead of like a technology bill.

That framework is one example of the kind of intelligence Quanta is built to deliver. Not a report waiting to be opened weeks after it would have mattered. Something you can reach directly at idc.com, or through connectors built into the platforms your team already uses, so the intelligence shows up where the decisions and execution happens instead of sitting in a document.

Why I’m telling you this today

Quanta didn’t come from spotting a market opportunity from a distance. It came from our team solving the exact problem I just described, for our own organization, alongside the people who build and research this every day.

For decades, our model was simple: we publish research, and you come find it. That worked when the pace of decisions making was slower. It doesn’t work anymore, not with a billion agents coming and the decisions being made this year that companies will live with for years. We built Quanta to work two ways. Come to idc.com directly for grounded, evidence-based answers. Or reach that same intelligence through connectors already built into the platforms your team uses, so you’re not switching context.

I’m proud of what we’re launching today. But the thing I actually want you to take from this is the reminder that the gap between where your organization is and where it needs to be closes the same way ours did: someone has to own the whole board, and go get the intelligence instead of waiting for it to come to you.

Meredith Whalen - Chief Research Officer - IDC

Analyst Profile
As IDC's Chief Product, Research & Delivery Officer, Meredith Whalen leads the company's global product, research and data, and delivery organizations. Under her leadership, IDC delivers cutting-edge intelligence to the world's leading technology vendors, enterprises, and investors as they navigate the evolving AI economy. Meredith sets the strategic direction for IDC's global analyst community, shaping research methodologies and agendas that generate industry-leading data and actionable insights to drive high-impact business decisions. With more than 20 years at IDC, Meredith has been a catalyst for some of the company's most transformative initiatives. She founded IDC's Industry Insights and Tech Buyer business units and pioneered the industry's first comprehensive business use case taxonomy. She also led the creation of IDC's DecisionScape methodology-a strategic framework that empowers organizations to better plan, implement, and optimize their technology investments. A recognized thought leader and sought-after speaker, Meredith regularly delivers keynotes at major global technology events and advises senior executives on the trends shaping the future of business and technology. Meredith holds a B.A. with honors from Wellesley College and an MBA with honors from Babson College's F.W. Olin Graduate School of Business.

I spent much of my career helping organizations operationalize customer and employee intelligence. During that time, I watched an entire industry emerge around dashboards.

Companies invested billions collecting customer feedback, employee sentiment, operational metrics, and business intelligence. Entire software categories were built around helping organizations visualize that information and drive action.

The model worked extraordinarily well.

Companies like Qualtrics, Medallia, Tableau, Salesforce, and many others helped define a generation of enterprise software. But over time, a pattern emerged.

The problem was never collecting the data; the problem was getting people to use it.

Organizations spent years trying to encourage executives, managers, and frontline employees to regularly log into dashboards, review reports, identify issues, and take action.

Adoption became a business problem unto itself. The intelligence existed, but the behavior did not.

The hidden cost of dashboards

The challenge with dashboards is simple: they require users to interrupt their workflow.

Every dashboard assumes a user will:

  1. Stop what they are doing.
  2. Open a separate application.
  3. Find the relevant information.
  4. Interpret it.
  5. Decide what to do next.

That process creates friction, and friction is the enemy of adoption.

Today, most professionals spend the majority of their time in a handful of environments:

  • Email
  • Teams
  • Slack
  • CRM platforms
  • ChatGPT
  • Claude
  • Productivity applications

These have become the operating systems for modern work. Every additional application competes for attention against those environments, and most lose.

AI changes the equation

Large language models have created a new interface for work, allowing users to interact with intelligence through natural language rather than reports, dashboards, and portals. For the first time, intelligence no longer needs to live in a separate destination. Instead, it can travel directly to the user.

An executive can ask a question inside ChatGPT.

A seller preparing for a customer meeting can instantly surface market trends, competitive threats, and analyst insights directly within Salesforce.

A product leader can receive market insights through Teams.

A strategist can query complex research through an AI assistant.

The user never leaves their workflow, because the intelligence comes to them. This represents more than a user experience improvement: It’s about introducing a fundamentally different operating model.

The goal isn’t simply better intelligence. It’s reducing the friction between intelligence and action.

Why proprietary data matters more than ever

Many organizations believe AI itself is the competitive advantage. I believe the opposite.

As models become increasingly accessible, the differentiator will be intelligence.

Organizations that possess unique, proprietary, trusted data will have a significant advantage because they can combine AI with insights that cannot be found on the open internet. That’s exactly what makes this moment so rich with potential.

At IDC, we have decades of proprietary market intelligence: market sizing data, competitive positioning, technology adoption trends, vendor performance data, industry forecasts, and strategic research.

These are the datasets organizations use to make billion-dollar decisions. Historically, customers accessed that intelligence through reports, portals, and analyst interactions.

Today, AI allows us to reimagine how that intelligence is consumed.

From intelligence systems to decision systems

The next evolution is bigger than dashboards, and it’s bigger than reports. It’s even bigger than AI assistants.

The real opportunity is creating a technology intelligence layer that connects:

  • Market intelligence
  • Customer intelligence
  • Operational intelligence
  • Financial intelligence
  • First-party enterprise data

When those signals come together, organizations gain a more complete view of their markets, customers, competitors, and business performance. At that point, we are no longer talking about a research platform, but a new decision system.

