McKinsey found that 73% of leaders don't believe their organizations are ready for the shifts an agentic future will require. But most teams already have the tools, so what leaders actually doubt is whether their people are ready to work differently alongside them.

AI Isn’t Built Into the Work

Many people find themselves using AI at work without ever being told how it fits into their role. Only 21% of workers said their employer had given them clear AI guidelines specific to their job. And nobody explains what it means when they say that AI should free up their time. Should that time be used toward  solving more complex problems, or just doing more of the same work? Without answers, people feel lost about how and why to use AI.

Gallup's latest numbers show where that leaves organizations: at companies that have implemented AI, only 14% of employees strongly agreed it has transformed how work gets done, and three in four said their organization hasn't communicated a clear AI plan.

Rollout announcements don't fix this on their own. A rollout roadmap tells people where the organization is going, but it doesn't explain what changes for them. And as roles and expectations keep shifting, people need ongoing coaching through the transition, not a one-time announcement.

What makes the difference? Often, the manager.

Research found that 33% of workers who strongly agree their manager champions AI say it has transformed how work gets done at their organization. Among everyone else, it's 4%. Meanwhile, half of the CHROs Gallup surveyed weren't confident their managers could guide employees' AI use.

Managers don't need to be AI experts. They need to be visibly curious, use the tools themselves, and make experimenting feel safe.

What can leaders do next?

  • Have managers use AI where their teams can see it. When managers model curiosity and trial-and-error, they normalize it for their teams.
  • Give people a place to compare notes. When colleagues share when they used AI's output and when they pushed back, it teaches people to trust their own judgement. 
  • Treat AI output as a draft. Harvard Business School AI Institute research found that the best outputs were made by people who translated AI’s suggestions and tailored them to their own business.

These habits build with practice, which is the thinking behind Aurora, reacHIRE's leadership development program. It blends peer cohorts, where people work through real company issues together, with one-on-one coaching from executive Guides who have led through change themselves.

Learn more in The Human Side of AI: What Organizations Need to Build Next, our full whitepaper. It goes deeper into the risks of leaning too hard on AI's answers and what organizations can build to counteract them.

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