01 The Stall

Every engineering organisation has now seen the demo. An agent writes a feature in minutes; a room of senior engineers goes quiet. And then, a year later, remarkably little about how software actually ships has changed.

The bottleneck is not capability. The models are good enough, and getting better on their own schedule. The bottleneck is adoption: turning a personal trick performed in a sandbox into the way a team builds — safely, repeatably, in production. That turn is organisational, not technical, and it is where almost everyone stalls.

There is now evidence for the trap. In the 2024 DORA State of DevOps research, every 25% rise in AI adoption came with a small lift in individual productivity (about +2.1%) — and, on the very same people, a 2.6% drop in the time spent on genuinely valuable work. At the team level it was starker: a 1.5% drop in delivery throughput and a 7.2% drop in delivery stability. Developers feel faster while the valuable work — and the shipping — quietly slips. Individual speed, spread across the software development lifecycle (the SDLC) without the right guardrails, makes the system as a whole less reliable, not more.

This paper offers two things. A model to see where your organisation actually is — six honest stages, not a hype curve. And a way to move that treats adoption as the real work: small, guardrailed experiments that deliver value and build belief at the same time.

02 Where you are - the six stages

Adoption here is not "how many AI tools do you own." It is a change in two things at once: who builds the software, and what the software is. The model tracks both.

On the left, the team. Engineering is the core; product and UX sit around it; and the place the AI actually lives moves stage by stage — from a chat window in another tab, to an agent that owns the control flow. On the right, the architecture inverts: code that calls a model at the edge becomes an agent that calls deterministic code as tools, for the moments the answer must be exact.

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