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Senior Manager, Data & AI Products and Analytics Engineering at Disney Studios — a matrixed data and AI product engineering organization running enterprise-wide across the studios. Nights and weekends, the same operating model runs a household-software practice where I'm the only human on the team.
Built independently, outside the day job. Live means a stranger can use it today, Invite means real users behind a gate, In Development means not yet in anyone's hands. Nothing here is aspirational.
Household Kitchen Software
Geopolitical Intelligence
Multi-Household Trip Planning
Forecasting & Evaluation
Multi-Agent Editorial Framework
Spec-as-code for PMs
AI Prototyping Platform
Most product leaders can describe an AI-native operating model. This one runs on two: a full product organization at Disney, and a one-person practice at home. Same primitives, different scale — and every product above is evidence for the claim.
Roles, not prompts
A principal PM and a principal PMM review every spec before it becomes code. They hold standards, not context — which is why the output stays consistent across sessions that share no memory.
Specs under version control
PRDs, decision logs, and conventions live in the repo with the code they govern, validated on commit. When a decision gets revisited, the reasoning is still there.
Shipping as the forcing function
Typechecks, linting, test suites and content smoke tests gate every deploy; cost guards and liveness checks watch the data collectors. The discipline that makes enterprise platforms trustworthy is the same discipline, just without an org to enforce it.
Every figure carries its measurement method. If it can't be sourced, it isn't here.
~4 → ~1
Months from spec to shipped
Disney Studios · spec-to-ship duration, before and after the operating-model rollout
100%
Weekly active PM adoption of AI coding tools
Telemetry-tracked, not self-reported
80%
YoY loyalty growth on the Yum CDP
Marketing-mix-model attributed
3
Enterprise data platforms built 0 → 1
Financial services · restaurants · entertainment
Investment analysis into business intelligence into data products into AI product leadership. The domain kept changing; the work of turning messy data into something people trust did not.
The Data Product Agent
Long-form work on data products, AI-native team operating models, multi-agent systems, and building enterprise software through AI coding tools.
Read the newsletterOpen to Director and VP roles in AI product leadership.
Teams building where data and AI actually become products — and where the person leading it is expected to understand what's underneath.
ethan.c.stuart@gmail.com