Why workforce readiness and company culture may be AI’s biggest risks.
By Mark Paulek
For years, organizations have focused on reducing technical debt by modernizing infrastructure, upgrading systems, and building the digital foundations needed to compete. As artificial intelligence becomes a business imperative, organizations face another challenge that may prove just as pressing: Ensuring their people are prepared to keep pace.
Kyndryl’s 2026 People Readiness Report found that 57% of organizations have broadly deployed AI or embedded it into core business processes, yet only 23% believe their workforce is fully ready to leverage it. Just 25% believe their organizational culture is prepared to successfully adopt AI. And workforce readiness declined year-over-year despite the rapid expansion of AI initiatives. Meanwhile, 79% of leaders worry that the speed of AI advancement will outpace their workforce, governance, and operating models.
The question is straightforward: If organizations are making progress with AI, why are so few confident their people and culture are ready?
The answer lies in how many organizations approached the first phase of AI adoption. The emphasis was understandably on technology deployment: identifying use cases, acquiring new capabilities, and scaling AI across the enterprise. In many cases, that effort succeeded. What often received less attention were the human factors that determine whether AI creates lasting business value: skills, trust, accountability, and change management.
As AI became increasingly embedded into day-to-day operations, many employees were left navigating uncertainty about how work would change, what new skills would be required, and what role they would play in an AI-enabled future. Technology advanced, but workforce readiness lagged.
These organizational readiness gaps emerge when a company’s people are not brought along the journey. They grow when deployment is prioritized over preparedness and when organizations invest heavily in technology without making similar investments in workforce transformation. Over time, employees become less confident navigating changes, they adopt new tools inconsistently, and their trust erodes. The result is an organization with advanced technology that struggles to realize its full potential because its people are not equipped to use it effectively.
Just 25% believe their organizational culture is prepared to successfully adopt AI.
One reason these workforce readiness challenges often remain hidden is that executive enthusiasm for AI can obscure employee skepticism.
For executives, the business case for AI is compelling. Leaders see opportunities to improve productivity and innovation. But employees often experience AI differently. They are confronted with changing workflows, evolving expectations, and growing demands to learn new skills. Naturally, they ask questions about career development, job evolution, accountability, and the pace of change itself. Without clear answers, they continue to lose trust.
Employee trust is increasingly becoming one of the most important predictors of AI success. Organizations with stronger governance and higher workforce trust are significantly more likely to achieve transformative outcomes from their AI investments. Trust creates the conditions for experimentation, adoption, and innovation. Employees who understand how AI is being used, and who believe leadership is investing in their success, are more likely to embrace new ways of working.
Kyndryl’s research identified a small group of organizations, representing just 9% of those surveyed, that consistently outperform their peers on AI outcomes. These organizations are more likely to achieve AI-related revenue growth and innovation gains. What separates them is not access to better technology. Rather, they have taken deliberate steps to prepare their people for change. They redesign roles around AI, implement formal change-management programs, and build workforces and cultures that are ready to leverage AI effectively. Those capabilities create the foundation for turning AI investments into business value.
For HR leaders, the implication is clear: Preparing people for AI requires the same rigor and sustained investment that organizations have historically applied to reducing technical debt.
That begins with treating skills as real-time assets rather than static records. Organizations need visibility into how work is evolving, which skills are growing in importance, and where talent can be redeployed as business needs change. Workforce planning can no longer operate on annual timelines when AI is reshaping jobs and workflows in real time.
At Kyndryl, that philosophy informs an approach called “AIR: Anticipate, Inventory, Reskill and Redeploy.” The framework focuses on understanding how AI will reshape work, maintaining a dynamic view of workforce capabilities, and creating pathways for employees to build skills aligned with emerging business needs. The objective is not only to train people on new technologies, but to help them evolve alongside those technologies.
Technical debt remains an important challenge. But as AI adoption accelerates, an organization’s ability to prepare its people for change may ultimately determine whether its AI investments deliver meaningful business value.
Mark Paulek is chief human resources officer for Kyndryl.
