Fabric partnered with the organisation to develop an AI-assisted testing enablement framework that standardised how developers analyse coverage gaps, generate unit and integration tests, and improve code quality.

Rather than delivering a one-off solution, Fabric created a reusable testing playbook, AI skill kits and developer guidance that could be adopted across multiple engineering teams with minimal onboarding. The pilot was implemented across teams with varying technology stacks and testing maturity, ensuring the approach was validated in real-world production environments.

The engagement focused on enabling developers to spend less time writing repetitive tests and more time delivering value. Alongside AI-assisted test generation, the solution introduced consistent testing conventions, automated coverage analysis and AI-supported merge request reviews, helping teams identify quality issues earlier in the development lifecycle.

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The pilot demonstrated that AI can meaningfully improve engineering productivity while increasing confidence in software quality, laying the foundation for broader enterprise adoption.

Key outcomes included:

• Up to 50% reduction in time spent writing unit tests for pilot teams, with an average productivity improvement of around 20%, equating to an estimated 1–2 hours saved per developer each week.
• 100% of pilot participants said they would continue using the AI testing tools, demonstrating strong developer confidence and adoption readiness.
• AI-generated tests received an average 4/5 quality score for correctness, readability and confidence during review and merging.
• Improved visibility into test coverage gaps, enabling teams to strengthen regression protection and incrementally increase automated test coverage.
• Delivered a reusable AI Testing Playbook and standardised skill kits that provide a scalable foundation for enterprise-wide testing enablement.
• Established consistent testing standards across diverse technology stacks, improving collaboration and making AI-generated outputs easier to review and trust.

Developer feedback reinforced the value of the solution, with teams noting that generated tests were comprehensive, aligned with engineering standards and often covered scenarios that would otherwise have been overlooked. Most importantly, the pilot demonstrated that AI-assisted testing can be successfully embedded into enterprise engineering practices, helping a large national organisation improve software quality, accelerate delivery and build a scalable capability for the future.

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