Our customer, a leading player in the technology and development area, was under a lot of pressure to deliver high-quality software and to deliver fast. With complex projects running across multiple teams, it became clear that existing QA practices were no longer enough. Modernising their quality assurance approach was not just nice to have, but essential to keep up with expectations and stay competitive.

Success Story: Accelerating Quality Assurance with AI

The organisation recognized that AI could be a game-changer, but translating potential into practical results required both vision and hands-on support.

Challenges

The journey began with several key challenges:

Sustaining Momentum: Ensuring that the adoption of AI in QA didn’t stall after initial enthusiasm, but instead became embedded across teams and projects.

Bridging the Gap: Turning AI’s technical capabilities into concrete, day-to-day improvements in QA processes and outcomes.

Empowering People: Enabling QA professionals with different technical and non-technical backgrounds to confidently apply AI in their own roles.

Our Approach

To address these challenges, we designed and delivered two tailored workshops:

Requirements and Specifications with AI: This session focused on how AI can help generate, structure, and refine requirements, test plans, and test cases. Participants explored real-world scenarios where AI tools streamlined the creation, refinement, and reuse of QA artifacts.

Improving Test Automation with AI: Here, the emphasis was on accelerating the journey from code and specifications to robust automated tests. Attendees learned how AI could interpret source code and API specifications, enabling faster and more reliable test automation.

Both workshops blended expert presentations with interactive, hands-on exercises. Participants worked through practical tasks relevant to their own QA environments, fostering lively discussions about immediate applications and future opportunities. The collaborative format ensured that everyone—from seasoned engineers to newcomers—could see how AI might transform their work.

Results and Value Delivered

The impact was immediate and measurable:

Teams gained a clear understanding of where and how AI could add value within their QA processes.

The creation and maintenance of test plans and test cases became faster, with improved coverage and quality.

Release readiness and prioritization decisions were enhanced by AI-assisted analysis and actionable insights.

Automated tests could be generated more quickly from source code, API specifications, and structured inputs.

Best practices, methods, and examples were shared across teams, enabling the scaling of AI-enabled test automation throughout the organisation.

Conclusion

By investing in targeted, practical workshops, our customers not only accelerated their adoption of AI in QA but also empowered their teams to drive continuous improvement. The result: a more agile, innovative, and quality-focused organisation—ready to meet the demands of tomorrow’s technology landscape.