How Do You Measure the ROI of AI in Your SDLC?

​We're all wondering what the actual return on all these AI tools is. A lot has gone in already — licences, tooling, a good deal of your teams' time.

​There's no clean formula for it yet, but the question isn't a new one for me. I've been studying delivery metrics since well before any of this: as Head of Engineering at Bridge I went deep into DORA and DX, and that became the baseline I brought to leading AI adoption into the SDLC at an American insurtech.

​My thesis then and now is that AI in the SDLC is one brick, not a new building — what you're after is still engineering performance, so the metric set stays broadly the one we already had, with some tactical extensions for what's genuinely specific to agents.

​What we'll look into:

• ​DORA, and what its authors say about measuring AI-assisted delivery

• ​DX Core 4 and its tension metrics, read against each other so no single number can be gamed on its own

• ​The Lean Startup learning loop, for connecting engineering work to business outcomes

• ​The data your AI vendor is already collecting on your behalf

• ​The metrics I landed on myself, and what each one can and can't tell me

​For: directors, VPs and heads of engineering who own an AI rollout and the decision about whether to keep investing in it — and the platform or developer-productivity leads who'd be the ones instrumenting it. If you're writing features rather than measuring how they get delivered, the hands-on episodes in this series are the better fit.

​Register to get the live link and the recording.