Using AI as a trigger for immediate layoffs is usually a weak starting point for transformation. Early examples of AI-driven job cuts have often:

• Failed to deliver the expected financial returns
• Exposed gaps in organizational knowledge and processes
• Created new demands for human oversight that weren’t planned for

A more effective approach is to start with the work itself, not the headcount. That means:

• Breaking roles down into specific tasks and workflows
• Identifying where AI can genuinely improve performance, quality, or speed
• Redesigning processes around the right mix of human skills and AI capabilities

This work-first approach helps you avoid cutting critical expertise too early, and it gives you room to adjust as AI tools and your own understanding of them evolve.

The impact of AI on jobs so far is more modest than many headlines suggest. For example, Goldman Sachs estimates that AI reduced monthly U.S. payroll growth by about 16,000 jobs over the past year, adding roughly 0.1 percentage points to the unemployment rate. That figure includes both slower hiring and actual job cuts.

Many high-profile “AI layoffs” are better understood as AI-washing — traditional restructuring decisions framed as AI-driven to signal that leadership is responding to technological change.

The takeaway: we are still early in the adoption curve. Now is the time to experiment, learn, and redesign work thoughtfully, rather than assume that large-scale automation is already a proven path to efficiency.

A more sustainable AI workforce strategy focuses on redesigning work and aligning AI with business goals. Key elements include:

• Start from strategy, not tools: Tie AI initiatives directly to your core business objectives, not to generic promises of automation.
• Deconstruct roles into tasks: Map which tasks are best handled by AI, which require human judgment, and which benefit from a human–AI partnership.
• Preserve human judgment and accountability: Keep people in the loop for decisions that affect customers, ethics, risk, and brand trust.
• Invest in leadership understanding: Help leaders build a realistic view of what AI can and cannot do so they can make grounded workforce decisions.
• Move gradually: Make workforce changes in stages so you can adjust as AI capabilities, regulations, and internal skills develop.

This approach helps you reimagine work, strengthen capabilities, and manage risk, instead of relying on one-time headcount cuts that may undermine long-term performance.