Emerging AI tools and systems, supported by the modern digital infrastructure that allows them to scale, have the potential to not only drive efficiency but also change how businesses interact with customers, deliver products and services, and compete in the market.
However, at a moment when AI is reshaping so many industries, I’ve continually found myself returning to the same conclusion: IT leaders are too focused on the technology.
For years, I’ve overseen applications, data, process transformation and IT infrastructure strategy for global enterprises. I led Kyndryl through its own transformation, tackling a decade’s worth of work in less than two years as we became “customer zero” for many of the same challenges our customers face. If past technology cycles have taught me anything, it’s that the number one reason transformations fail is people, not technology. People are complex, so organizations must be nuanced in their approaches to change.
I’m increasingly concerned that history is repeating itself — and at a time of accelerated and unprecedented change. In our 2025 Readiness Report, 95% of leaders told us they would change the way their organization implemented its cloud strategy if they had the chance. For many, their do-over would include investing more in people.
Today’s AI transformation provides that opportunity – one where leaders can take a more deliberate approach to workforce readiness.
Workforce readiness — not technology alone — will determine whether organizations realize the full value of AI.
In the AI era, too many leaders have been quick to focus on the specs of new tools while paying far less attention to how their workforce is organized and trained to use them effectively. No matter how much an organization invests in cutting-edge technology, transformation will stall if people aren’t ready to adapt. Passive or active resistance can quickly become entrenched, and the downstream effects of that disengagement have far-reaching implications for value creation.
Kyndryl's 2026 People Readiness Report suggests many organizations are confronting this reality head-on. Enterprise AI adoption increased dramatically in the last year, with more than half of organizations reporting that AI is broadly deployed. Yet workforce readiness decreased over the same period. That helps explain why few organizations are reporting high-value outcomes driven by their AI investments.
This disconnect also mirrors a longstanding lesson of economic theory, diffusion does not equate to productivity. Economists often distinguish between adoption at the extensive margin, or how many people have access to a technology, and the intensive margin: how deeply and productively that technology is used.
Electrification transformed factories not when engines were replaced but when plants were redesigned. PCs proliferated in offices, but the productivity payoff came when leaders rethought business processes. While AI tools may now be widely available across organizations, their application can still be shallow, inconsistent, ineffective or misaligned in ways that create more risk than value.
say their workforces are completely ready for AI — down 6 points from 2025
Given the current level of AI-driven change, technology leaders have a crucial role to play in helping build the systems, structures and support needed to help their workforces adopt AI responsibly, effectively and at scale. They can no longer operate in silos. Agentic AI demands a new level of enterprise collaboration as it transforms how work is accomplished, forcing organizations to grapple with how to evolve roles, skills and even career trajectories.
The rewards will be tremendous for those who get this right, as both industry analysts and outside research point to the same conclusion – workforce readiness, not technology alone, will determine the realization of AI value. Our People Readiness Report quantifies how much: those leading in workforce readiness are roughly 1.5 times more likely to report achieving their most desired outcomes from AI investment, such as efficiency gains, revenue growth and AI-enabled innovation. Over time, their advantage compounds: their readiness investment shortens the value horizon.
Our research also shows that these leaders follow a similar playbook. They are creating new roles, redesigning workflows for AI collaboration and sharing decision-making authority between AI and people. They’re carefully planning for redeployment, charting career transition pathways for employees and investing in upskilling. The most advanced organizations have an accurate skills inventory, a clear understanding of how to close skills gaps, and strategies for how people will become more important as AI handles more routine work.
That’s not to say this work is easy. Big ships, as they say, take a long time to turn, and large enterprises will need to embrace a fundamental mindset shift to overcome organizational inertia and compete with their nimbler counterparts. That’s part of why leaders have been so focused on technology investment. It’s easier to throw money at a problem than to reinvent company culture and methodically drive widespread and effective adoption of AI.
If I’m critical of my own profession, it’s because I know we’re more than capable of rising to this challenge and prioritizing our people through this transformation. But we must remember that even the best technology only accomplishes so much. The organizations that will be the most prepared for the future, and in turn most likely to achieve their goals and pull ahead of competitors, will also be those that put their people first.
