Selected workTesla Energy
Tesla Energy: AI product that got used
Tesla Energy needed AI tools people would actually use. Priorities were scattered, nobody owned the use cases, and we couldn't tell if adoption changed results.
Tesla Energy
1,000+
Active users across 20+ production AI solutions
Context
Scattered experiments sat next to real Energy work with no shared bar for "done." Leaders wanted tools in daily jobs, not another pilot deck.
Constraints
- Adoption had to be scored against workflows, not vanity installs
- Finite roadmap capacity meant every use case needed a named owner
- Security and data boundaries could not bend for release speed
Decisions
- Build a real product-and-engineering team, not a side project
- Sequence the backlog by hours reclaimed and decision quality
- Ship training plus weekly adoption reporting with each release
Outcome
We hit 1,000+ active users across 20+ production AI solutions. Modeled impact: about 540 hours back each week and $1.6M in annualized productivity value.
My role
- Owned the enablement roadmap and what leadership saw each week
- Hired and led the product-and-engineering squad
- Set prioritization rules, governance, and adoption metrics
Team
- Forward-deployed engineers embedded with Energy teams
- Product managers sequencing the use-case backlog
- Business sponsors who owned training the trainers
AI ProductOrganizational DesignExecutive Alignment