What if your engineering team could deliver a production-ready feature in two days that would traditionally take one month When Xero partnered with AWS, that's exactly what happened. In this session, we'll take you inside a real-world AI-Driven Development Life Cycle (AIDLC) workshop run against a live production use case. Working across an existing brownfield codebase, the Xero engineering team reached MVP in just two days against a feature that would have taken a month using traditional development methodology. We'll unpack what made that possible, what we learned along the way, and how those lessons are now shaping a plan to scale the AIDLC methodology across the entire engineering organisation.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
AI-Driven Development Lifecycle (AIDLC) — pair-programming with AI through the entire SDLC, not just code completion. Brownfield codebase, MVP in 2 days for what used to take a month.
Why it matters
If a 30× delivery speedup is achievable on real production work — even partially — your release cadence assumptions need re-baselining.
The hard parts
Brownfield is much harder than greenfield. AI hallucinates against bad context. The gain depends entirely on the quality of context you can feed the model.
Playbook moves
(1) Invest in repo-level context — `CLAUDE.md` / `AGENTS.md` files, ADRs, architecture diagrams the AI can read. (2) Spec-first, not code-first. The spec is the leverage point. (3) Verify with tests, not vibes — and let the AI help write the tests.
The surprise
The 90%+ time saving is mostly in *design + scaffolding*, not raw code typing. The lever is "decisions made fast," not "characters typed fast." Senior engineers who already produced fast scaffolds will see smaller gains than juniors who got stuck on architecture.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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