Engineering · Illustrative use case
Software engineering productivity
Adopt AI coding assistance and agents in a way that improves throughput and quality rather than just generating more code.
- Step 1
The problem
Teams have access to AI coding tools but adoption is uneven, quality effects are unclear, and leadership cannot tell whether the investment is paying off.
- Step 2
The AI opportunity
A deliberate adoption programme: tool selection, secure configuration, team practices for review and testing, and measurement of both delivery flow and quality.
- Step 3
Our approach
- 1.Baseline delivery and quality metrics before rollout
- 2.Configure tools against your security and IP requirements
- 3.Establish practices for prompting, review and testing of generated code
- 4.Introduce agentic workflows for well-bounded tasks such as test generation and migrations
- Step 4
Expected business outcome
- Higher delivery throughput
- Stable or improved defect rates
- Less time on repetitive engineering work
Outcome types are described without figures. Numbers come from your baseline and business case, not from a template.
- Step 5
How it is measured
- Lead time for changes
- Change failure rate
- Review turnaround
- Tool adoption by team
Services that deliver this
- delivery
AI Adoption & Optimisation
Embed AI in real workflows, measure outcomes against the business case, and keep improving.
Learn how it works - delivery
AI Governance & Security
Privacy, security, responsible AI and operational risk handled proportionately.
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Other use cases
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