Ask engineering leaders how they feel about AI-generated code and 78.1% say they trust it more than they did a year ago. But ask them how long it takes to review that code before it merges and the picture is less rosy: 64.8% say AI-generated code needs more review time than code a person wrote, and only 21.9% say it needs less.
AI made writing code faster but verification hasn’t kept up. AI-powered testing platforms are starting to change the equation (Checksum test suites average an 82% lower failure rate than suites maintained by hand), but that raises the question: if AI can generate the tests too, does anyone still need a QA team to run them?
At Checksum, we believe the role of AI is to augment QA engineers, not replace them. Here’s what that looks like for five organizations using our platform.
Key takeaways
- AI testing changes what QA does, not whether QA exists. Across Söderberg & Partners, Reservamos, ClearPoint Strategy, Stellic, and Tend, Checksum customers report reallocating engineering and QA time rather than cutting roles. Judgment and resolution stay human while AI absorbs test maintenance and triage.
- Maintenance is now the machine's job. Reservamos redirected the 20% of engineering time (~$200,000 a year) it used to lose to test upkeep back into building, after Checksum took over looking after flaky, real-time pricing data.
- The review tax is rising; route around it with automation, not headcount. Checksum's State of AI code report found half of engineering leaders report code review cycles growing 25% or more since adopting AI coding tools; innovative orgs are addressing that load by automating test maintenance, with Stellic cutting manual testing time by 40% and ClearPoint now catching six critical bugs a week.
Söderberg & Partners: trust replaces busywork
Söderberg & Partners, a Swedish financial services firm, used to run a long written manual test plan by hand before every release. This process was so painful that releases became rare, making each one riskier. After adopting Checksum, the team went from zero to full E2E coverage in weeks.
"We trust the releases now… we can focus on more important stuff than writing boring tests," says Robert Gergeo, AI Lead at Söderberg & Partners. Checksum didn't replace the team's judgment about what counts as release-ready. The platform allows them to stop manually re-verifying that same judgment every cycle.
Reservamos: automated maintenance
Reservamos, a technology partner for bus companies across multiple markets, needed three dedicated QA automation engineers to keep its test suite alive across every client deployment. Real-time pricing and availability data made tests flaky by nature, and engineers were losing 20% of their time to quality upkeep, costing upwards of $200,000 a year.
Checksum’s auto-healing tests now keep the suite green, even as third-party data shifts minute to minute. The engineering time the team used to spend on maintenance is now spent on building. "Our engineering team moves and innovates faster," says Elias Matheus, CTO at Reservamos.
ClearPoint Strategy: focused on building
At ClearPoint Strategy (a leader in strategic planning and business reporting software), engineers were losing hours to writing and fixing brittle tests instead of building product, yet avoidable bugs were still reaching enterprise customers like AT&T and Kimberly-Clark. Checksum built 250-plus end-to-end tests in under a month, and the team now catches six critical bugs a week.
Co-founder Ted Jackson explains: “Checksum is a game-changer. It saves me so much time writing tests so I can deploy my engineering resources to building tomorrow's technology today—not fixing yesterday's release over and over again.” The team’s focus has changed, not its makeup.
Stellic: “a partner, not a replacement”
Stellic, an academic planning platform for higher education, needed E2E tests that stayed stable as both the team and the customer base grew. Head of Engineering Jeffrey Wescott wasn't looking for another tool that would suck up hours of engineering time. He wanted AI that could generate and heal tests on its own.
“We no longer worry about broken tests or lengthy testing cycles,” Wescott says. The team has seen a 40% reduction in manual testing time with Checksum. Wescott adds, “We can focus on scaling our platform and delivering value to our customers.”
Stellic sees Checksum as a partner rather than just a tool; where a tool sits idle until someone operates it, a partner shares the workload.
Tend: disappearing grunt work
Tend is a farming management platform. Heading into its busiest season, the team ships constantly—and that’s exactly when a broken workflow does the most damage. Checksum solves this problem by running Tend’s full E2E suite on a daily basis. Failures are opened as a JIRA ticket with reproduction steps, screenshots, logs, and failure traces automatically attached.
Engineers start with a diagnosed problem but still decide what a failure means and how to fix it. By keeping the judgment and resolution parts of QA human, AI testing doesn’t remove people from the loop, it moves them to the part that actually needs a person.
What QA teams do with the time back
Söderberg & Partners stopped writing boring tests and started trusting its releases. Reservamos redirected 20% of engineering time back into building. ClearPoint's co-founder pointed his team at tomorrow's technology instead of yesterday's release. Stellic's engineers went from worrying about broken tests to scaling their platform. Tend's engineers stopped hunting for bugs and started triaging pre-debugged ones.
These teams are reallocating resources rather than losing QA or engineering roles. There’s more time for the work that matters, like exploratory testing, release strategy, and shipping quality code faster. That's the multiplier Checksum offers: QA stops being the bottleneck and becomes the function that keeps everyone else moving.
That reallocation isn't a nice-to-have. Checksum’s State of AI code report found that half of engineering leaders say their code review cycles have grown by 25% or more since adopting AI coding tools, and 28.6% say their most senior engineers now spend more time reviewing other people's code (including AI) than building anything of their own. The review tax is real, and it's growing. Teams don’t solve it by hiring more people. They solve it with an AI-powered maintenance layer while trusting their team to keep making strategic judgement calls.
If your QA engineers are buried in maintenance instead of strategy, see how Checksum handles the parts of the job that shouldn't require a person. Request a demo to see our agents work in your own suite.
FAQs
Will AI testing replace QA engineers?
No. Checksum's customers use AI testing to remove the maintenance burden (writing, fixing, and healing tests) while keeping judgment calls (what counts as release-ready, how to fix a bug, what to test next) with people. For example, Tend still has engineers decide what a test failure means; Checksum hands them an already-diagnosed problem with reproduction steps, screenshots, and logs attached.
What tasks does AI testing take off a QA team's plate?
The maintenance layer: writing and healing brittle tests, re-running manual test plans before every release, and triaging failures from scratch. Söderberg & Partners stopped manually re-verifying release readiness every cycle and Reservamos stopped losing engineering time to flaky-test upkeep.
What do QA and engineering teams do with the time AI testing saves?
They reallocate it to work that still requires a person: exploratory testing, release strategy, and building product. ClearPoint Strategy redirected engineering time from fixing brittle tests to building new features, and Stellic's team used a 40% cut in manual testing time to focus on scaling its platform instead.