IDC Quanta was built around this idea: bringing trusted technology intelligence directly into the workflows where decisions are made.

The organizations that win in the next decade will not necessarily have the most data, but they will have the least friction between intelligence and action.

Dashboards are dead because intelligence no longer needs a destination. It can travel directly to the moment of decision.

Nick Mercurio - Chief Revenue Officer - IDC

Chief Revenue Officer As Chief Revenue Officer of IDC, Nick Mercurio leads the company’s global commercial organization, including Sales, Customer Success, and Revenue Operations. He is responsible for accelerating growth, expanding customer value, and advancing IDC’s position as the technology intelligence layer of the AI economy.

Right now, the world is generating more than seven petabytes of data every second. That’s roughly the equivalent of producing 17 billion books every second. By 2029, that number will more than double. And that’s before more than a billion AI agents come online, each one generating, consuming, and amplifying information at machine speed. [IDC Global DataSphere Forecast, 2025–2029]

We’re drowning in information.

But more data doesn’t mean more clarity. In fact, it’s quite the opposite. Most of what’s flooding in isn’t even original; it’s copies, reprints, and regurgitations. And AI can make even bad data look very convincing.

In a world where noise is growing and clarity is fading; the biggest challenge enterprises face is discerning what’s real from what’s just an echo.  The winners won’t be the organizations with the most data. They’ll be the ones with frictionless access to the truth — and the confidence to act on it.

Today, IDC is delivering that with IDC Quanta, the technology intelligence fabric of the AI-enabled enterprise. For more than 60 years, organizations around the world have trusted IDC to help them navigate technology decisions and deliver data-backed intelligence to sharpen business strategies.

IDC Quanta takes the same structured, sourced, defensible insights, pairs them with your data and context, and puts them inside the tools your teams already use. A process that previously required disjointed systems, manual synthesis, and endless hours is now relevant, citable, and frictionless.

Intelligence embedded where you work

IDC Quanta lives inside email, inside Anthropic’s Claude AI, and inside the custom AI tools and workflows enterprises are building right now. No portal or separate login required. No need to leave the tools or workflows you’re already in. Instead, IDC Quanta gives you the ability to call on its intelligence the moment you need it — whether that’s a competitive client deal on the line, a board presentation, a roadmap under review, or vetting a new software platform.

Intelligence built on your context

Upload your own data and documents, and IDC Quanta synthesizes and visualizes them alongside IDC’s research in a single session. Adding to that is an extensive memory layer that ensures every conversation deepens what IDC Quanta knows about your role and your priorities, making each answer it returns sharper than the last. Your business context, combined with IDC’s, gives you a fuller picture than either could produce on its own.

But there’s something else important to note when it comes to context, and that’s security. With more than 175 beta customers helping us shape IDC Quanta’s development, the questions we heard most were simple: is loading data and documents secure, and are those documents used to train the model? The answers are “yes” and “no, respectively. Every upload lives in a private workspace with AES-256 encryption, enterprise-grade compliance and privacy controls, is automatically deleted after 90 days, and is never used to train IDC’s models. That means the confidence IDC Quanta gives you never comes at the cost of what you shared to get it.

Intelligence you can defend

Every response IDC Quanta gives runs through a multi-agent system that validates it against IDC’s 15B proprietary data points and expert-led research before it ever reaches you. An expandable reasoning panel shows exactly how the answer was built and includes detailed citations for answers you can stand in the boardroom and defend.

Intelligence on your schedule

IDC Quanta doesn’t wait to be asked. Automated scheduling capabilities enable IDC Quanta to proactively deliver the intelligence you need to monitor with regularity and then goes a step further, surfacing anonymized peer signals and related follow-up questions you didn’t know to ask.

An exciting future

IDC Quanta is live today and available to all current and new IDC customers. It was Amarok CIO Ashley Spicer who said it best:

“We are getting ridiculous, previously unimaginable value from IDC Quanta.  If you had told me 20 years ago that I would have access to something like this in my lifetime, I would have said no way. Our team is creating massive value by fielding questions and guiding understanding for critical decisions and strategic programs.”

With more integrations, more use cases, and packages tailored specifically for CIOs and IT leaders arriving this August, we’re just getting started. Intelligence will never be the same.

Visit www.idc.com/quanta to see it for yourself.

 

Lorenzo Larini - Chief Executive Officer - IDC

Chief Executive Officer of IDC, responsible for leading the company’s global strategy, operations, and growth. Lorenzo brings more than two decades of experience across the Research, Advisory Services, Enterprise Software, and AI sectors, most recently serving as CEO of Mint.ai, where he led the development of AI-driven workflow solutions. Previously, he served as CEO of Ipsos North America, where he led transformative growth for one of the world’s top market research and data analytics firms. He has also held global senior executive roles in Gartner’s technology division, including SVP of Executive Programs, advising global CIOs and enterprise leaders on digital transformation and operational excellence.

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…
Duncan Brown

Duncan Brown - Group Vice President, Global Domain Lead: Worldwide Security & Trust

Analyst Profile
Duncan Brown leads IDC’s worldwide Security and Trust research, encompassing cybersecurity hardware and software products as well as professional and managed services. His analysis and opinions on security, cyber-resiliency, sovereignty and AI governance are widely sought by industry leaders and…