AI is deployed in 57% of enterprises, but only 11% have hit their top two 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. That is the headline figure from Kyndryl's second annual People Readiness Report, published June 25, 2026, which surveyed 1,100 senior business and technology leaders across eight countries. The problem is what sits on the other side of that deployment number.

Only 32% of those organizations have achieved at least one of their top two AI objectives. A mere 11% have hit both. Deployment is running far ahead of outcomes, and the data points to a consistent culprit: the workforce side of the equation is being systematically underinvested.

Confidence is falling as capital flows in

Worldwide AI spending is forecast to reach $2.52 trillion in 2026, a 44% year-over-year increase, according to Gartner research cited in Kyndryl's report. The money is going into tools and infrastructure. Human enablement is not keeping pace.

Kyndryl's findings make that gap concrete. Only 23% of business leaders now believe their workforce is fully prepared for AI, a six-point drop from 2025. Nearly four in five respondents agreed that the pace of AI development will outstrip their organization's workforce, governance, and operating models. Confidence is falling precisely when it should be growing.

The picture is starker at the individual employee level. 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. In a typical enterprise, more than 80% of the workforce lacks either the confidence or the clarity to integrate AI into daily work, even as leadership accelerates deployment.

What Pacesetters do differently

Kyndryl's report isolates a cohort it calls Pacesetters: roughly 9% of respondents that are generating measurable AI returns. These organizations share three operational behaviors. They redesign roles around AI rather than layering AI onto existing job structures. They implement structured change management so employees understand the new operating model and have guardrails in place. And they build workforce readiness deliberately, treating it as a foundational investment rather than an afterthought.

The performance differential is concrete and significant. According to Kyndryl, 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 compared to peers. They are also roughly twice as likely to have fully implemented AI governance across every dimension measured in the study.

The broader organizational picture shows how much ground most companies still need to cover. While 61% have redesigned roles to support AI adoption and 24% are creating new AI-focused management positions, only one-third have fully implemented employee training programs focused on working alongside AI tools. Another third have clear policies defining which decisions AI can and cannot make, and just 27% are operating a registry and monitoring capability for their AI systems, per the Kyndryl report.

Governance gaps create operational risk

The governance shortfall is becoming a near-term operational problem, not a future concern. Kyndryl's data shows 81% of organizations expect AI agents to make impactful business decisions within the next year. Only 25% completely trust AI systems operating without human oversight today. That trust gap is not just cultural; for operations, finance, and supply chain teams deploying agentic AI, it is a live risk in any workflow where automated decisions carry downstream consequences.

Kim Basile, CIO at Kyndryl, noted in the report's release that organizations investing in their people, through rethinking roles, dedicating resources to upskilling, and guiding employees through change, are achieving positive outcomes at a much higher rate. Mark Paulek, Kyndryl's Chief Human Resources Officer, added that the leaders pulling ahead are aligning skills, roles, and decision-making with how work is actually changing, and that when employees understand their role in the new system, trust and performance scale together.

The skills pipeline is tightening as well. Half of leaders surveyed, 52%, say it has become more challenging to find employees with the right skills to advance their AI strategy, according to the Kyndryl report. That figure points toward a procurement and talent implication that goes beyond training budgets: organizations that have not built internal AI fluency now face both a vendor dependency risk and a longer runway to ROI than their Pacesetter peers.

What this means for your team

• Audit your training coverage before your next deployment cycle: with only one-third of enterprises reporting fully implemented AI training programs, gaps in workforce enablement are the most common reason deployment fails to translate into outcomes.
• Treat governance as an enabler of trust, not a compliance checkbox: Kyndryl's data shows organizations with stronger governance report higher workforce trust in AI strategy, and high-trust organizations are significantly more likely to report transformative outcomes.
• Evaluate new AI management roles now: 24% of organizations are already creating dedicated AI management positions; waiting until deployment is mature means building governance and oversight capacity under pressure.
• Pressure-test your agentic AI plans against your current oversight model: 81% of organizations expect AI agents to make impactful decisions within a year, but only 25% fully trust AI without human oversight, making oversight architecture a critical gap to close before autonomous systems go live.