Only 23% of organizations believe their workforce is fully ready for artificial intelligence (AI), down six points from the previous year, even as companies accelerate AI adoption and spending reaches $2.52 trillion globally. A new report from Kyndryl, based on interviews with 1,100 senior business and technology leaders across eight countries, reveals a troubling disconnect: companies are embedding AI into their core operations faster than they can prepare their employees to work alongside it.

The findings paint a picture of enterprise AI adoption in 2026 that looks less like a smooth transformation and more like a race against the clock. While 57% of organizations now say AI is embedded in core business processes or deployed broadly across their enterprise, up from 35% the previous year, the human side of the equation is falling further behind. This gap between technological deployment and workforce readiness is emerging as the real bottleneck for AI success.

What's Driving the Workforce Readiness Crisis?

The challenge isn't that companies don't understand the problem. In fact, 79% of leaders agree that the speed of AI will outpace their organization's workforce, governance, and operating models. Yet knowing the problem exists and solving it are two different things. The report identifies several concrete barriers that organizations are struggling to overcome.

• Skills Gaps: Half of all leaders (52%) say it has become more challenging to find employees with the right skills to advance their AI strategy, and only one-third have fully implemented training programs focused on helping employees effectively collaborate with AI tools.
• Role Redesign Lag: While 61% of organizations have already redesigned roles for AI, and 24% are creating new positions focused on AI management, the pace of change is outstripping the ability to communicate new expectations clearly to employees.
• Trust and Governance Gaps: Just 33% of organizations have clear policies on which decisions AI can and cannot make, and only 27% are using a registry to monitor all their AI systems across the enterprise.

The trust issue is particularly acute when it comes to autonomous AI agents. Eighty-one percent of organizations expect AI agents to make impactful decisions for their business within the next year, but only 25% completely trust AI systems operating without human oversight. This mismatch between expectation and confidence suggests that many companies are deploying AI agents before they've built the governance frameworks or workforce understanding needed to use them safely.

Who's Getting It Right, and What Are They Doing Differently?

The Kyndryl report identifies a small group of organizations, representing just 9% of those surveyed, that the researchers call "Pacesetters." These companies are achieving significantly better outcomes from their AI investments, and their approach offers a roadmap for others. The Pacesetters share three core practices that distinguish them from the rest of the field.

"The data shows that the organizations investing in people, whether it's rethinking roles and workflows, dedicating resources for upskilling and retraining, or guiding employees through change, are experiencing positive outcomes at a much higher rate," said Kim Basile, Chief Information Officer at Kyndryl.

Kim Basile, Chief Information Officer at Kyndryl

The three practices that define Pacesetters are straightforward in concept but demanding in execution. First, they redesign roles around AI, ensuring that job descriptions and responsibilities reflect how work actually changes when AI enters the picture. Second, they implement deliberate change management so that employees understand their new operating model and have guardrails in place. Third, they actively build workforce readiness through training, upskilling, and clear communication about how AI will affect different roles. As they implement these practices, they simultaneously build governance frameworks that create trust in AI systems.

The results speak for themselves. Pacesetters are roughly twice as likely to have fully implemented every governance dimension measured in the study. More importantly, they report tangible business outcomes: they are 1.5 times more likely to achieve AI-related revenue growth and 1.6 times more likely to report better innovation for products and services. These aren't marginal improvements; they represent the difference between AI becoming a strategic asset and AI becoming a costly experiment.

How to Close the AI Readiness Gap in Your Organization

For companies looking to move beyond the current state of AI adoption, the Kyndryl findings suggest a clear set of priorities. Rather than focusing solely on deploying more AI tools or expanding AI use cases, leaders should focus on the foundational work of preparing their workforce and building trust in AI systems.

• Redesign Roles Intentionally: Don't assume that existing job descriptions will work in an AI-enabled environment. Proactively redesign roles to reflect how AI will change daily work, and create new positions focused on AI management and governance to ensure oversight and accountability.
• Invest in Change Management and Communication: Implement structured change management programs that help employees understand not just what AI tools do, but how their role and responsibilities are changing. Provide clear guardrails and decision-making frameworks so employees know what they're responsible for and what AI handles.
• Build Governance and Trust Simultaneously: Develop clear policies on which decisions AI can and cannot make, implement monitoring systems for all AI deployments, and communicate these policies transparently to your workforce. Organizations with stronger governance report higher levels of workforce trust in AI strategy and execution.
• Prioritize Training Over Tool Deployment: Before rolling out new AI tools, ensure that one-third or more of your workforce has completed training programs focused on effective collaboration with AI. This is not a one-time effort but an ongoing commitment as AI capabilities evolve.

The Real Cost of Ignoring Workforce Readiness

The stakes for getting this wrong are significant. The report reveals that only 32% of organizations have achieved at least one of their top two AI goals, and just 11% have achieved both. This suggests that the majority of companies investing heavily in AI are falling short of their own expectations. The common thread among those falling short is insufficient attention to workforce readiness and change management.

"The leaders pulling ahead are aligning skills, roles and decision-making with how work is actually changing. When people understand their role in that system, trust and performance scale together," noted Mark Paulek, Chief Human Resources Officer at Kyndryl.

Mark Paulek, Chief Human Resources Officer at Kyndryl

As global spending on AI continues to accelerate, the window for getting workforce readiness right is narrowing. Companies that treat AI adoption as purely a technology challenge will likely find themselves among the 68% that fail to achieve their primary AI goals. Those that recognize AI adoption as fundamentally a people challenge, and invest accordingly, are positioning themselves to capture the real value that AI can deliver.