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Energy and industrial software

Backend systems with AI-assisted development

Introduce AI-assisted coding into backend development with review, testing, security, and maintainability controls.

Practical fit

Where it fits

AI tools can help backend teams draft code, tests, migrations, integration adapters, and documentation. The value is highest when teams define where AI is appropriate and how generated work is reviewed before it affects production systems.

Implementation areas

Typical applications

  1. 01

    Drafting service code, API handlers, and integration adapters

  2. 02

    Generating unit and integration test candidates

  3. 03

    Explaining legacy code paths and dependencies

  4. 04

    Preparing technical documentation for internal systems

Evidence and governance

Controls to establish

  • Code review standards for AI-assisted contributions
  • Repository and issue data kept within approved tooling boundaries
  • Security and dependency review before merge
  • Automated test coverage for generated or modified paths
  • Traceable decisions for architecture and production-impacting changes

Expected outcome

A useful workflow with retained control

Higher development throughput without weakening engineering ownership, review discipline, or production reliability.