At its flagship Directions 2026 event on April 8, IDC announced something that would have been strange to say five years ago: the world’s leading technology research firm is now an AI platform company.

IDC Quanta, the new product introduced at the event, is an AI-powered intelligence layer designed to embed IDC’s 60-plus years of proprietary research directly into the workflows where business decisions get made. It is building an MCP server and working with Anthropic to bring IDC research natively into Claude workflows. General availability is expected in summer 2026.

That is notable. But the research IDC shared alongside the platform launch is arguably more useful to the average business leader trying to understand where AI is heading.

The Numbers That Matter

IDC’s projections from Directions 2026 are worth keeping in the back of your mind over the next few years:

$22.5 trillion in cumulative economic value by 2031. That is IDC’s forecast for the total global economic impact of AI, driven by productivity gains, new revenue models, and business transformation across industries.

1 billion AI agents deployed by 2029. IDC expects the number of actively deployed agents worldwide to exceed one billion within three years. That is not a metaphor for chatbots — it refers to autonomous software systems handling real tasks inside enterprise operations. The inflection point, IDC says, will come when AI shifts from training to inference at scale and agent deployments embed themselves into everyday business operations.

42% of organisations cannot measure AI ROI. Despite all the investment and all the enthusiasm, nearly half of companies currently deploying AI cannot tell you what they are getting from it. IDC introduced an Agentic Business Value Maximization Framework at the event specifically to address this gap — a structured approach to strategy, use-case prioritization, value mapping, and continuous optimization.

The Agentic Buyer

One of the more thought-provoking pieces of research IDC highlighted was about how buying itself is changing. As AI agents become more capable and more embedded in business operations, the purchasing journey is shifting. Agents are increasingly shaping how companies discover, evaluate, and select technology. The role of the human buyer — researching, shortlisting, negotiating — is being augmented, and in some cases replaced, by AI-mediated decision processes.

This is an early-stage shift, but it has real implications for how enterprise vendors market their products and how procurement teams operate. The agentic buyer lifecycle is a new concept that will matter more as agent deployments scale.

What IDC Quanta Actually Does

IDC Quanta is designed around five principles the firm calls Embedded, Contextual, Secure, Aware, and Rigorous.

The Embedded principle is the most interesting from an enterprise AI perspective. Rather than delivering research through a portal that employees have to visit, IDC Quanta pushes intelligence directly into tools professionals already use — starting with email and expanding to collaboration and AI platforms. The Anthropic/MCP integration is the clearest expression of this: IDC research becomes accessible inside Claude sessions without switching tools.

The Contextual principle lets organisations bring their own data, documents, and third-party content into the platform alongside IDC research, with context retained across interactions. The Rigorous principle grounds everything in cited, transparent source material — not generated summaries that may or may not reflect the underlying data.

What This Means for Business

The fact that IDC — a 60-year-old research firm — is rebuilding itself as an AI intelligence platform says something about where enterprise AI is heading. Every knowledge product, every data service, every business intelligence tool is working through the same transition: move from a destination you visit to an intelligence layer embedded in your workflows.

The $22.5 trillion projection and the 1 billion agents forecast are headline numbers, but the 42% ROI measurement gap is the most actionable insight for business leaders right now. Nearly half of organisations deploying AI cannot confidently answer the question: what is this actually worth?

That gap is not a technology problem. It is a strategy and measurement problem. It shows up most visibly in companies that jumped to implement AI tools before defining what success looks like — before mapping the workflows where AI can genuinely reduce cost or increase output, and before building the measurement infrastructure to track that impact over time.

The organisations that close this gap will have a meaningful advantage. Not because they will have better AI — the models are commoditising fast. But because they will know precisely which agent deployments are working, which need adjustment, and where the next investment should go.

That is what separates AI experimentation from AI as a business capability.

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Enterprise DNA put together a free field guide on exactly this: the full Claude ecosystem, Claude Code, and how to roll agents out without breaking things. Get the guide.