Engineering AI Adoption
The client's engineering teams had been experimenting with AI tools individually — some using ChatGPT, others trying GitHub Copilot — but there was no consistency, no governance, and no way to measure whether any of it was actually improving delivery.
Leadership wanted to move AI adoption from ad hoc experimentation to an operational capability, but faced real constraints: compliance requirements from enterprise customers, varied team skill levels, and scepticism from senior engineers who had seen hype cycles before.
They needed a partner who could work alongside their teams — not deliver a strategy deck, but actually help build the workflows, train the people, and wire in the governance from day one.
- Reviewed engineering processes and delivery patterns
- Created an AI adoption plan for the organization
- Trained engineers to use AI tools effectively
- Supported compliance-oriented process improvements
- Introduced AI-assisted UI workflows using Figma-connected tooling
- Implemented first-pass AI PR reviews with human escalation
- Deployed Claude Code skills for test generation and automation
We started with a two-week assessment: mapping delivery workflows, reviewing tooling, and interviewing engineers across the team to understand actual working patterns — not just what management assumed was happening.
From there we designed a phased adoption plan that prioritised high-leverage, low-risk use cases first — AI-assisted code review and test generation — before moving to more complex agent-driven workflows.
Training was hands-on and embedded into real work. Engineers learned to use AI tools by applying them to their actual codebase, not through abstract workshops. We paired with developers during sprints to build muscle memory.
Governance was wired in from the start: access controls, audit trails, and clear escalation paths for AI-generated outputs that touched production code.
Within 8 weeks, moved AI usage from ad hoc experimentation to governed operational practice — PR review cycles shortened by ~60%, first compliance audit passed without findings.
PR review cycles dropped from an average of 2.5 days to under 4 hours, with AI handling first-pass reviews and flagging issues before human reviewers engaged.
Test coverage increased measurably across the codebase as AI-generated test suites were adopted by the team as standard practice.
The client passed their first enterprise compliance audit with no findings related to AI tooling — governance was already in place before the auditors arrived.
Engineers reported meaningful confidence gains in working with AI tools, shifting from reluctance to active adoption within the first month.