When I worked on games, we obsessed over details: how the camera responded to the controller, the sound of a sword against a shield. In consumer tech, I was surprised by how little time we spent on that craft. Scarce engineering time made every detail compete for a place on the roadmap. But if we cut all the “nice-to-haves,” our product won’t be nice to have.
AI is changing that constraint. Agents can get a team to a working version sooner and shorten the time between feedback and a better result. Product Delivery is the work of turning a shared definition into a product worth shipping, adapting as the team learns. The first working version is a starting point, not the finish line.
What this essay explores
- Deliver quickly with a cross-functional team while keeping the customer goal in view.
- Use faster implementation to refine the product during development instead of adding another feature or deferring improvements to the backlog.
- Give people and agents the context and decision rights to make the small choices that shape the experience.
- Replace sequential handoffs with collaboration throughout the build as the product evolves.
- Test probabilistic features across many inputs, then expand AI evals with real customer cases after launch.
- Grow from managing day-to-day delivery tradeoffs to building the systems that help many teams deliver well.
AI gets a team to a working version sooner. That leaves more time to improve the product before launch.
Key takeaway
A faster first draft should give us more time to make the final draft better.
