IT & Data
AI coding assistance for internal development
Equip internal developers with an AI coding assistant for code generation, refactoring, test writing, and code review, integrated into the existing development environment and review process.
15–30%
increase in developer throughput
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Very easy to implement. Ready-to-use tools exist. Can be up and running in days or weeks with minimal technical resource.
Minimal data complexity. Works with whatever is readily to hand. No special data infrastructure needed. Most businesses can start immediately.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Controlled studies show real but variable gains, with the largest effects on well-specified, self-contained tasks and much smaller effects on complex work in unfamiliar codebases (GitHub research).
Throughput gains evaporate if code review becomes the bottleneck. Expect review load to rise and plan for it, rather than measuring only lines shipped.
Set an explicit policy on what code and data may be sent to a third-party model, and choose a tier that excludes your code from training. This is a procurement decision, not a developer decision.
Experiment starter: Give two teams the assistant and two comparable teams none for a full quarter. Compare cycle time, change failure rate, and developer satisfaction. Change failure rate is the one that tells you whether the speed is real.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Experimentation FrameworkThis is an augmentation opportunity — see the relevant playbook section for how to approach it.More in IT & Data
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AI Transformation Playbook
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