Establishing an AI-enabled SDLC

An AI-enabled software development lifecycle uses AI across specification, code generation, review, testing, CI/CD and release. A quality gate checks the generated work before it moves on, and the change record says which changes were AI-assisted. The assistant itself is your choice.

Agree a policy first: which assistants and models may be used, on which code and data, and what must never leave your environment. Then put gates in the pipeline so that generated work meets the same review, test and release standard as any other change, and record which changes were AI-assisted.

Less than many teams expect. Most of review and CI/CD stays as it is; two things are added. A person agrees the specification before anything is generated, and each AI-assisted change is recorded as such. The full gate list is under "Quality gates" above.

That is a legal question, and the answer depends on your contracts, your assistant's terms and the jurisdiction. We do not give legal advice. We help you set the policy and records your counsel needs to answer it: which assistants are used, on what code, under which terms.

Each gate produces a record: who agreed the specification, who reviewed the change, which tests and evaluations ran, and whether the change was AI-assisted. Those records sit with your existing change records, so audit and incident review work as before.

In waves. The rules are the same for every team from the start; what changes between waves is the guidance and the gate settings, adjusted from what the pilot teams showed, including where the gates slowed work down.

We agree the measures with you at the design stage: for example, what the gates catch, how often generated changes are sent back at review, and whether incident review can trace an AI-assisted change. We do not promise figures in advance; the measures show what is happening in your organisation.