Only 10% of Leaders Use AI—So Who’s Teaching Everyone Else?

Only 10% of senior leaders admit to using AI. Which raises an awkward question: if leadership isn’t really using the tools, who exactly is teaching everyone else how to work with them? Increasingly, the answer is peers. In this conversation from Transform, David Rice sits down with returning guest Kamaria Scott to unpack why AI adoption is becoming less of a top-down transformation initiative and more of a global peer-learning experiment happening in real time.

They explore the growing tension between organizational pressure to adopt AI and the complete lack of capacity many employees have to actually experiment with it. Along the way, they dig into manager enablement, skill atrophy, learning agility, performance management, and the uncomfortable possibility that companies are automating away the very expertise employees need to judge whether AI output is any good in the first place. The result is a candid discussion about why managers may become the bottleneck in AI adoption—and why organizations that failed to build real learning cultures before this moment are now paying for it.

What You’ll Learn

  • Why AI adoption is failing when organizations don’t create space for experimentation
  • How peer-led learning is quietly becoming the real AI enablement strategy
  • Why managers—not HR—ultimately determine whether AI adoption succeeds
  • The risks of skill atrophy and losing the ability to evaluate quality work
  • How AI can strengthen human expertise instead of replacing it
  • Why “learning agility” has become a critical organizational capability
  • What performance management might look like when AI agents do part of the work
  • How organizations can create healthier AI cultures instead of reactive AI policies

Key Takeaways

  • AI adoption fails without slack time.
    You can fund AI initiatives all day long, but if employees are expected to maintain full workloads while “experimenting” on the side, adoption stalls immediately. Managers control capacity far more than strategy decks do.
  • Peer learning is becoming the dominant learning model.
    The people discovering the most useful AI workflows are learning from coworkers doing the same job—not from executives giving keynote speeches about transformation.
  • Managers are becoming the AI bottleneck.
    Every organizational strategy eventually flows through frontline people leaders. If managers aren’t enabled, coached, and given room to experiment themselves, the strategy dies there.
  • AI should amplify expertise—not replace it.
    Kamaria frames the danger perfectly: people risk becoming “human interfaces” for AI output instead of experts applying judgment and experience to improve it.
  • Skill erosion is already happening.
    Employees may produce technically correct outputs while losing the ability to evaluate whether the work is actually good. That’s a much bigger problem than whether someone used AI to draft an email.
  • Passion projects create safer experimentation environments.
    Giving employees protected time to explore AI around work they already care about creates far more engagement than forcing blanket adoption mandates.
  • Organizations never really built learning cultures.
    Many companies spent years deprioritizing learning time in favor of billable utilization. Now they’re entering a massive technological transition without employees who have strong learning agility.
  • Performance management is entering strange territory.
    If AI agents complete half the work, what exactly are organizations evaluating? Output? Judgment? Prompting skill? Collaboration? Companies are still figuring this out in real time.

Chapters

  • 00:00 — AI adoption bottlenecks
  • 02:31 — Managers and AI rollout
  • 04:15 — Peer learning at work
  • 05:05 — AI passion projects
  • 07:51 — Experimentation fatigue
  • 09:01 — Automating admin work
  • 10:34 — AI as a strength multiplier
  • 11:42 — The return of human writing
  • 13:54 — AI-written communication
  • 15:27 — Skill atrophy risks
  • 18:09 — Human judgment vs AI output
  • 20:58 — Why expertise still matters
  • 23:06 — Learning agility failures
  • 25:42 — AI and hiring backlash
  • 26:24 — Performance reviews in the AI era
  • 27:52 — AI as a conversation starter
  • 29:35 — Final reflections

Meet Our Guest

Kamaria Scott is the Founder and CEO of Enetic, a technology and innovation company focused on helping organizations leverage AI, automation, and emerging technologies to drive smarter business outcomes. With a background spanning digital transformation, strategy, and operational leadership, she works with leaders and teams to bridge the gap between innovation and practical execution. Kamaria is recognized for her forward-thinking approach to AI adoption, organizational growth, and future-ready leadership, helping businesses navigate change while building more agile, people-centered systems. 

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