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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.

There is no shortage of ways to get an answer quickly. A question into ChatGPT, a search across a few analyst portals, a summary pulled from a PDF someone emailed three weeks ago. In under ten minutes, you have something that looks like intelligence. 

The problem is what happens next. When that answer gets questioned, challenged, or presented to a board that wants to know where it came from. 

This is the AI answer gap: the widening distance between the speed at which AI tools can produce answers and the standard to which those answers must be held in real decision-making environments. It is not a technology problem. It is a structural one. And it is getting harder to ignore. 

Three failures hiding inside one problem 

The AI answer gap isn’t a single breakdown. It’s three distinct failures that tend to travel together, and that compound each other when they do. 

The speed-to-answer gap 

When a critical decision arrives: a market entry call, a board presentation, a competitive repositioning. How long does it actually take your team to ground it in trusted, defensible research? For most organizations, the honest answer is longer than it should be. 

In IDC’s conversations with enterprise technology leaders, the time between a strategic question and a confident, evidence-based answer is consistently measured in days or weeks, not hours. Teams spend that time searching: pulling reports, cross-referencing sources, building a coherent picture from fragments. 

The cost isn’t just time. It’s the decisions that get made on incomplete intelligence. 

The AI credibility crisis 

The intuitive fix is to use AI to speed up the research process. That introduces a second problem the first one obscures. 

Public AI tools are fast. They are also wrong in ways that are difficult to detect until the moment they matter most. Hallucinated citations. Outdated market data presented as current. Confident summaries of research that doesn’t exist. The pattern is consistent enough that “AI hallucination” has become a standard budget discussion item for enterprise technology teams. 

The deeper issue isn’t accuracy in isolation. It’s accountability. When a technology leader presents a recommendation to the CFO or the board, the question is never just “is this correct?” It’s “where did this come from, and can you defend it?” A fast answer with no traceable source fails that test regardless of whether it happens to be right. 

“An AI backed by IDC’s research gives me a lot more confidence in the answers.”
— Phillip Langeberg, CTO, The Resorts Companies

Confidence isn’t just about accuracy. It’s about provenance. The ability to say: here is the answer, here is the source, here is the reasoning — and have all three hold up under scrutiny. 

The workflow intelligence barrier 

The third failure is the most underestimated. Even when high-quality intelligence exists: proprietary research, trusted analyst data, validated market sizing. It tends to live somewhere other than where decisions get made. 

A portal that three people have bookmarked. A PDF that gets forwarded by email. A report that someone read six months ago and summarized in a slide deck that may or may not reflect the current version. Intelligence that should be shaping decisions is instead sitting one context switch away from the people who need it. 

The result is a structural gap between the intelligence an organization has access to and the answers that actually inform its decisions. Closing the AI answer gap requires more than better data. It requires rethinking where intelligence lives. 

Why the gap is widening, not closing 

The intuitive assumption is that more AI tools mean better answers. The evidence suggests the opposite is happening. As the number of AI research tools available to enterprise teams has multiplied, so has the complexity of the research environment: more sources to reconcile, more outputs to verify, more decisions about which tool to trust for which type of question. 

Across IDC’s research with enterprise technology organizations, the pattern is consistent: the teams that make the fastest, most confident decisions aren’t the ones with the most tools. They’re the ones whose intelligence infrastructure is the most coherent, where trusted data, workflow integration, and traceability work together rather than separately. 

That’s a design problem. And it’s one that the current generation of AI research tools, built to maximize speed rather than defensibility, hasn’t been designed to solve. 

What closing the AI answer gap requires 

Addressing the AI answer gap doesn’t mean slowing down. It means building toward a different standard, one where speed and defensibility aren’t in tension. 

Three things distinguish intelligence infrastructure that closes the gap from infrastructure that widens it. First, answers need to be traceable: every output should be linkable to a source that can be examined, challenged, and defended. Second, intelligence needs to live where decisions happen, not in a separate system that requires deliberate effort to access. Third, the research base itself needs to be trustworthy at the source: proprietary, current, and built on a methodology that can withstand scrutiny. 

These aren’t aspirational standards. They’re the minimum bar for answers that are usable in a real decision-making environment. The organizations closing the AI answer gap are the ones treating them as requirements, not nice-to-haves. 

The path forward 

The AI answer gap is a structural problem, and structural problems require structural solutions. Adding faster tools to an incoherent intelligence infrastructure doesn’t close the gap. It accelerates it. 

IDC has spent six decades building the proprietary research base that enterprise technology decisions depend on. The next step is making that intelligence accessible where decisions actually happen, embedded in the workflows, the tools, and the moments where the AI answer gap currently lives. 

IDC Quanta is that platform. If you want to stay informed as it launches this summer, the link below takes you there. 

Discover IDC Quanta

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.