The long-standing ‘Shift-Left” philosophy in software testing is coming under renewed pressure as AI-driven development accelerates code production beyond the capacity of traditional quality assurance models.
This raises fresh concerns for banks and financial institutions already operating under tight regulatory scrutiny.
“Software runs the world, and how it’s built is ever-changing,” stated Dan Faulkner, the current chief executive officer of SmartBear. “However, that doesn’t mean it’s always built well, or as good as it can be,” he argued.
For QA and testing teams in financial services, where system failures can trigger regulatory breaches, customer disruption and reputational damage, the implications are significant.
The rapid expansion of AI-assisted coding is not being matched by an equivalent evolution in testing practices, creating what Faulkner describes as a growing imbalance between speed and quality.
“AI is helping to crank out new software and applications faster than ever,” Faulkner pointed out. “But the ‘Shift-Left’ movement that gathered steam more than a decade ago is failing to make sure those created applications are actually being adequately tested to ensure they work as intended.”
Shift-Left under strain
Originally designed to catch defects earlier in the development lifecycle, Shift-Left testing placed greater responsibility on developers to validate code before it reached later stages.
While effective in principle, Faulkner argued the model has not delivered the systemic quality gains it promised.
“Shift-Left put the emphasis on developers to test software earlier in the development life cycle,” he wrote in a recent analysis. “The idea was sound; earlier testing would catch problems sooner and improve quality.”
However, the execution has often fallen short.
“In practice, though, the approach was often poorly implemented,” Faulkner continued. “Over time, it led to a de-emphasis of dedicated testing disciplines rather than a true elevation of quality across the software development life cycle.”
This erosion of specialised QA capability is now being exposed by AI, which is dramatically increasing both the volume and velocity of code entering production environments.
“The ‘Shift-Left’ movement that gathered steam more than a decade ago is failing.”
– Dan Faulkner
The scale of the issue is already becoming visible across the industry. According to SmartBear’s latest research, quality concerns are rising sharply.
“60% of software and quality assurance experts we surveyed experienced quality issues in the past year,” Faulkner shared. “Almost seven of ten said quality has already diminished and will slide even more in the next year.”
For banks, insurers and financial service providers, where software underpins everything from payments to trading infrastructure, these trends translate into tangible operational risk.
“One application failure can be devastating both to the application owner and the application user,” Faulkner noted.
Despite the surge in AI-generated code, testing processes remain heavily manual and increasingly misaligned with modern development realities.
“Our research found that almost 60% of teams do more than 40% of their application testing manually,” he pointed out. “At the same time, AI is producing or helping to produce more software code.”
The result is a widening validation gap between what is built and what is properly tested—one that is becoming increasingly unsustainable.
For financial institutions, the issue extends beyond code correctness to real-world performance under stress, regulatory expectations and customer impact.
“Even if developers test code to make sure it’s free of bugs and works on its own, that doesn’t mean the resulting application will work as intended,” Faulkner explained. “Clean code doesn’t entail an effective end-user experience.”
When deployed into complex, high-demand environments typical of banking systems, weaknesses can quickly surface.
“When code gets compiled into applications, and is then required to work under high demand, stuff can break,” he added. “That stuff is what a truly rigorous application testing approach would catch.”
This is where traditional Shift-Left approaches fall short, particularly in an era where production environments are dynamic, distributed and constantly evolving.
From speed to resilience
Faulkner argued that incremental improvements to existing testing models will not be enough. Instead, a broader shift in mindset is required, particularly at leadership level.
“The deeper shift must happen higher up the organisation in terms of mindset,” he said. “Software development can’t continue to be treated primarily as a developer problem.”
In regulated financial environments, this means aligning software delivery more closely with business risk, resilience requirements and long-term outcomes.
“Is speed to market more important than how resilient the application is when it gets there?” Faulkner wondered. “What’s the true cost of an application failure, not only in the moment but also in a long tail of potential damage to brand reputation?”
The answer is increasingly pushing organisations toward continuous validation models that extend beyond pre-release testing.
“Early testing is necessary, but it’s not sufficient,” he said. “Post-deployment monitoring and validation are critical to ensuring applications behave as expected in production.”
“Software development can’t continue to be treated primarily as a developer problem.”
– Dan Faulkner
To keep pace with AI-driven development, QA teams will need to adopt more automated and autonomous testing approaches, particularly in high-frequency, high-risk environments such as banking.
“Autonomous testing that can keep pace with autonomous code creation will be essential to close the gap,” Faulkner said.
At the same time, success metrics for QA must evolve beyond traditional definitions of functionality.
“Success isn’t simply ‘Does it work?’” he added. “It’s ‘Does it deliver the intended business outcome?’”
For financial services firms, this shift aligns closely with regulatory expectations around operational resilience, where proving system reliability under real-world conditions is becoming as important as initial validation.
A competitive reckoning
As AI continues to reshape software development, Faulkner suggested the industry is approaching a tipping point, one that will separate organisations that adapt their QA strategies from those that fall behind.
“The industry today isn’t provisioned to handle the volume of applications and constant change that AI is unleashing,” he stated. “That reality will force a reckoning.”
In highly competitive and tightly regulated sectors such as banking, the consequences will be immediate and visible.
“When one company launches a promotion and its application fails, triggering downtime, lost revenue and brand damage, while a competitor succeeds because it invested in adequate application testing to keep up with AI-accelerated software development, the lesson becomes unmistakable,” Faulkner said.
Ultimately, the firms that rethink the software development lifecycle, embedding continuous, AI-aligned testing and resilience at its core, will define the next phase of competitive advantage in financial services.
“The organisations that reimagine this life cycle the fastest will be the ones that win,” he concluded.
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