Software teams are delivering faster than ever – but quality assurance is struggling to keep pace. Many QA functions still rely heavily on manual execution or brittle, code-heavy automation frameworks that require deep technical skills to maintain. In a recent 2025 DogQ report, 77% of companies reported using test automation, yet only 24% had automated more than half of their test cases. This gap isn’t due to lack of intent but is caused by a shortage of skilled talent in the market.
At CBTW, we believe the future of QA Engineers isn’t more code. It’s more contributors. Codeless, AI-powered testing is giving teams a way to increase quality and accelerate testing without relying solely on automation engineers, and it’s transforming how quality gets built into the delivery process.
Automation Is Still Depending on Engineers
Most automation strategies still require dedicated automation QA engineers to build and maintain test scripts. But as applications grow more complex, these scripts become increasingly fragile, breaking when UI elements change, APIs evolve, or test data shifts. Test maintenance becomes a full-time job. Meanwhile, manual testers often remain on the sidelines, unable to contribute to automation due to a lack of coding skills.
This creates friction and overhead. QA is forced into a trade-off: either scale back coverage to maintain speed or accept longer delivery cycles to preserve quality. Neither path is sustainable. According to a 2024 GitLab DevSecOps survey, only 15% of QA teams reported being fully integrated into their organization’s DevOps process, and test execution time is still cited as a top bottleneck to deployment.
Making QA More Inclusive and Scalable
The next generation of test automation platforms – like testRigor, Functionize, and Autify – are designed for accessibility. They allow testers to create test cases in plain English using natural language processing, while AI manages everything from element identification to test maintenance. These platforms support end-to-end testing across web, mobile, and APIs, without requiring scripts or framework setup.
The result is a model where manual QA can automate repeatable scenarios, QA teams can scale faster, and delivery cycles become less dependent on niche technical skills. At CBTW, we’ve helped QA teams streamline their processes, resulting in faster test creation and significantly reduced maintenance efforts, all by empowering teams to automate with less coding. These aren’t isolated experiments – they’re becoming best practices across industries including financial services, logistics, and software.
How CBTW Help Teams Shift to AI-Driven QA
We don’t lead with tools – we lead with outcomes. Our QA enablement model helps teams transition from manual or siloed automation to an integrated, AI-supported QA approach. It unfolds in four practical phases:
1. Identify automation-ready scenarios
We work with delivery teams to pinpoint repetitive, high-impact test cases – such as login workflows, onboarding flows, or search functionalities – that are ideal candidates for codeless automation. These quick wins build momentum and reduce skepticism.
2. Enable manual testers to contribute
Through short enablement workshops, we upskill QA teams on how to use natural language to create, reuse, and maintain automated test cases. This removes the bottleneck of needing developers to write every script.
3. Integrate with delivery workflows
We support seamless integration into CI/CD pipelines, so test automation becomes a routine part of delivery, not a separate phase. AI-driven features ensure stability even as interfaces change, drastically reducing false positives and broken tests.
4. Scale with intent
Once the foundation is stable, we help QA leads grow test libraries, align test coverage with product goals, and manage the balance between manual and automated validation over time.
A Real-World Shift in Capability
In one recent engagement, a digital banking client partnered with CBTW to modernize their QA function. Their QA team, entirely manual, faced growing pressure to support weekly releases. We introduced a codeless AI-powered testing platform and trained the team to build tests in plain English. Within three weeks, they had automated 80% of their critical user flows. Test failures due to UI changes dropped significantly thanks to the tool’s AI-driven “self-healing” features. The team now runs hundreds of tests daily across environments, without adding headcount or pulling developers into QA tasks.
Similar stories are emerging across industries. According to Capgemini’s 2024 World Quality Report, AI-driven QA adoption has doubled in the last two years, with 57% of organizations citing improved release speed and 54% citing reduced QA cost as key benefits.
Building QA That Matches the Pace of Delivery
Modern delivery doesn’t allow for manual QA to be the default or for automation to remain a silo. Yet many organizations still struggle to scale testing without increasing complexity. The opportunity is clear: when automation becomes more accessible, quality becomes a team-wide capability – not just the domain of engineers.
At CBTW, we help QA teams evolve through practical change. Whether you’re piloting your first AI-powered tool or scaling codeless testing across teams, we help you unlock fast wins and long-term value, without rebuilding everything from scratch. Because faster delivery doesn’t have to mean lower quality. With the right tools and the right support, it means better collaboration, broader ownership, and QA that finally moves at the speed of the business.
