The annual QA Financial and E-Commerce Forum returned to New York City recently, at the esteemed Harvard Club, bringing together leading experts and professionals in the field of software quality engineering.
During an afternoon session, Mina Sprengeler, Senior Solutions Engineer at Perforce Software, explored how AI-powered testing is transforming the role of QA in financial services.
Traditionally seen as a gatekeeper, QA is now becoming a proactive force driving innovation, resilience, and risk management.
Sprengeler discussed how AI can help QA teams reduce manual effort, accelerate delivery cycles, and maintain control over potential risks in today’s fast-paced environment.
Following the event in New York City TODAY, QA Financial caught up with the industry insider.
First of all, let’s take a broader look at the sector at the moment: how do you see the role of QA evolving in the financial services industry, especially with the adoption of AI-powered testing tools?
I think AI-powered testing tools are reshaping QA from a traditional gatekeeper role into more of a strategic partner in software delivery and risk management. Instead of just focusing on scripting or coverage metrics, these tools give QA teams the ability to apply their domain expertise to test strategy and real-world user flows—across platforms and environments.
That shift means QA can have a more direct impact on business outcomes, customer experience, and trust. And the teams leaning into AI early are getting a real advantage—they’re speeding up feedback loops, surfacing risks sooner, and freeing up time to focus on higher-impact areas. It’s not just about efficiency; it’s about elevating the role QA plays in delivering reliable, user-centered software.
Can you share an example where a defect or delay in QA led to a significant impact on business outcomes such as revenue or customer trust?
A couple of big examples come to mind—like the Ticketmaster crash for Taylor Swift tickets or the Delta outage linked to the CrowdStrike update last summer. In both cases, the impact was huge: lost revenue, frustrated customers, and a big hit to brand trust. But even outside of the headlines, bugs in production happen—and it’s important to recognize that solid testing isn’t just a quality thing, it’s a cost-saver.
Bank of America conducted an exercise a few years ago that broke down the direct cost to find and fix a defect by SDLC stage by measuring dollar cost and hours and it clearly illustrated how much more expensive it is to fix a bug in production compared to earlier stages like planning, coding, or testing. The numbers were pretty staggering. And in industries like finance, where the tolerance for error is razor-thin, catching issues late can mean real regulatory risk and loss of customer confidence—not to mention the actual financial impact.
What strategies have you implemented to maintain high-quality standards while accelerating release cycles in a high-pressure environment?
From what I’ve seen, the most effective QA teams embrace a shift-left mindset—getting as close to component-level testing as possible. It’s not just about testing earlier; it’s about identifying issues sooner, so releases are faster, cleaner, and a lot less stressful.
One-way teams are doing that is by reducing test environment dependencies through techniques like service virtualization, which allows them to test components in isolation and get faster, more reliable feedback. With AI-assisted development tools like copilots accelerating application development, QA needs to move just as fast—if not faster.
“One of the biggest hurdles in adopting AI for QA isn’t necessarily a skills gap, it’s more of a mindset shift.”
– Mina Sprengeler
At the same time, advances in AI are making shift-right strategies more powerful too. AI-driven defect analysis and production monitoring are expanding QA’s role beyond just pre-release checks. Imagine using logs, telemetry, and real user behaviour to dynamically generate test cases—not just scripts—and validate edge cases with real-world evidence. It’s a way to align QA more closely with actual user impact, not just theoretical coverage.
In your experience, what are the key challenges QA teams face when transitioning from traditional testing methods to AI-driven approaches?
One of the biggest hurdles in adopting AI for QA isn’t necessarily a skills gap—it’s more of a mindset shift. Traditional testing has conditioned teams to think in terms of scripts, step-by-step flows, and locators.
Moving to AI-driven approaches, especially those using agentic tools or prompt-based test generation, requires a different mental model. It’s less about controlling every test step and more about guiding outcomes, training systems, and trusting probabilistic insights.
In my opinion, teams that will succeed are the forward-thinkers—those that embrace this shift. They understand that AI isn’t meant to replace them but to enhance and transform their role. AI can handle tasks like pattern recognition, dynamic test generation, and defect prediction, allowing testers to focus on high-level test strategy, critical flows, and business logic. This isn’t just a change in tooling—it’s a shift in the entire QA framework. And getting buy-in across the team is key to making this transition work smoothly.
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