A new report from Mercer suggests the next wave of AI disruption may have less to do with software and more to do with organizational restructuring.
The consultancy’s Global Talent Trends 2026 study found that 98% of executives are planning organizational design changes over the next two years, while 99% expect artificial intelligence to result in at least some workforce reduction. The research surveyed approximately 12,000 executives, employees, HR leaders and investors globally.
For many companies, the challenge is no longer adopting AI. It is reorganizing the business around it. As boards come under pressure to show returns on technology spending, leadership teams are increasingly examining reporting lines, workforce structures and skills requirements rather than simply adding new AI tools.
This is not simply a technology story. It is becoming a management story.
Many companies are discovering that AI does not automatically produce productivity gains when layered onto existing structures. Mercer found that 63% of C-suite leaders believe redesigning work around AI and automation is the people initiative most likely to generate return on investment. Yet only 32% believe their workforce currently combines human and machine capabilities effectively.
That gap is beginning to influence how businesses are organized. Executives report plans to simplify reporting lines, centralize governance, flatten hierarchies, expand agile teams and build more flexible workforce models. Some organizations are even preparing operating structures where AI agents and human workers are managed within the same functional systems.
The shift comes as investors become more focused on execution than ambition.
For several years, capital flowed toward companies promoting AI adoption strategies. Mercer’s findings suggest investors are paying closer attention to whether organizations can redesign work, skills and leadership models to generate sustainable returns from those investments. Firms attracting the strongest support are often those combining AI adoption with workforce capability rather than treating technology as a standalone solution.
Technology does not solve the talent problem. Talent scarcity ranked as the leading force shaping workforce plans in Mercer’s survey, ahead of many other economic and business concerns. Companies continue to report difficulty attracting people with critical digital capabilities while leadership teams face pressure to build skills quickly enough to keep pace with technological change.
Workers are aware of the disruption. More than half of employees worry about whether their skills will remain relevant, while many say they would willingly exchange future pay increases for opportunities to develop AI and digital capabilities. Employers face a simple reality: demand for new skills is arriving faster than many traditional talent systems were designed to handle.
Workforce sentiment is moving in the opposite direction. Mercer found employee thriving has fallen sharply, dropping from 66% in 2024 to 44% in 2026. Employees report growing concerns about job security, AI-driven disruption and broader economic uncertainty. For companies attempting large-scale transformation programs, that decline presents an operational challenge rather than simply a cultural one. Workforces struggling with uncertainty are harder to mobilize around change.
A disconnect is also emerging between executive priorities and HR priorities. While executives are concentrating on AI integration, workforce redesign and people analytics, HR leaders remain more focused on employee experience initiatives and talent processes. Mercer warns that this misalignment risks slowing transformation efforts at precisely the moment many organizations are attempting to move from experimentation to implementation.
Many firms already possess significant workforce data. The challenge lies in turning those signals into decisions about skills, leadership, deployment and organizational design. Mercer found that relatively few executives believe their HR teams currently provide effective guidance on human-capital risks and opportunities, despite growing expectations around workforce intelligence.
For business leaders, the next stage of AI adoption may look very different from the first.
The early years were dominated by software procurement, pilot programs and technology announcements. The next stage will be measured through reporting structures, management layers, workforce mobility, leadership capability and skills development.
AI spending remains easy to announce. Rebuilding an organization around it is considerably harder. Across large companies, attention is moving away from software procurement and toward workforce design, management structures and skills planning. That work is slower, less visible and far more difficult to outsource.
