Scaling AI Across Teams

Once a few AI bets have worked, the challenge changes from "can we make this work?" to "can every team do this well, safely and consistently?" Scaling rests on three things:

Buying licences does not create value; using them well does. Many organisations see adoption plateau after an initial surge, with a minority of enthusiasts getting real benefit while most people tried the tool once and stopped.

Without a deliberate operating model, each team reinvents prompts, evaluation and integrations, and quality varies wildly. Without upskilling, people either avoid the tools or trust them too much. Without measurement, leaders cannot tell which investments to grow, and AI budgets become an act of faith that the next downturn cuts.

Operating models:

Capability by role:

A champions network, with one or two people per team given time to help others, spreads practice faster than training alone.

Measurement, every quarter:

A retailer's central AI team of six delivered three successful use cases in a year and now has a backlog of 40 requests.

They move to hub and spoke. The central team becomes a platform team: it runs the LLM gateway, evaluation tooling and governance fast lane, and keeps two forward deployed engineers for the hardest projects. Each of five business units nominates two engineers, who spend a quarter following the AI Engineer roadmap and pairing on a real use case with the platform team.

A year later the business units ship their own use cases on the shared platform, the central team's backlog has turned into platform improvements, and the quarterly value review covers eleven systems with one consistent set of measures.

Give champions real time, such as a day a fortnight, and recognise the work. Unfunded champion networks fade within months.

Train on people's own work. A workshop where a team improves its own process with AI changes behaviour; a generic demo rarely does.

Measure with baselines and, where possible, control groups. Adoption alone is not value, and self-reported time savings run high; delivery and outcome data keep the picture honest.

Publish results internally, including what did not work. Teams copy what they can see succeeding.

Your organisation bought 500 licences for an AI assistant six months ago. Usage data shows 35% weekly active, most of them in two teams. Design a 90-day plan to raise meaningful use, including the operating model, enablement, champions and the measures you will report to the executive team.

You can now scale AI across teams with a fitting operating model, role-based capability and honest measurement. This completes the AI Engineering Leadership series.