Journey¶
The Pivot¶
After 20 years in program and creative operations, I found myself at an inflection point. The work I'd been doing—managing complex projects, coordinating teams, building systems—was increasingly being augmented, and in some cases replaced, by AI tools. Rather than resist this shift, I decided to lean into it.
The transition wasn't sudden. It was a gradual recognition that the skills I'd developed—systems thinking, process design, stakeholder management—were exactly what's needed to build AI products that actually work. AI isn't just a tool; it's a new medium that requires understanding both its capabilities and its limitations. My operational background gives me a unique perspective on how to structure AI workflows, evaluate outputs, and build systems that humans can actually use.
Now I'm focused on AI Product Management: the intersection of product thinking, operational excellence, and AI capabilities. I'm learning by doing, building experiments, and documenting the process. This site is part of that documentation.
Timeline¶
| Date | Milestone |
|---|---|
| 2005-2024 | Program + Creative Operations across agencies, studios, and tech companies |
| 2024 Q3 | First deep dive into AI tools (ChatGPT, Cursor, n8n) |
| 2024 Q4 | Began building agentic workflows and automation systems |
| 2025 Q1 | AI Week engagement sparked formal pivot planning |
| 2025 Q1 | Launched AI PM Bootcamp v1 (self-directed learning system) |
| 2025 Q1 | Built first production agentic workflow prototype |
| 2025 Q1 | Launched LukeMoody.ai to document and share the journey |
Skills Map¶
| Category | Skills | Application to AI PM |
|---|---|---|
| Operations | Process design, workflow optimization, systems thinking | Structuring AI workflows, designing evaluation frameworks, building reliable systems |
| Project Management | Stakeholder management, timeline coordination, risk mitigation | Managing AI product development, coordinating cross-functional teams, managing AI project risks |
| Creative Operations | Resource allocation, creative brief development, quality control | Prompt engineering, output evaluation, creative AI workflow design |
| Communication | Clear documentation, stakeholder alignment, training | AI product documentation, user education, team enablement |
| Problem Solving | Root cause analysis, iterative improvement, constraint navigation | AI debugging, prompt iteration, constraint-based AI design |
| Learning | Rapid skill acquisition, knowledge synthesis, teaching | Staying current with AI tools, synthesizing AI capabilities, building learning systems |
Learning Stack¶
My approach to learning AI Product Management is systematic and hands-on:
Formal Learning - AI Week Learning Playbook (structured curriculum) - AI PM Bootcamp v1 (self-directed program) - Tool-specific deep dives (n8n, Lindy, Cursor, Napkin)
Experimentation - Building real workflows and prototypes - Testing AI tools in production contexts - Documenting failures and successes
Community - Engaging with AI product communities - Sharing learnings and experiments - Learning from others' approaches
Reflection - Structured note-taking and synthesis - Regular evaluation of what's working - Iterating on learning systems themselves
How I Work with AI as a Partner¶
AI isn't a replacement for thinking; it's an amplifier. Here's how I structure the collaboration:
1. Define the Problem Clearly Before bringing AI into the process, I articulate the problem, constraints, and success criteria. AI is powerful, but it needs clear direction.
2. Use AI for Ideation and Structure I use AI tools (ChatGPT, Cursor, Atlas) to generate ideas, explore structures, and draft initial content. But I always review, edit, and refine. The AI suggests; I decide.
3. Build Manually, Iterate with AI I write code, structure documents, and make design decisions myself. Then I use AI to review, suggest improvements, and catch errors. This maintains my understanding while leveraging AI's pattern recognition.
4. Evaluate Outputs Critically Every AI output gets evaluated against my criteria. I test, refine prompts, and iterate. The goal isn't perfect first outputs; it's learning what works.
5. Document the Process I document what I tried, what worked, what didn't, and why. This creates a feedback loop that improves both my skills and my AI collaboration.
6. Ship and Learn I prioritize shipping over perfection. Real use reveals what actually works. Every project teaches something.
This approach keeps me in control while leveraging AI's capabilities. It's partnership, not delegation.
What's Next¶
The journey continues. I'm building more experiments, deepening my understanding of AI product management, and looking for opportunities to apply these skills in real product contexts. This site will evolve as I learn and build.
If you're on a similar journey, or if you're looking for someone who understands both operations and AI, let's connect.