I Didn't Adopt an AI Tool.
I Accidentally Built an AI Team.
Something I didn't plan for happened while trying to transfer context between AI tools. It turned out to be more interesting than the original task.
About two and a half years into my role as VP of People Operations, I realized I had been having a running conversation with an AI — ChatGPT, at the time — about nearly every significant work challenge I was navigating. Org design. Business cases. Executive communications. Leadership decisions I wasn't ready to talk through with anyone inside the organization.
Somewhere in all of that, I had accumulated something I hadn't set out to build: a living record of how I think.
The Knowledge Transfer Problem
When I started working with Claude — which has genuinely different strengths — I hit the same wall every organization hits when a key employee leaves: the new person is capable, but they don't have the context. They don't know the history. They don't know how you think, what you've already tried, or what matters.
So I did what I would have done with a new director joining my team. I onboarded Claude — not by uploading files, but by transferring knowledge the way knowledge actually transfers. How I make decisions. What I care about. Where I've been burned. What reads as generic to me.
It worked. But what happened in the process was more interesting than I expected.
AI Job-Skill Fit: A Classic I/O Problem
One of the foundational concepts in I/O Psychology is person-job fit — the idea that performance improves when an individual's capabilities align well with what the role actually requires. What I noticed, gradually, was that the same principle applied to AI systems.
| Capability | ChatGPT | Claude |
|---|---|---|
| Longitudinal memory & context | Strong — 2.5 years of accumulated history | Limited at first; built over time |
| Strategic conversation | Strong — iterative, relationship-aware | Strong — rigorous, structurally precise |
| Large document synthesis | Adequate | Exceptional |
| Visual artifacts & design | Limited | Strong — website, decks, structured outputs |
| Long-form writing & editing | Good | Excellent — nuanced, voice-preserving |
| Org context & leadership style | Deep — years of calibration | Developing — through deliberate onboarding |
Different tools. Different fit profiles. Different jobs. The right response wasn't to pick one — it was to understand what each was actually good at, assign accordingly, and manage the handoffs between them. Which is, again, exactly what you'd do with a team of people.
The Unexpected Output
To transfer context effectively, Claude had to analyze and articulate how I think. Communication patterns. Decision preferences. Leadership tendencies. What I find motivating. Where I tend to overcomplicate.
What emerged was the most detailed and accurate leadership profile I have ever received — more nuanced than any 360 I've been through, grounded in actual behavioral evidence rather than survey responses.
Not in a mystical sense. The act of making my thinking legible to a system that needed explicit context forced a level of self-reflection that implicit organizational knowledge never demands.
What I Took From It
The biggest surprise wasn't how much work AI could absorb. It was how much organizational psychology still mattered. Onboarding. Role clarity. Knowledge transfer. Job-skill fit. Every concept that governs how humans integrate into organizations turned out to apply — with remarkable fidelity — to how AI systems integrate into a leadership operating model.
Most organizations struggling with AI adoption aren't struggling because the technology is insufficient. They've skipped the organizational work that would make the technology useful. They've bought the hire before building the job.