At the World Economic Forum’s annual meeting in Davos this year, AI was everywhere. From panels to private sessions, CEO interviews, and policy debates, it dominated the agenda. Yet, the central message was not what many expected. Leaders repeatedly cautioned that while AI adoption is accelerating, productivity gains remain uneven, fragile, and difficult to scale.
The question is no longer what AI can do but whether organizations have the human systems required to make AI productive over time. Without deliberate investment in human capability, work design, and accountability, AI can create the illusion of progress while actually weakening performance.
In short, Davos delivered a clear warning on AI, and the only way through the next phase is by investing in people.
Across industries, employees are using generative AI to draft content, analyze data, write code, and accelerate routine work. Adoption is widespread. On the surface, it looks like progress. Work is accomplished faster. Output is increased. Cycle times are shortened.
Beneath the dashboards, a different pattern is emerging. Leaders report more activity but thinner decisions. Managers review growing volumes of output while struggling to explain where value is actually created. Employees produce more, but with less clarity, ownership, and learning.
What is largely missing from these conversations is a harder truth. Faster output does not automatically mean better outcomes. When lower-quality work scales faster, error rates rise, review burdens grow, and downstream decisions suffer. Over time, these effects do not merely erode productivity; they create revenue risk. Speed that increases rework, misalignment, or customer-facing errors can quietly undermine the very value AI is meant to deliver.
Economists are also tracking this pattern. Researchers at Stanford’s Digital Economy Lab find that AI adoption is generating early, localized productivity gains among a narrow set of firms and workers, while broad, economy-wide productivity growth remains limited. Others point to a familiar dynamic known as the productivity J-curve. When general-purpose technologies are introduced, performance often stagnates or declines initially as organizations deploy tools faster than they redesign work, decision rights, and management practices.
At present, organizations are in the AI version of this familiar pattern, which has been intensified by generative systems capable of technological scale we’ve never seen before.
The AI Productivity Paradox
This tension has a name: the AI productivity paradox. AI collapses the cost of production while straining the human systems that make work effective. Output scales instantly. Judgment does not. Learning, governance, and accountability lag behind.
Unchecked, AI adoption creates a dangerous mirage. Because AI improves what organizations already measure, early gains look real and reassuring, while the downstream costs to quality, decision-making, and revenue remain largely invisible—until they don’t.
The paradox is not that AI fails to work. It is that performance is now constrained less by technology and more by human readiness. What’s more, AI amplifies existing gaps and problems within systems. When work design is weak, AI accelerates the weakness.
Why This Is the CHRO’s Moment
Generative AI is not just another tool layered onto existing workflows. It operates inside cognitive processes that were once exclusively human, shaping how work is written, reasoned through, evaluated, and decided. That distinction matters because it changes not just how fast work moves, but how judgment and authority function inside organizations.
Employees who know how to interrogate, refine, and override AI output gain leverage and influence. Those who do not know how to do this carry more risk when AI is wrong, often without realizing it. Over time, speed-first adoption quietly shifts authority away from people and into systems, weakening judgment, blurring accountability, and slowing learning even as output increases.
The reality of the AI productivity paradox is that AI strategy has become inseparable from workforce strategy. Organizations that convert AI speed into sustained performance are not optimizing tools—rather, they are carefully redesigning human systems, including how work is defined, how decisions are made, who owns outcomes, and how learning is protected as work accelerates.
That places CHROs at the center of the challenge. They see where judgment erodes, accountability drifts, and speed outpaces sense-making. The task is not to slow AI but to sequence it so that AI strengthens human capability. CHROs must embrace their role as workforce architects, and quickly, before AI-generated “workslop” becomes the default mode of operation.
What Comes Next
Davos sounded the alarm on AI. Speed alone will never deliver AI’s promise. Organizations that treat AI as a technical rollout will not experience the success they envision, but those that redesign human systems will build durable advantage. The future of productivity belongs to leaders who keep humans firmly in the loop.
Join our upcoming webinar to unpack the AI productivity paradox and what HR leaders must do next.
