Presented by JumpCloud

The organizations losing confidence in AI are the ones most likely to get it right.

Six months ago, 40% of IT leaders described their organizations as mature in AI deployment. Today that number is 23%. Before you read that as a setback, consider what it actually reflects.

We recently surveyed 800 IT leaders across the U.S. and U.K. for our Q3 2026 trends report, and the data tells a consistent story: the organizations revising their self-assessment downward are overwhelmingly the ones that have moved AI agents from pilots into production. They’re not losing faith in AI. They’re running into the problems that only show up when agents are doing real work in real systems, and they’re being honest about what they found.

That kind of honesty is harder to come by than it sounds, and it matters more than the confidence number itself.

Deployment was the easy part

84% of organizations plan to expand AI use in IT operations over the next 6 to 24 months, so the drop in confidence isn’t a retreat. What it reflects is a more accurate picture of what production actually requires.

In a pilot, an AI agent does one thing in a controlled setting. In production, it accesses real systems, makes decisions that affect real workflows, and operates continuously, often without a human in the loop. The governance infrastructure that entails is materially different from what it took to get the pilot working. Most organizations built enough to ship. Fewer built enough to scale.

The IT leaders revising their self-assessment are confronting questions they didn’t have to ask at the pilot stage: Can we see every agent running in our environment? Do we know what each one can access? If an agent behaved unexpectedly last week, how long would it take to find out? For most organizations, at least one of those answers is uncomfortable.

The gap between perception and reality is where risk accumulates

The graphic above captures the structural problem. Across confidence, governance, and autonomy, the same pattern holds: deployment is moving faster than the controls built around it.

The organizations that have closed this gap share specific characteristics. They’ve consolidated their IT environments rather than adding tools to solve each new problem, because every additional platform creates another place where agent identity, access, and accountability can go unmanaged. They treat AI agents as governed identities rather than tolerated shadow processes. And they measure what AI actually produces, not just what it deploys.

The payoff is tangible. Organizations in the top tier of our maturity model are five times more likely to report no barriers to expanding their AI agents than the average organization. They are not more cautious about AI. They are more confident in it, because they built the foundation that makes confidence earned rather than assumed.

The governance gap has a specific shape

The hardest problem in enterprise AI right now is not capability. It is accountability, and the data makes the specific failure point clear: non-human identity governance is the least adopted AI security practice we measured, in place at just 21% of organizations.

Non-human identities now outnumber human users in 83% of organizations, and that population is growing fast. Yet most of those identities exist without the governance structures that every human employee has as a matter of course: no formal record, no named owner, no defined scope of access, no offboarding process when their purpose expires. They keep running. They keep accessing systems. They keep accumulating permissions. We call these Zombie Agents, and they are the service account problem of the AI era, operating at machine speed and in every department.

The accountability gap is where real risk lives. When a human employee takes an action, there is an implicit accountability chain. When an autonomous agent takes an action, that chain breaks unless it has been deliberately engineered. Most organizations have not yet engineered it, and the gap between the autonomy agents are being granted and the oversight structures in place to manage them is widening every month.

What the confidence drop is actually telling us

When AI maturity confidence was uniformly high across the market, that was worth worrying about. It meant most organizations hadn’t yet run into the hard parts. A selective drop, concentrated among organizations actively running agents in production, means the market is developing a more accurate picture of what AI operations genuinely require.

The organizations recalibrating are doing the work that makes long-term AI adoption possible: building identity infrastructure that covers agents alongside humans and devices, unifying the environments where governance needs to apply, and measuring outcomes rather than just counting deployments. They haven’t lowered their ambitions for AI. They have raised their standards for what it means to run it responsibly.

84% of organizations plan to expand AI use over the next two years. The ones that will do it well are honest enough, right now, to admit what they haven’t yet built.

JumpCloud’s Q3 2026 AI Readiness Research report (n\=800 IT leaders, U.S. + U.K.) is available here. The report covers AI agent deployment stages, identity governance gaps, IT unification benchmarks, and budget realism across mid-market and enterprise organizations.

Rajat Bhargava is CEO and Co-founder at JumpCloud.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.