AI and jobs: The leadership question CEOs are missing: PwC

Paul Griggs

PwC US Senior Partner, PwC US

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Key takeaways

AI is creating a workforce dividend: a growing pool of human capacity that can fuel the next wave of growth.

The defining AI question for leaders is what to build with the value AI creates.

Reskilling alone is not the answer. The AI dividend test puts growth before savings, so businesses don’t cut themselves out of future opportunities.

The window to act is open now. Leaders who reinvest rather than retreat can build advantages that are difficult to close.

The debate about AI and jobs is louder than ever. It’s a concern I hear frequently in conversations with business leaders, employees, and clients. It’s also one that has accompanied other major technology shifts. Each time, the story has been one of short-term disruption followed by long-term growth—not collapse.

Early signals on AI point in a similar direction. AI creates value. Value creates growth. And growth creates demand for new kinds of work. What’s different this time is the pace. Prior tech transitions took decades of building before we felt economic impact. AI is generating impact now, even as the build-out continues.

AI investment already accounts for an increasing share of GDP. Enterprise AI spending is accelerating rapidly, including infrastructure and transformation investments. The impact is showing up in the workforce too. PwC’s 2026 AI Jobs Barometer, which analyzed one billion job advertisements globally, found that headcount growth at the organizations most exposed to AI is double that of the least exposed. At the same time, the analysis found that AI is reshaping jobs in very different ways: Roles where AI places greater value on human expertise, judgment, and creativity are growing faster and often paying more; roles where AI absorbs the higher-end expertise work are becoming narrower and experiencing slower growth.

AI is delivering efficiency. But it’s also lifting performance and enabling capabilities that make entirely new things possible. Together, they’re creating something larger: a dividend of human capacity, resources, and potential that most leaders have yet to fully confront.

How should leaders put that dividend to work?

The default answer, and the easiest one to defend, is to harvest those gains for today’s earnings: Cut costs, expand margins, return capital to shareholders.

For many companies, realizing savings is an important near-term priority. Some workforce reshaping is likely unavoidable, and pretending otherwise isn’t leadership. But treating AI solely as a cost opportunity risks mortgaging tomorrow to fund today.

The real leadership decision isn’t whether to capture savings. It’s how to maximize what AI can create—and then put it to work. The difference isn’t the technology. It’s the choice leaders make about what happens next.

Companies will respond to AI by investing in reskilling, and that will be essential as jobs evolve. But reskilling alone doesn’t answer the bigger strategic question: where should we redirect new capabilities as AI changes demand and creates new areas of growth?

That’s the role of redeployment. It connects workforce decisions to business strategy and growth. Start with the opportunity: the new markets, products, and customers AI makes possible. Then identify the capacity AI is creating, and determine the workforce needed to pursue those growth priorities. Workforce decisions should follow accordingly: redeploy and reskill where possible, hire where skills don’t exist yet, and reduce where the gap is too wide.

The sequence matters. Define the opportunity before deciding what to do with people. Get that order right, and AI can fuel results today while funding growth tomorrow. Get it backwards, and companies may risk optimizing themselves out of future opportunities.

Otherwise, we risk preparing people for the work AI is changing rather than the work AI is creating. That’s how we miss the bigger prize: new opportunities made possible as AI lowers the cost of expertise, coordination, and personalization. What becomes possible when personalized financial guidance can be delivered profitably to customers once too small to serve? When small businesses can access legal, tax, and compliance support that once required teams of specialists? When health systems can extend patient navigation and preventive outreach beyond the limits of traditional labor models?

We’re beginning to see what redeployment looks like in practice. AI code generators are automating portions of software development that once consumed significant time and effort. AI puts more coding capability in the hands of developers. That frees more experienced developers to coach, develop the team, and drive quality. The work is changing, not disappearing. In fact, employment of software developers is projected to grow 15% over the coming decade. As the cost of building software falls, organizations can pursue more projects, launch more products, and serve more customers.

The AI dividend test

The difference between harvesting the AI dividend and reinvesting it comes down to a handful of decisions. The four questions below depend on one another. The first is foundational: Everything else depends on the opportunities leaders choose to pursue.

Where are our growth plays? What markets, products, or customers does AI now make economically possible to pursue? What new products, services, and capabilities can differentiate us, drive growth, and amplify the impact of our people?

Then commit to them. Growth plays need to be named, prioritized, and funded now, whether agentification is just beginning or already underway. Otherwise, the dividend AI creates may default to savings, not growth.

What capacity is AI creating, and where should it be deployed? Which tasks, roles, and workflows are changing? What time, expertise, and decision-making capacity are freeing up? And how does that map to the growth plays we’ve defined?

Then make it visible. Don’t let efficiency gains disappear into a spreadsheet. Instead, track them as strategic capacity: hours, roles, and bandwidth made available for higher-value work, and mapped explicitly to the growth plays already defined.

What workforce will those growth plays require? Which roles can be reinvented? Which may be reduced? Where are new skills and capabilities needed?

Then design for it. Reinvent where the work has changed. Reskill where people can grow into the future. Reduce where the gap is too wide. Some roles will likely be retired. Some headcount decisions may be unavoidable. But reduction should follow strategy, not substitute for it.

How do we connect people to that future? Companies can name the strategy. The harder part is execution: knowing the skills people have, the skills they’ll need, and the pathways that get them there.

Then build for it. If leaders can’t see their workforce clearly or move talent quickly, redeployment stays a slide in a deck. Talent mobility is the operational engine that makes growth strategy real.

The AI dividend test reframes the conversation. Human capacity isn’t what’s left after AI has done its work. It’s the raw material for the next wave of growth and performance.

People respond differently when they can see where change is leading: opportunities to build new things, advance their careers, and share in the company’s success. Fear of what’s coming next does the opposite.

In my experience, the most powerful signal a leader can send about AI is not a memo about its potential. It’s a growth strategy that shows where the business is headed, paired with a workforce plan that shows how people will help get it there.

The opportunity ahead

The AI dividend is not simply a source of savings. Used narrowly, AI becomes a tool for shrinking. Used boldly, it can become a platform for better performance, new capabilities, and growth that wasn’t previously possible. The organizations reinvesting their AI dividend into new sources of value for their customers and their people are creating advantages that may be difficult to close. In the age of AI, the cost of waiting compounds quickly. Leaders who reinvest rather than retreat shape what comes next.