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At the IDC Quanta launch webinar, Joe Bradley, CTO at IDC, made the case for why trusting an AI-powered answer shouldn’t require faith; it should require an architecture you can actually inspect. You’ve heard “AI-powered” enough times this year that a healthy dose of skepticism is the right response. Fair. So instead of asking you to trust that IDC Quanta gets its answers right, here’s what Bradley says is actually happening under the hood when it does.

Bradley breaks it down into two layers. The first is an MCP server: essentially a pipe that gives Claude direct access to IDC’s data, the trackers, the forecasts, the market figures. It also carries instructions for how that data is structured and how to use it. The second is IDC’s own Claude plugin, which goes further, Bradley explains. It shapes how the AI reasons about that data, tells it what’s relevant for what purpose, and requires it to surface a source before handing over any answer. Put together, when someone asks a question, Claude isn’t searching a phrase in a database, Bradley says. It’s reasoning with IDC’s own methodology built into the process.

That’s the theory. Here’s what it looks like in practice.

The Moment It Earned Trust

In the product demo, Bradley walked through a scenario: an FP&A analyst building a market forecast ahead of a CFO review, projecting 16% growth in a segment his company competes in. He asked IDC Quanta to check that number against an external benchmark, right inside Excel.

In under a minute, Quanta surfaced IDC’s actual forecast for that market: 12.1% growth through 2029, with the category decelerating to single digits in the later years. His model hadn’t caught up to where the market was actually headed. That’s the payoff of the architecture above. The answer arrived with its source attached.

Why Even Build an App

Technical buyers reasonably ask why IDC needs its own app when Claude and ChatGPT already exist. IDC isn’t positioning itself as a competitor to the assistants people already use daily. It’s building something with a narrower job.

The case for IDC Quanta comes down to control over how IDC’s own data gets handled and delivered. It exists because of what only a dedicated app can guarantee: a single place that collects everything relevant across an IDC relationship, a direct line to a live analyst when the automated answer isn’t enough, and data handling built on tenant isolation, enforced access controls, and audit logs that capture every user action. Which raises the next question technical buyers ask first.

Provenance You Can Check Yourself

Bradley’s last test for anyone skeptical of AI-generated answers is provenance. Can you verify where it actually came from? In a second demo, he showed IDC Quanta processing a strategy document sent over email, then breaking its answer down into cited data cuts, each one tied explicitly to the filters and definitions behind it.

Nothing here is asserted without a source attached, and nothing requires trusting the AI’s summary over the underlying data itself. That’s the actual answer to “how do you know it’s not confidently wrong”: you don’t have to take Quanta’s word for it. You can check.

See It Yourself

The architecture, the demo, and the sourcing are easier to evaluate firsthand than to take on faith. Request a demo, or talk to your IDC account team if you already have one.

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.

This is the second post in Meet IDC Quanta, a short series showing what the product actually does, starting with the portal, then your inbox, then wherever you’re already working in Claude.

It’s 4:47 PM. You’re prepping for tomorrow’s board meeting, and a question just landed that wasn’t on your list: how does your cloud infrastructure position compare to what’s shifted in the market over the last two quarters. You could open a portal, log in, run a search, filter by date, read three reports, and synthesize an answer. Or you could pull up your phone in the elevator.

Most executives don’t have a research workflow. They have an inbox. IDC Quanta was built on that premise: the fastest tool for a time-pressed leader is the one they never have to open, because it’s already open.

What it actually does

IDC Quanta in email lets you ask a question and get a sourced IDC answer back in about 60 seconds. It works the way you’d already expect email to work, which is exactly the point.

Emailing Quanta
  1. Compose an email to brief@quanta.idc.com, from your phone, your laptop, whatever’s in front of you.

  2. Ask the question in plain language. No query syntax, no keyword tricks. “Which cloud infrastructure vendors gained share in EMEA in the past 12 months?” is a complete request.

  3. Send it, and keep moving. A structured, sourced answer arrives in about 60 seconds, fast enough that you can send the question walking into a meeting and have the answer before you sit down.

  4. The reply carries its own receipts. Every claim in the answer traces to a specific IDC source. If you want to go deeper, “View in IDC Quanta” drops you straight into the full portal conversation, context intact.
A sample email response from IDC Quanta

No login screen to remember, no new app for IT to vet. And no tab-switching mid-meeting. The intelligence comes to the inbox. The inbox doesn’t change to accommodate it.

Where this earns its keep

The mechanic is simple. What makes it valuable is what it replaces across a week of an executive’s actual work:

  • Before the call.
    A prospect meeting is in twenty minutes and you need a current read on their competitive positioning. Email the question on your way to the conference room. The answer’s there before you sit down, cited and ready to use.

  • Benchmarking your own thinking.
    Attach a competitive deck or account plan to your email along with your question. IDC Quanta reads it alongside its own research base and flags where your internal view and IDC’s tracked data disagree. You’re getting your own analysis checked against the numbers.

  • The follow-up nobody has time to chase.
    Someone asks a sharp question in a meeting and the honest answer is “let me get back to you.” Now that follow-up takes one email and about a minute. Who else on your team wishes they had that?

  • Board and investor prep, compressed.
    The night-before scramble for one more data point doesn’t need the whole deck reopened. One email, one sourced answer, dropped straight into the slide.

Why this isn’t just a fast AI reply

A lot of tools will answer an email question with confidence. Confidence isn’t the same as being right, and it isn’t the same as being defensible in a room full of people who will ask where the number came from. Every IDC Quanta answer draws on IDC’s research base: 1,000+ analysts across 100+ countries, tracking 15B+ data points and 800K+ companies annually. That’s the citation trail behind every answer. It’s what makes an answer dropped in your inbox worth repeating in the room.

Get it in your inbox

If you’re already an IDC Quanta customer, brief@quanta.idc.com is live. Send the question you didn’t have time to research properly and see how fast a sourced answer actually moves.

If you’re not yet a customer, request a demo, and we’ll show you what it looks like when your inbox starts acting like a research 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.

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.