“Speed without confidence is dangerous. Confidence without speed is irrelevant.” — Lorenzo Larini, CEO, IDC

AI investment is accelerating at a pace not seen since the internet reshaped markets in 1996. Global IT spending is growing at 14% annually, and by 2029, IDC expects enterprises and service providers to commit $1.6 trillion to AI infrastructure worldwide. The ambition is real. The money is real.

But something is widening beneath the surface. There is a growing gap between the speed at which AI decisions need to be made and the quality of intelligence those decisions are grounded in. Organizations that close that gap will pull ahead. Those that don’t will spend more to fall further behind.

This is the intelligence gap: the widening distance between the speed at which AI-driven decisions must be made and the quality of verified, timely intelligence available to ground them. And the data tells a sharper story than most organizations realize.

The data storm is already here

Start with the raw scale of the problem. In 2025, enterprises created 6.9 petabytes of data every second. By 2029, agentic AI is expected to push that figure to 17.1 petabytes per second, a 2.5x increase in four years.

The volume isn’t the issue. The issue is what happens to decision-making when intelligence can’t keep pace with the data that should be informing it. Market conditions shift. Competitive landscapes change. Research that was current in January may be strategically stale by June. And yet most enterprises are still relying on intelligence delivered through portals they open infrequently, PDFs that don’t update, and AI tools that pull from sources they can’t verify.

IDC’s own field conversations with dozens of enterprises across financial services, healthcare, pharma, and manufacturing confirm the pattern: organizations are piloting and deploying AI faster than they are building the intelligence infrastructure to support good decisions at scale.

That’s not an adoption problem. That’s an intelligence gap.

42% of organizations can’t measure what they’re getting

Here’s the number that should be commanding more attention in every AI strategy conversation: 42% of organizations worldwide said in a recent report assessing the ROI of their AI and digital investments is difficult or even impossible.

That figure isn’t a measurement problem. It’s a symptom of a deeper structural issue. Most organizations evaluate AI value through a narrow financial lens (head count offsets, cost efficiency ratios, per-query economics) while leaving eight other dimensions of business value (customer experience, resilience, time to market, innovation, and more) systematically unmeasured and undervalued in their investment cases.

The consequence: enterprises are underreporting the business case for their own AI investments while simultaneously losing confidence in what AI is actually telling them.

Agentic AI makes the measurement problem harder, not easier. Value is nonlinear. Costs are dynamic. Benefits compound across functions and emerge over iterations, not from a single project deployment. That means the organizations waiting for a clean ROI calculation before committing fully to AI intelligence infrastructure are waiting for a number that the current approach to measurement can’t produce.

The credibility crisis is structural, not anecdotal

The frustration with AI hallucinations has become a familiar story. But the more consequential shift is happening at a deeper level: organizations are recognizing that their AI is only as trustworthy as the data it draws from, and most of them can’t verify where that data comes from.

IDC’s April 2026 research found that enterprises are actively restructuring their approach to AI governance, moving from model-centric oversight toward data-centric risk management, where validation, lineage tracking, and source credibility are the primary controls.

Non-digital-native organizations, which represent the majority of enterprise buyers, are responding by tightening AI inputs to structured, internally validated data sets. That reduces hallucination risk but also limits the scope of AI-driven insight. The same organizations shrinking their AI’s aperture for safety reasons are competing against organizations that have found ways to bring trusted external intelligence into the loop without sacrificing rigor.

The answer isn’t to trust AI less. It’s to ground AI in better sources and to make every answer traceable back to its origin. Speed without traceability isn’t a competitive advantage. It’s a liability that compounds every time an answer has to be defended in a boardroom.

Governance is an afterthought for most, and that’s a compounding risk

The gap between governance intent and execution is where risk compounds. As agents take on broader decision-making authority across more functions, the question of what those agents are drawing from becomes a board-level concern, not just an IT one.

Without a reliable, traceable intelligence layer, governance is a framework without a foundation.

What the stakes look like when the gap closes, and when it doesn’t

IDC’s field research puts a concrete number on the cost of the intelligence gap in action. A multinational financial services firm incurred a $150 million compliance fine due to failed processes across 11 million customer accounts and needed to conduct full due diligence on all of them within 24 months. By deploying a data-driven platform with agentic AI to automate the workflow, the firm cleared 4 million low-to-medium-risk cases automatically and achieved $120 million in cost savings compared with their prior approach.

That’s not a productivity story. That’s what happens when intelligence is operationalized at scale: the right data, verified and traceable, embedded into the decisions that need to be made.

But IDC’s conversations with those same organizations also surfaced the failure mode. Ten of the 33 enterprises interviewed flagged overreliance on AI outputs without sufficient human oversight as a real and active risk. When you can’t see where an answer came from, you can’t know when to trust it, and you can’t defend it when it’s challenged.

The gap between making fast decisions and making confident ones isn’t closed by deploying more AI. It’s closed by grounding AI in intelligence that holds up.

IDC’s field research shows the divergence clearly. Organizations that have successfully embedded AI into workflows are realizing measurable gains: faster decisions, higher accuracy, improved risk management, and stronger business outcomes. Those still struggling share the same profile: fragmented data, unverifiable AI outputs, and governance frameworks that exist on paper but don’t connect to execution.

Strategy and execution are out of sync, and the gap is widest at exactly the point where intelligence quality matters most. The organizations on the right side of that divide share a common characteristic. The differentiator is not investment level. It’s the depth of integration between trusted intelligence and the decisions that matter.

The organizations closing the gap are building intelligence infrastructure that does three things: it delivers answers proactively rather than reactively; it embeds into the workflows where decisions actually happen; and it makes every insight traceable to a verified source.

Closing the gap: Why IDC Quanta exists

IDC Quanta is IDC’s technology intelligence fabric, built to deliver verified, sourced market intelligence directly into the tools and workflows where decisions are made.

Most intelligence tools ask you to go looking. You open a portal. You search. You read. You synthesize. Then you decide, often hours or days after the decision needed to be made. IDC Quanta reverses that model. Intelligence arrives on your schedule, grounded in IDC’s proprietary research and data, traceable to its source and date, and embedded in the tools you already use rather than a separate system you have to remember to open.

For organizations navigating the credibility crisis, Quanta addresses it at the source: every response is verified against IDC’s proprietary data through a multi-agent validation system, with a reasoning panel that shows the scope, sources, and assumptions behind each answer.

For organizations trying to move faster without sacrificing rigor, Quanta delivers recurring intelligence on your priority topics so you are informed before you need to ask.

IDC CEO Lorenzo Larini put the challenge plainly: “Speed without confidence is dangerous. Confidence without speed is irrelevant.”

The intelligence gap is real. The data is unambiguous. And the organizations that close it first will be the ones setting the pace, not chasing it.