In the first quarter of 2024, Air Canada’s AI chatbot told a grieving passenger he could apply for a bereavement fare retroactively — a policy that did not exist. The airline was held liable. The case became a landmark in AI accountability.
The chatbot was not undertrained. It was undertested.
This is the quiet crisis at the centre of the global artificial intelligence boom. Organisations are deploying AI at unprecedented speed — into customer service, clinical decision support, fraud detection, autonomous vehicles, and financial trading systems. The capital flowing into model development is measured in the hundreds of billions. The capital flowing into validating that those models behave correctly, consistently, and safely in production environments is a fraction of that figure.
“We have built a civilisation on software and then been surprised, repeatedly, that software breaks,” says Vadeesh Budramane, Founder and CEO of AlgoShack. “With AI systems, the consequences of that breakage are not a bug report. They are a liability, a regulatory failure, or a patient outcome.”
Budramane has spent 35 years at the intersection of product engineering and software quality — from embedded systems and telecom infrastructure through to the NHS Lorenzo healthcare platform in the UK, one of the most complex regulated software deployments in British history. His Bengaluru-based company, AlgoShack, develops algoQA — an AI Augmented Autonomous Testing platform that sits at the precise intersection of the two forces now reshaping enterprise technology: artificial intelligence and the urgent need to validate it.
His argument is not that AI is overhyped. It is that the infrastructure required to trust AI at scale is being systematically underbuilt.
The Validation Gap
The numbers support the concern.
The 2024 IBM Cost of a Data Breach report found that the average breach lifecycle stands at 258 days, and that organisations using AI and automation in security identified and contained breaches over 100 days faster than those that did not. The Consortium for Information and Software Quality estimated the cost of poor software quality in the US at $2.08 trillion in 2020, including software failures, unsuccessful development projects, and technical debt.
These are not edge cases. They are the structural consequence of an industry that has consistently treated testing as the last step in the development lifecycle rather than the first line of defence.
“The architecture of trust for AI systems does not come from better models,” Budramane says. “It comes from the ability to verify that those models behave correctly under conditions they were not designed for. That is a testing problem.”
The distinction he draws is between AI as capability and AI as infrastructure. The former is visible — language models, image generators, recommendation engines. The latter is invisible until it fails. Autonomous testing is the mechanism by which that infrastructure is validated — not once, at deployment, but continuously, as the systems it governs evolve.
Why Conventional Testing Cannot Keep Pace
The challenge is architectural. Traditional QA approaches — scripted test cases, manual regression cycles, record-and-play automation — were designed for deterministic software. Write code. Define expected output. Verify the match.
AI systems are not deterministic. They are probabilistic. Their outputs vary by context, by training data, by user input patterns that no test script anticipated. Testing an AI system with a scripted framework is the equivalent of evaluating a jazz musician by whether they can play scales.
This is the problem that AI Augmented Autonomous Testing addresses. Rather than prescribing what to test and how, autonomous testing platforms generate their own test cases from application profiling, execute them without human instruction, identify edge cases that predefined scenarios miss, and adapt when the system under test changes. The platform itself exhibits the kind of adaptive intelligence required to validate adaptive systems.
AlgoShack’s algoQA platform, ranked 27th globally among more than 900 test automation companies, delivers this capability across regulated and non-regulated enterprise environments — with up to 90 percent automation coverage, less than 3 percent defect leakage, and 10X productivity gains over conventional testing approaches.
It holds IEC 62304 and ISO 14971 attestations — the standards that govern medical device software and risk management — making it one of the only AI Augmented Autonomous Testing platforms in India cleared for deployment in environments where software failure has direct patient consequences.
The Uncomfortable Question
The AI industry’s current trajectory raises a question that few investors, fewer regulators, and almost no enterprise technology leaders are asking loudly enough: at what point does the speed of AI deployment outpace the capacity to validate it?
The answer, historically, has arrived not in boardrooms but in incidents. The Air Canada chatbot. The algorithmic trading flash crashes. The diagnostic AI that performed brilliantly on training data and inconsistently on patients who did not fit the demographic profile of that data.
Each incident is, at its core, a testing failure. A gap between what the system was expected to do and what it actually did — a gap that adequate validation infrastructure would have identified before deployment.
“Every organisation building AI is also, whether they acknowledge it or not, building a testing debt,” Budramane says. “The ones that will survive the next wave of AI regulation are the ones that started paying it down before the auditors arrived.”
The algorithms will keep getting smarter. The question is whether the infrastructure built to trust them will keep pace.
AlgoShack is a Bengaluru-based AI product company and developer of algoQA, India’s leading AI Augmented Autonomous Testing platform. Ranked #27 globally among 900+ automation companies. www.algoshack.com
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