AI coding assistant rollout playbook for a 5-10 developer team

In short: to roll out an AI coding assistant to a team of five to ten developers, start with a process review and a measured baseline, install one workflow in the team's own tools, run a hands-on workshop, and write a playbook the team keeps. The tool matters less than the process around it: how specs get written, how AI-written code is reviewed, and how you will know in six months whether it worked.

Why most rollouts disappoint

A licence is not a rollout. Teams that are handed a tool tend to use it unevenly, review its output inconsistently and cannot say whether it helped. An outside consultant announcing that the process needs fixing, in front of the person who signs the team's reviews, produces compliance rather than change. So the rollout works best when it is framed and delivered as an investment in the team.

The five steps

1. Review the real process (about two days)

How are specs written, how is code reviewed and how does testing actually happen, as opposed to how the handbook says it happens? Read artefacts such as tickets, pull requests and the commit history instead of interrupting people.

2. Take a baseline

Pick two or three measures you can track: time to write a spec, review turnaround, defects traced to unclear requirements. Without a before-number, no one can tell later whether anything worked.

3. Install one workflow, on your conventions

Choose a method such as spec-driven development, where the written specification is the artefact the model works from. Put prompts and templates under version control next to the code, so the workflow is reviewable and shared. The same specification discipline is what the PRD template describes.

4. Run a hands-on workshop (half a day)

The team should leave having run the workflow themselves, not having watched a demo. Frame it as upskilling, because that is what it is.

5. Write the playbook the team keeps

One document covering what to use the assistant for, what not to use it for, how to review AI-written code, and the failure modes to expect. It belongs to the team permanently.

Rules that prevent the common failures

• AI-written code gets the same review as any other code, and the reviewer must understand it. "The model wrote it" is not a reason to skip review.
• Tests are not optional. Generated code needs tests where money, permissions and data migration are involved.
• Decide what must not leave the building: client data, secrets and anything under a confidentiality clause.
• The model proposes, a person decides. The accountable author of a change is always a named developer.

What to measure after a quarter

• The same baseline measures, compared with the "before" numbers.
• Quality indicators: bugs found after release, review comments about unclear requirements.
• Adoption: how many developers use the workflow weekly, and where they stopped.

An example of a measured result

Applying an AI-assisted workflow to structure scoping notes cut my spec-writing time by roughly 40% on projects where I used it systematically. It is one measured activity, not total delivery speed. The method is in what the 40% means.

Get help setting it up

The AI-Augmented Delivery Enablement offer follows exactly these steps over about two weeks: process review, workflow design and installation, a workshop and a playbook with a measured baseline. It is bought by the engineering leader, not imposed on the developers. I do not build your automations or supply tool licences.