Capability 07
AI-enabled Software & Product Development
Use modern AI-assisted engineering selectively to accelerate discovery, implementation and automation — without compromising engineering quality or control.
The challenge
AI can speed delivery up. It can also erode quality.
- Pressure to adopt AI tools without agreed practices for using them.
- Prototypes that are fast to build but unsafe to put into production.
- Generated code merged without the review it needs.
- Automation opportunities that never reach the top of the backlog.
Our approach
Use AI where it helps, and keep engineers accountable.
- 01
Find the opportunities
Identify where AI-assisted workflows genuinely shorten discovery, build or operations.
- 02
Prototype quickly
Validate ideas with working prototypes before committing to a full build.
- 03
Build properly
Implement with the same security, testing and review standards as any other code.
- 04
Govern the practice
Agree how AI tools are used, reviewed and improved across the team.
Representative activities
- AI-assisted development workflows
- Solution prototyping
- Automation agents where appropriate
- Product and platform design
- Integration
- Secure implementation
- Human review and engineering governance
Technology
- Python
- TypeScript
- AWS
- AWS Bedrock
- GitHub Actions
- LLM APIs
Expected outcomes
Faster discovery
Ideas tested with working prototypes, not slide decks.
Quality maintained
Every change reviewed and tested to the usual standard.
Useful automation
Recurring work automated where it pays for itself.
Clear practices
Agreed guidance on how your teams use AI tools.