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Active Modeling¶
An AI sponsor who doesn't use AI is a contradiction.
The Argument¶
Leadership credibility in AI adoption is not conferred by title, budget authority, or executive sponsorship decks. It is earned through visible, personal use. When a senior leader delegates all AI interaction to their team — when they have never written a prompt, never struggled with a hallucination, never experienced the disorientation of a tool that is simultaneously impressive and unreliable — they lack the embodied understanding necessary to make sound decisions about adoption pace, risk tolerance, and resource allocation.
Active Modeling is the practice of using AI tools personally, visibly, and reflectively. It is not about becoming a power user or a technical expert. It is about closing the experiential gap between those leading AI initiatives and those doing the actual work. Research on organizational change consistently shows that leader behavior is the strongest signal of what an organization actually values (Schein, 2010). Espoused support without personal practice produces cynicism.
The concept extends beyond mere tool use. Active Modeling means sharing what you learned — including what failed. It means demonstrating the productive struggle that accompanies any new capability. It means being willing to look incompetent in public, which is precisely the vulnerability that most senior leaders have spent careers learning to avoid.
Three Levels¶
| Level | What This Looks Like | Red Flags |
|---|---|---|
| Leading Self | Uses AI tools weekly in actual work — drafting, analysis, research, decision support. Can describe specific instances where AI changed an output. | Has never personally used an AI tool. Delegates all interaction to an assistant or direct report. Cannot name the tools the organization has deployed. |
| Leading Teams | Shares personal AI use cases in team settings. Demonstrates both successes and failures. Encourages team members to share their own experiments. | Talks about AI exclusively in abstract, strategic terms. Never demonstrates personal use. Frames AI as something "the team" does. |
| Leading Systems | Creates organizational norms around visible AI use. Ensures leadership forums include AI-use sharing. Models the expectation that all leaders, not just technical ones, engage directly. | AI adoption is positioned as a technology initiative. No expectation that non-technical leaders engage personally. Executive communications about AI are ghost-written by the AI team. |
Observable Behaviors¶
- References specific personal experiences with AI tools in meetings and communications — not hypothetical use cases or vendor demos.
- Shares failed experiments and what they revealed, not just polished success stories.
- Can articulate the difference between what AI does well and what it does poorly in their specific domain, based on firsthand experience.
- Asks informed questions about AI tools that reflect actual use, not surface-level briefings.
- Visibly iterates on their own AI practices — trying new tools, adjusting workflows, retiring approaches that don't work.
Development Pathways¶
Start with your actual work. Pick one recurring task — a weekly summary, a decision analysis, a communication draft — and use AI for it. Not as a demonstration, but as genuine work practice. The learning comes from real stakes.
Keep a use journal. For 30 days, log every AI interaction: what you tried, what worked, what surprised you, what failed. Review weekly. The patterns that emerge will inform how you lead adoption more effectively than any briefing.
Share publicly. In your next leadership meeting, spend five minutes showing something you tried with AI. Include the rough edges. The signal you send by being imperfect in public is more powerful than any mandate.
Pair with a practitioner. Find someone on your team who uses AI fluently. Sit with them for an hour. Watch how they work. Ask questions. The gap between executive understanding and practitioner reality is often larger than leaders assume.
Set a personal cadence. Commit to trying one new AI capability per month. Not a major initiative — a single experiment. The compound effect of twelve experiments per year transforms understanding.