Kyndryl's 2026 People Readiness Report: AI deployment hit 57% of enterprises, but only 11% are hitting their goals

Fifty-seven percent of enterprises now have AI embedded in core business processes or deployed broadly across their organizations, up from 35% just one year ago. Yet only 11% of those same organizations have achieved both of their top two AI objectives. That gap between deployment and outcomes is the central finding of Kyndryl's second annual People Readiness Report, released in late June 2026, which drew on a global survey of 1,100 senior business and technology leaders across eight countries.

The report arrives as AI spending reaches a historic peak. Worldwide AI investment is forecast to total $2.52 trillion in 2026, a 44% year-over-year increase, according to Gartner research cited in the Kyndryl report. Capital is moving fast into tools and infrastructure. The workforce side is not keeping pace.

Confidence is declining as deployment accelerates

Kyndryl's data shows that workforce preparedness has moved in the wrong direction over the past year. Only 23% of business leaders now say their workforce is fully prepared for AI, down six points from 2025, according to the PR Newswire release of the report. Nearly four in five respondents agreed that the pace of AI development will outstrip their organization's workforce, governance, and operating models.

The employee-level picture is even more pronounced. The Achievers Workforce Institute's seventh annual State of Recognition Report, cited by MarketScale, found that just 19% of workers feel confident using AI tools, and only 18% feel supported in adapting to them. That means the vast majority of the workforce in a typical enterprise lacks both the confidence and the clarity to integrate AI into daily work, even as leadership accelerates deployment.

Only 32% of organizations have achieved at least one of their top two AI goals, and the 11% that have achieved both represent a narrow band of organizations that are doing something structurally different, per Kyndryl's findings. The report flags specific execution gaps that explain the underperformance: just one-third of organizations 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 the top 9% are doing that the rest are not

Kyndryl's report identifies a cohort it calls Pacesetters, comprising roughly 9% of survey respondents, that are converting AI investment into measurable business results. These organizations share three operational behaviors: they redesign roles around AI rather than adding AI capabilities to unchanged job structures, they implement structured change management so employees understand the new operating model, and they invest deliberately in workforce readiness before scaling deployment.

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 products and services, according to Kyndryl's data. They are also approximately twice as likely to have fully implemented every governance dimension the study measured compared to peers.

Sixty-one percent of all organizations surveyed have already redesigned roles to support AI adoption, and 24% are creating new positions focused on AI management, per the report. The disconnect is in training: only a third have fully implemented programs to help employees work effectively alongside AI tools, which means role redesign is happening faster than the skills development needed to make it stick.

Governance gaps are creating a trust problem at the worst possible time

The timing of the readiness shortfall is particularly acute because autonomous AI agents are arriving in the enterprise now. Kyndryl's survey found that 81% of organizations expect AI agents to be making impactful decisions within the next year. Yet only 25% say they completely trust AI systems operating without human oversight, according to the PR Newswire release. That trust gap sits directly in the path of any agentic AI rollout.

Only 27% of organizations are using a registry and monitoring capabilities for all their AI systems, per Kyndryl's findings. The report draws a direct line between governance investment and workforce trust: organizations with stronger governance frameworks report higher employee trust in AI strategy, and those high-trust organizations are significantly more likely to report transformative outcomes from their AI investments.

Skills sourcing is becoming harder in parallel. Half of the leaders surveyed, 52%, say it has become more challenging to find employees with the right skills to advance their AI strategy, according to Kyndryl's report. That pressure points toward internal upskilling as the more reliable path forward, given that the external talent market is tightening at the same time enterprise demand is rising.

What this means for your team

• Audit training coverage before scaling deployment: if your organization is in the two-thirds that have not fully implemented AI collaboration training, role redesign and tool rollout are likely running ahead of employee capability.
• Establish governance before agents go live: with 81% of enterprises expecting agentic AI decisions within the year, documenting which decisions AI can and cannot make independently is an immediate operational priority, not a future-state exercise.
• Benchmark against Pacesetter behaviors: Kyndryl's three markers, role redesign, structured change management, and workforce readiness investment, are measurable. Assess where your organization sits on each before the next AI budget cycle.
• Treat the skills gap as a sourcing constraint: with 52% of leaders reporting difficulty hiring AI-ready talent, internal reskilling programs are likely a faster and more reliable path to capability than external recruiting alone.