Designing the Thinking Organization

If the CHRO doesn't take the lead on defining how people put AI to work, the CTO, CFO, or COO will. And in much of the market, they already have. Every organization has access to the same AI models. What they cannot rent is critical thinking, judgment, client relationships, and the creative work the technology cannot replicate. Growth accrues in the organizations that decide, deliberately, which human capabilities offer competitive advantage and then protect them in the design of work.

Every workflow redesign is an opportunity to determine where people add the most value and answers the question: what should people own, and why?

The C-suite can already see where it’s headed: 77% of CEOs say talent and technology leadership roles are converging. Enterprises can no longer design human and technological capability separately. Protecting what is inherently human means owning the human-capability agenda in every room where AI’s role is being decided.

Employee confidence in AI-enabled decisions is rising in 63% of organizations where the CHRO shares the ownership of what remains human-led, versus 19% where they do not. A gap that wide isn’t about the technology. It’s the measurable difference strong CHRO ownership makes.

Work is changing faster than organizations are redesigning it. More than half—52%—of employees say the tasks they are assigned have changed over the past year as AI reshapes day-to-day responsibilities. Yet organizations deliberately designing work remain in the minority. In many organizations, work is evolving through day-to-day adaptation rather than intentional decisions about how people and AI should work together. Thinking Organizations close that gap by treating work redesign and workforce recomposition as continuous disciplines.

As AI distributes work across people and systems, 36% of executives say unclear accountability complicates deployment. Work redesign must make ownership, validation, exception handling, escalation, and override explicit within the workflow. That design difference changes how employees experience AI. Where judgment is built into work, 62% of CHROs report rising employee confidence in AI-enabled decisions; where it is not, 57% report confidence declining. Same workforce, different work design, opposite outcomes.

The redesign should extend to the workforce itself—recomposing roles, teams, and the structure of work so the organization can execute the new workflow consistently at scale. Organizations that manage recomposition as a continuous discipline—rather than a one-off exercise—report revenue uplift of 4% versus 2% for those who do not.

Enterprises are not ignoring the skills problem: 80% have a reskilling roadmap for their workforce to collaborate with technology. But a roadmap addresses skill gaps—the skills people need to use new AI tools. It does nothing about skill erosion: the skills people already have, that are quietly degrading as AI takes on more of the thinking. A skill gap gets funded. Skill erosion goes unmanaged.

Executives and employees agree on much of the foundation AI-enabled work requires. Both place critical thinking and problem framing near the top, at 57% and 49%, respectively. More concerning is that both groups place creativity near the bottom—even though it is essential to turning generic AI output into something distinctive.

The sharpest divide between executives and employees concerns the skills required to evaluate AI outputs. Seventy-one percent of executives prioritize the ability to supervise, validate, or override AI outputs, compared with 38% of employees.

Employees recognize they must adapt to AI but may be underestimating the evaluative skills organizations expect them to bring to it.

Culture shows up in what the organization recognizes and rewards, how AI-freed capacity is used, and whether employees feel safe challenging AI. Thinking Organizations build the cultural capabilities that turn AI-created capacity into growth, rather than productivity alone. Most organizations have not made reinvestment of AI savings the default. Only 42% primarily direct AI productivity gains to innovation or reskilling. The rest either split them between savings and reinvestment, take them as cost reduction or profit, or lack a clear path from productivity to opportunity.

AI-funded productivity is not the same as workforce strength. An enterprise can post impressive efficiency numbers while the workforce within it weakens. The difference comes down to what happens to the gains.

The answer is a cultural decision before it is a financial one—and it shapes employee experience. Without an explicit plan, it is likely to be absorbed by additional work or captured as cost savings. CHROs can instead direct it toward coaching, development, internal mobility, stretch assignments, and innovation. That choice sends its own cultural signal, showing employees whether AI-created capacity will expand their opportunities to learn, contribute, and grow.

HR is expected to guide the enterprise’s workforce transformation—yet has limited experience applying AI within its own walls. The function charged with leading the change has barely started its own. It isn’t for lack of awareness. CHROs’ top personal development priority—cited by 71% and more than any other leadership capability—is digital and AI fluency. But personal investment hasn’t yet translated into adoption across the function.

Leading CHROs are resolving this directly: making HR the first environment to design, test, and refine AI-enabled work. This proving ground serves a second purpose as well. By running its own redesign, HR shows the organization what converting freed hours into new capability actually looks like, in the one function every employee can see.

HR’s risk discipline is why it should go first. The function sits closest to the enterprise’s highest-trust decisions: sensitive workforce data around hiring, promotion, pay, and performance. If AI cannot be made trustworthy in HR—where privacy, fairness, and employee confidence matter most—the rest of the enterprise will not trust it at scale. Organizations with mature HR AI capability—governance, architecture, measurement—are nearly two times more likely to positively impact a higher number of business KPIs.