The data that got my attention
Gallup’s fourth-quarter 2025 workplace survey delivered a stark split. Among U.S. workers, 44% of leaders now use artificial intelligence frequently on the job. Among individual contributors, that number is just 23%. Since the second quarter of 2023, frequent leader use has climbed from 17% while frequent individual contributor use has climbed from only 9%. The gap has more than doubled in absolute terms. This is no longer a technology story. It is a leadership story.
The number matters because it describes a split in everyday experience. Leaders are learning what the tools can do. Frontline employees often see only the downstream effects: new reports, shifting priorities, and unclear expectations. When the people designing the workflow change faster than the people doing the work, trust erodes.
Why this matters now
AI is no longer an experiment in a research lab. It lives inside email, calendars, customer relationship systems, and reporting dashboards. Yet 49% of U.S. workers say they never use AI in their role. Another 38% say their organization has integrated the technology, 41% say it has not, and 21% do not know either way. That uncertainty creates friction. Workers watch leaders adopt tools faster while they receive little guidance on what the shift means for their daily work.
This divide shows up in productivity numbers and retention conversations. Employees who feel left behind disengage. Managers who are themselves still learning struggle to coach their teams. The result is a patchwork organization where some people benefit from AI while others see it as another burden layered onto an already heavy job.
What the research actually shows
PwC’s 2026 Global AI Jobs Barometer analyzed more than one billion job advertisements across six continents. The findings point to a two-track labor market. In roles most exposed to AI, required skills are changing more than twice as fast as in roles least exposed. The gap is 75% larger than it was a year ago. New tasks added to those AI-exposed roles are 2.5 times more likely to rely on empathy, judgment, and creativity.
The same report found that U.S. entry-level roles most exposed to AI are now seven times more likely to require traditionally senior skills such as leadership and strategic thinking. Those seniorized entry-level roles grew 35% since 2019, while comparable non-seniorized entry-level roles fell 10%. AI is not eliminating junior work. It is raising the bar for what junior work demands.
The Gallup data below shows how AI use has concentrated at the top of organizations.
| Worker group | Frequent AI use in Q4 2025 | Change since Q2 2023 |
|---|---|---|
| Leaders | 44% | +27 percentage points |
| Managers | 30% | +15 percentage points |
| Individual contributors | 23% | +14 percentage points |
Gallup’s first-quarter 2026 follow-up added another layer. Half of U.S. employees now use AI at least a few times a year, up from 46% the prior quarter. Yet only 12% of workers in AI-adopting organizations strongly agree that AI has transformed how work gets done. The tools are spreading faster than the organizational change they are supposed to produce.
A practical framework for leaders
Closing the AI-human gap requires more than training videos. It demands a structured approach that links technical adoption to skill development. Here is a five-skill framework leaders can use now.
Sharpen judgment. As AI produces more options, the ability to weigh trade-offs, ask better questions, and decide under uncertainty becomes the premium skill. Assign rising talent to decisions with incomplete information and give them feedback afterward.
Teach empathy. Customer and colleague interactions still hinge on emotional awareness. PwC’s findings show that tasks requiring empathy are becoming more central, not less. Build empathy into onboarding, coaching, and promotion criteria.
Reward creativity. AI can generate variations, but it cannot originate purpose, taste, or strategic context. Leaders must protect time for creative problem solving rather than filling every hour with execution.
Develop leadership at every level. Junior roles now demand senior skills seven times more often in AI-exposed fields. Waiting for promotion to teach leadership is too late. Introduce small-team leadership opportunities early.
Anch decisions in strategy. Strategic thinking keeps AI use aligned with business outcomes instead of becoming a disconnected productivity exercise. Every AI pilot should include a clear business hypothesis and a review date.
Start by auditing your current training spend. Most organizations invest heavily in AI tool training and barely at all in the human skills that make those tools useful. Rebalancing that budget is the fastest way to reduce risk.
The bottom line
AI adoption is accelerating. The workforce split is accelerating too. The companies that win the next three years will not be the ones with the most models. They will be the ones that help people build the judgment, empathy, creativity, leadership, and strategic thinking required to use those tools well. Human skills are not a soft add-on. They are the operating system that determines whether AI produces value or just produces noise.
Where to go from here
Leadership teams need a clear picture of where their human skills stand before the gap becomes permanent. Start with an assessment that measures readiness across judgment, empathy, creativity, leadership, and strategic thinking, then build a targeted development plan for the people who will work alongside AI. AI Leadership Readiness Assessment →
