Ninety days is not a magic number. But it is a useful constraint. It’s long enough to see real change in how a team works, short enough to sustain focus, and specific enough that you can tell at the end whether it worked.
Here is the framework I use for AI-native engineering transformations. It isn’t a rigid script — every organisation starts from a different place — but the phases and sequencing hold across contexts.
Every company I’ve worked with in the past two years has wanted an AI strategy. About half of them were ready to actually execute one. Understanding the gap between wanting AI and being ready to use it effectively is the starting point for any serious AI initiative.
This is the framework I use to assess where a company actually stands.
A technology leader I worked with recently pulled up a spreadsheet of his company’s software subscriptions. AI tools alone: fourteen line items. When we went through each one and asked who was actively using it, the answer was: two tools with meaningful adoption, three with occasional use, nine that had been evaluated and quietly shelved.
Annual spend on tools the team wasn’t using: significant. But the subscription cost wasn’t the real problem.
Six months ago I watched a founder with no engineering background ship a working B2B SaaS prototype in three weeks using Cursor and Claude. No co-founder. No agency. No contractor. The prototype had a login system, a dashboard, and enough functionality to run a dozen customer discovery calls.
This is now possible. It wasn’t two years ago. Understanding what has changed — and where the limits still are — will save you a lot of wasted time.
Most engineering teams adopting AI tools go through the same arc. Initial excitement, a few individual wins, a gradual sense that something isn’t quite scaling — then a plateau. The tools are being used, but the organisation isn’t getting dramatically more capable.
The reason is almost always the same: the team adopted the tool but didn’t build the harness.
What a harness actually is
In physical engineering, a harness is the system of cables, connectors, and guides that lets a complex component operate reliably within a larger system. It doesn’t generate power — it routes it precisely to where it needs to go.
Every month I talk to a technology leader who bought AI coding tools for the team three to six months ago, achieved broad installation, and is puzzling over why adoption is shallow. Before diagnosing the change management layer, it helps to have a clear baseline — the free AI Readiness Assessment surfaces where the specific bottlenecks are across tooling, process, and culture.