AI is reshaping how work gets done, not just which tools people use. Roles are becoming more fluid, decisions are happening faster, and value is increasingly created through human–machine collaboration. These are structural shifts that cut across functions, reporting lines, incentives, and even professional identities.

Recent research shows how much this is now a CEO agenda:

• 72% of CEOs say they must be the main decision-maker on AI in their organization—twice as many as in 2025.
• 50% of CEOs globally believe their job stability depends on getting AI right.

At the same time, most organizations are not designed to absorb AI effectively. Years of legacy processes, systems, roles, and decision rights have created what many leaders now call “organizational debt”. This makes it hard to cut through old ways of working and unlock new sources of value.

Because AI challenges existing structures and power dynamics, it can’t be managed effectively from the middle of the organization. CEOs need to:

• Set a clear direction for how AI will reshape work and value creation.
• Align incentives, governance, and culture with that direction.
• Ensure workforce stewardship—upskilling, reskilling, and redeployment—sits at the top of the agenda, not as a side initiative.

In short, AI is no longer just a technology test. It’s a test of leadership, and CEOs are expected to lead the reinvention of work end to end.

Many organizations start by layering AI on top of today’s processes. That can boost individual productivity, but it often leaves enterprise value “stuck” inside legacy systems and workflows.

To unlock the full benefit, leaders need to reimagine the work itself, not just automate existing steps. That means asking different questions:

• Why do we do this work? What value does it actually create for customers, employees, and the business?
• Who should do this work? What should be handled by people, by AI, or by a combination of both?
• How must we redesign the work? How do we move from step-by-step execution to orchestrating work across systems and teams?

In practice, this involves:

• Redesigning end-to-end workflows instead of automating isolated tasks.
• Breaking through organizational debt by simplifying roles, decision rights, and processes that no longer fit an AI-enabled environment.
• Focusing on the right value pools—where AI can change cost structures, speed, quality, or customer experience in a meaningful way.

BCG’s work with clients shows that AI can quickly improve personal productivity, especially for knowledge workers like software developers. But without a system-level redesign—often cutting across functions and even within a single leader’s scope—those gains rarely translate into sustained financial impact.

The leaders who turn AI into real advantage apply transformation discipline: they treat AI as a catalyst to rethink how value is created, not just as another technology rollout.

As AI takes on more routine tasks, work shifts toward higher-value activities. This doesn’t always mean entirely new job families; it often means raising the baseline of skills across the organization.

Key capabilities that become more important include:

• Stronger judgment to make decisions in more dynamic, AI-enabled environments.
• Ability to interpret and challenge model outputs rather than accept them at face value.
• Orchestrating work across systems instead of following linear, step-by-step processes.
• Creativity and curiosity to continuously learn and to “disrupt” one’s own ways of working.

For leaders, the challenge is less about one-off training and more about building the conditions for continuous learning at scale:

• Embedding learning into redesigned workflows, not just in classrooms.
• Providing ongoing enablement and reinforcement so new skills are applied in real work.
• Creating a culture where experimentation, test-and-learn, and cross-functional collaboration are expected.

Because these changes affect roles, boundaries, and identities, they can’t be delegated entirely to HR or middle management. CEOs and senior leaders need to:

• Be explicit about which work will change and why.
• Commit to upskilling, reskilling, and redeployment as a core part of the AI strategy.
• Provide clarity and courage in moments of uncertainty—challenging long-standing assumptions and ways of working even when the path isn’t fully mapped.

Organizations that pair AI investment with deliberate investment in their people are better positioned to bridge the gap between AI adoption and lasting competitive advantage.