When To Use AI In Product Engineering
Notes de l’épisode
AI can write code incredibly quickly. But simply using AI to make your existing software delivery process faster misses much of the opportunity.
In this episode of The Product Engineering Podcast, James looks at how AI changes Product Engineering when you treat it as a learning tool, rather than simply a faster way to turn requirements into production code. AI is excellent at implementing a well-defined plan, but human judgement is still needed to decide whether that plan represents the right thing to build.
We cover:
- Why using AI to accelerate a legacy delivery model isn't enough
- Where human product judgement still matters
- Why AI changes the economics of experimentation
- Moving engineers and product managers further upstream into product decisions
- Using AI to explore several possible solutions rather than committing immediately to one
- Creating prototypes and thin product slices that may never reach production
- Why engineers can use AI to test ideas directly with users
- How AI can compress the feedback loop between an idea and evidence
- Why killing ideas quickly can be more valuable than shipping them quickly
- Why discarded AI-generated code isn't necessarily waste
- A useful diagnostic: how much of the code you're generating actually reaches production?
Before AI, code was expensive enough that teams had strong incentives to decide what to build before implementation began. AI lowers that cost dramatically. That means teams can afford to build something partly to learn whether it should exist at all.
The goal isn't simply to produce more code. It's to explore more possibilities, gather evidence faster and make better product decisions.