Kyndryl's 2026 People Readiness Report: AI is spreading fast, but most workforces aren't ready

More than half of global enterprises now have artificial intelligence running inside core business processes, yet the share of business leaders who believe their workforce is fully prepared to use it has actually fallen. That is the central finding of Kyndryl's 2026 People Readiness Report, released this month, which surveyed 1,100 senior business and technology leaders across eight countries.

Deployment is running ahead of outcomes

The jump from 35% to 57% of organizations running AI broadly across the enterprise is notable on its own. What makes it operationally significant is the outcomes data sitting alongside it: only 32% of respondents have achieved at least one of their top two AI objectives, and just 11% have achieved both. Heavy investment is not translating into results at a comparable rate.

Kyndryl's report also references Gartner forecasts that put worldwide AI spending at $2.52 trillion in 2026, a 44% year-over-year increase. At that spending scale, the gap between adoption and realized value becomes a material financial exposure for operations and finance leaders, not just a technology concern.

Workforce readiness is moving in the wrong direction

Only 23% of business leaders rate their workforce as fully prepared for AI, down six percentage points from the prior year's survey. That decline is happening as deployment accelerates, which means the readiness deficit is widening, not closing. A full 79% of respondents agreed that the pace of AI development will outrun their organization's workforce, governance, and operating models.

The skills supply is also tightening. Fifty-two percent of organizations report that finding employees with the right AI capabilities has become harder. At the same time, only one-third have fully implemented employee training programs focused on working alongside AI tools, and only 33% have established clear policies defining which decisions AI can and cannot make.

What 'Pacesetters' do differently

Kyndryl identifies a cohort it calls Pacesetters: nine percent of respondents whose approach combines role redesign, structured change management, governance guardrails, and workforce investment simultaneously rather than sequentially. These organizations are approximately twice as likely to have fully implemented AI governance across every measured dimension the report tracks.

The performance differential is concrete. Pacesetters are 1.5 times more likely to achieve AI-related revenue growth and 1.6 times more likely to report improved innovation in their products and services, according to Kyndryl's analysis. The report's conclusion is that the separating factor is not the AI tooling itself but the organizational work done around it.

Agentic AI adds a new governance pressure

A distinct concern in this year's report is autonomous AI. Eighty-one percent of organizations expect AI agents to be making impactful business decisions within the next year. Yet only 25% currently say they have complete trust in AI systems operating without human oversight. For operations and compliance teams, that gap between expected deployment timelines and actual confidence levels is a governance problem that needs to be solved before, not after, these systems go live.

On the structural side, 61% of surveyed organizations have already redesigned roles to accommodate AI, and 24% are creating new AI-focused management roles. Those numbers reflect real headcount and job architecture decisions that HR, operations, and IT leaders are working through right now.

What this means for your team

• Audit your objectives gap: if your organization is in the 68% that has not achieved its primary AI objective, map where deployment ended and organizational change work did not begin. Tools without redesigned workflows rarely produce the outcomes in the business case.
• Pressure-test your governance coverage: only 27% of respondents have registries and monitoring across all AI systems. Before autonomous agents are deployed, establish decision-rights policies and an audit mechanism for every model in production.
• Close the training gap before the skills market tightens further: with 52% of organizations reporting AI talent is harder to find, internal upskilling is increasingly the faster path. The report links fully implemented training programs to higher trust and better outcomes, making this a near-term priority, not a future one.
• Evaluate agentic AI readiness separately: the 81% expecting impactful AI-agent decisions within 12 months should treat autonomous system governance as its own workstream, distinct from conventional AI tool rollout.