Kevin Thompson, the chief executive officer of U.S.-based Tricentis, has issued a stark warning about the escalating risk of software outages as companies increasingly prioritise speed over quality in an AI-driven software development landscape, the industry insider stated.
“As AI continues to evolve, we believe tech leaders and practitioners need to define what quality means for their organisation to strike the right balance between quality, speed, and cost, while implementing comprehensive testing strategies to deliver better business outcomes,” Thompson said.
He stressed that recent research by his firm had found that, despite mounting pressure to shorten release cycles, nearly two in three global organisations are deploying code without fully testing it, a figure that rises to three in four in the UK, 12% higher than in the U.S.
Poor communication between software development and quality assurance teams and a misalignment between leadership and development teams are identified as critical barriers to achieving software quality, Thompson added, pointing out that a third of firms cite ineffective communication as a key obstacle.
“Define what quality means for to strike the right balance between quality, speed, and cost.”
– Kevin Thompson
Thompson’s warning caution followed the release of his company’s 2025 Quality Transformation Report, which found that two thirds of organisations worldwide are at significant risk of software outages within the next year.
The Report surveyed around 2,700 DevOps and quality assurance leaders, as well as a number of CIOs, CTOs, and software developers from industries including banking and financial services.
In the UK, financial services firms are at risk, with 68% of firms admitting to releasing untested code.
Tricentis Head of UKI, Andrew Power, emphasised the urgency of implementing AI-driven delivery tools to mitigate risks.
“With outage risks in the UK now higher than the global average due to the pressure to deliver software faster than ever, it’s critical that teams tighten their engineering processes,” said Power.
“Agentic AI offers a real opportunity here to plug productivity gaps and raise the bar on software quality and performance,” he continued.
Globally, 82% of organisations are optimistic about leveraging AI agents to handle routine development tasks, allowing teams to focus on more strategic work.
Nearly nine out of ten companies believe they can effectively quantify the ROI from generative AI, underscoring its growing role in software development lifecycles.
GenAI errors
Thompson’s warning comes only weeks after another Tricentis executive, Damien Wong, warned that AI advancements are rapidly introducing new complexities, complications such as code hallucinations and vulnerabilities.
“Where we are seeing bottlenecks is in the validation of that code… code that is auto-generated by some of these GenAI tools introduce errors and all vulnerabilities,” Wong added.
Consequently, software testing has emerged as a critical bottleneck in software modernization efforts, a gap companies like Tricentis seek to address through AI-powered testing solutions.
In today’s hyper-accelerated DevOps environment, tools like Tricentis Tosca and Testim have become indispensable to software quality assurance, Wong argued.
The company employs a GenAI-augmented, model-based, codeless testing approach that fundamentally redefines testing workflows.
“So, model-based test automation effectively abstracts away the business processes of the business model from the underlying technology,” Wong said.
“If there are changes to the application, [these are] automatically propagated to the hundreds of thousands of test cases that are automated.”
Such solutions resonate with developers historically frustrated by testing overhead. A recent Tricentis-commissioned Techstrong Research study revealed a striking trend: 60% of DevOps professionals perceive AI’s greatest value in testing rather than in coding itself.
Yet GenAI’s true disruption extends beyond development teams, democratising the testing process. Non-technical stakeholders can now directly engage in testing activities.
“In the past, if a business user wanted test scripts, they’d speak with the teams that are responsible for engineering test automation,” Wong stressed. “Now, we remove that barrier entirely.”
This approach makes testing more pro-active. Wong shared a compelling example: “We had teams draw application prototypes on flip charts, photograph and scan them, and immediately generate test frameworks — before a single line of code was written.”
Further emphasising privacy and intellectual property protection, Tricentis utilizes metadata instead of raw data. Wong clarified.
“Now, metadata is very different from data; it’s kind of what we always differentiate on. With metadata, you can’t reverse engineer your software.”
Expanding into critical areas like mobile enterprise applications, Tricentis recently acquired Waldo, a SaaS-based, no-code mobile testing platform.
Despite these innovations, Wong underscored the inherent challenges of GenAI, particularly hallucinations in large language models.
“We don not use vanilla LLMs,” he continued. “We contextualize AI specifically for testing environments, ensuring privacy, reducing bias, and minimizing hallucinations.”
Wong stressed the supportive, rather than replacement-oriented, role of GenAI tools: “Our tools are co-pilots, not autonomous systems. We expect human review, but we’re reducing manual effort by 80-90%.”
So, as digital transformation accelerates, effective software testing becomes not just beneficial but essential. Rather than merely detecting bugs, the modern emphasis is on proactively preventing them, Wong concluded.
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