GENAI ADOPTION
3× FASTER
RELEASES
FOR A B2B SAAS PLATFORM
A fast-growing B2B SaaS company providing a workforce management platform to 300+ enterprise clients across Europe and North America. 180,000+ end users. 130 engineers across three product squads. Bi-weekly releases across web, mobile, and API.
QA had become the primary bottleneck. Regression cycles took 14 working days per release. Manual coverage was inconsistent. The team had no structured approach to AI or GenAI tooling. Leadership needed an external partner to lead a full GenAI adoption – not just for tools, but for culture.
An audit of the client's QA processes uncovered five core blockers:
68%+ of tests were run manually each release. The 4,200+ scenario regression suite kept 11 engineers busy for two weeks, leaving no room for exploratory or risk-based testing.
Test cases were split across Confluence, Notion, and spreadsheets. Many were 18–24 months stale, creating false confidence in coverage and contributing to production escapes.
Ad-hoc AI experiments with no standards, no prompt engineering discipline, and no CI/CD integration. GenAI potential was entirely untapped.
22% of critical defects were found post-release. Each production incident cost ~$18,000 in remediation and SLA credits.
No structured AI training. Previous tool rollouts had failed, leaving the team sceptical. They needed a guided transformation, not another tool drop.
Implementation
QASolvex deployed a 4-person GenAI Adoption team – AI Quality Architect, two Senior QA Engineers with AI/ML specialisation, and a QA Transformation Lead – running a structured 12-week programme across four phases:
Full audit of test assets, pipelines, toolchain, and team skills. All 4,200+ test cases categorised by automation potential, business risk, and AI-augmentation opportunity. Output: a prioritised GenAI Adoption Roadmap with a change management plan to bring the team along.
LLM-Based Test Case Creation: Built a custom GenAI pipeline using RAG over the client's Confluence docs, Jira tickets, and OpenAPI specs. The system auto-generated functional tests, edge cases, negative tests, and boundary scenarios in seconds. 1,500+ net-new test cases produced in 5 weeks – covering workflows never formally tested before.
AI-Driven Test Data Synthesis: GenAI generated realistic, anonymised datasets for payroll, scheduling, and compliance domains – replacing brittle hardcoded fixtures and cutting test data maintenance significantly.
Automated BDD Scenario Generation: GenAI layer integrated into Jira: when a ticket moved to "Ready for QA", Gherkin scenarios were auto-drafted from acceptance criteria and pushed to TestRail. Manual scenario-writing time cut by 80% per ticket.
ML Risk-Based Prioritisation: ML model trained on 24 months of defect history. Dynamically re-prioritised the regression suite before each run – highest-risk areas first, enabling early-exit decisions and faster feedback.
Self-Healing UI Automation: Selenium scripts augmented with AI element resolution. When UI changes broke locators, the AI identified the correct element via visual context and semantic matching – cutting maintenance effort by 65%.
AI Visual Regression Testing: Deployed across 140+ screens. The AI engine distinguished real regressions from intentional design changes with 96% accuracy, eliminating the false-positive overload that had doomed previous visual testing attempts.
GenAI Defect Analysis: AI assistant integrated into Slack and Jira. On test failure, it analysed the stack trace, correlated code changes, and posted a structured root-cause hypothesis – cutting investigation time from 3.2 hours to under 20 minutes.
8 hands-on workshops for the 11-person QA team: AI tool usage, prompt engineering for QA, critical evaluation of AI outputs, and AI ethics in testing. Each engineer shipped at least one AI-augmented workflow during the programme.
Engineers were also trained to use Claude Code and Cursor as daily testing tools – generating test scripts from natural language, reviewing test coverage inline with code, and using AI-assisted code analysis to spot testability issues early in the development cycle.
An internal "AI QA Guild" was established with a shared prompt library, tooling guidelines, and a bi-weekly review cadence – ensuring adoption continued evolving independently after the engagement closed.
Tools & Technologies
Key Achievements:
- Release cycle: 14 days → 4.5 days. Bi-weekly → weekly shipping cadence with no headcount increase
- 74% reduction in manual QA effort per release – team shifted to exploratory testing and quality coaching
- 1,500+ AI-generated test cases in 5 weeks, covering workflows never formally tested before
- Defect escape rate: 22% → 9% – estimated $220,000+ saved annually in remediation and SLA credits
- 80% less time writing BDD scenarios – auto-generated from Jira acceptance criteria via GenAI
- 65% reduction in UI automation maintenance through self-healing element resolution
- 96% accuracy in AI visual regression across 140+ screens
- Root-cause investigation time: 3.2 hours → under 20 minutes via GenAI defect analysis
- Full team trained in AI-augmented testing – including daily use of Claude Code and Cursor for test generation and code analysis
- "AI QA Guild" established – team expands toolchain independently post-engagement
- Client signed a 12-month extension for ongoing AI quality engineering support
Business value
- Manual-heavy QA transformed into an AI-augmented engineering function – same team, dramatically higher output
- 3× faster releases unlocked weekly shipping cadence and a direct competitive advantage
- 1,500+ AI-generated test cases closed long-standing coverage gaps and strengthened the quality baseline
- Defect escape rate dropped from 22% to 9% – $220,000+ in annual savings from year one
- Root-cause investigation 90% faster – less context-switching, faster incident response
- Engineers equipped with Claude Code and Cursor as everyday testing tools – catching testability issues earlier in the dev cycle
- Sustainable AI QA culture built in-house – not a one-time rollout, but a living practice that keeps improving
Ready to Lead Your GenAI Adoption Journey?
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