No, AI won’t replace QA: Lessons from Mercedes-Benz, BCG, and Nagarro

In the world of software testing, AI comes with a persistent myth: that there will be a magic button that makes quality “just work.” Press it once, and tests write themselves, defects disappear, and releases are suddenly risk-free.

The reality looks very different.

In a recent Nagarro expert panel, “AI and software testing – what works, what doesn’t”, leaders from Mercedes-Benz, Boston Consulting Group (BCG), and Nagarro shared how AI is reshaping QA today – and where human expertise remains non-negotiable. Their consensus: AI will not replace QA. It will redefine it.

The future of QA: Rethinking testing in the age of AI and intelligent systems

Let’s begin with what’s not news: Artificial Intelligence is rewriting the rules of software development, and testing is at the centre of that shift. Of course, AI’s impact is evident across every domain today, not least in the field of software testing or quality assurance. Amid all this buzz, there’s also a lurking misconception that AI will make testing effortless. This is where one needs to dig deeper to know better.

The reality is a little more subtle than that. AI is not some wondrous sleight of hand that suddenly eliminates the need for testers; it is a new medium through which testing itself must be reimagined.

Across industries, from automotive and consulting to enterprise tech, leaders are converging on one insight: AI will not replace QA. It will redefine it. The organizations that thrive will be those that treat AI not as automation, but as augmentation.

“AI doesn’t make testers irrelevant; it makes them stronger and faster.”

From automation to augmentation

The early narrative around AI in testing mirrored the early automation boom: reduce manual effort, cut costs, and speed up cycles. But automation, while powerful, is limited by rules - it does what it is told.

AI, on the other hand, can learn and make decisions based on incomplete information. It detects anomalies, understands context, and generates test cases dynamically. That is both its promise and its challenge.

The true shift is not from manual to automated, but from deterministic to adaptive. Testing is moving from a world of scripts to a world of systems that reason. The question for QA leaders is not “How do we automate everything?” but “How do we design systems that continuously learn from change?”

As Thomas Schweiger, AI Testing expert at Nagarro, notes, “AI doesn’t make testers irrelevant; it makes them stronger and faster.”

This redefinition transforms the tester’s role from executor to orchestrator - someone who guides intelligent systems, validates their reasoning, and ensures that human judgment remains the anchor of trust.

The myth of the magic button

Every technological revolution begins with overpromises, and AI in testing is no exception.

Many organizations still view AI as an instant productivity fix. But success with AI depends on groundwork: robust data pipelines, test observability, and a culture that values experimentation over perfection.

Mridul Latka, Head of Digital Data and AI (After Sales) at Mercedes-Benz, warns against what he calls the “magic button myth.” As he puts it, “There is no magic button. You always need a testing mindset, context, and domain knowledge. AI does not have that.”

The teams that rush in expecting instant ROI are often the first to stall. AI does not reward impatience; it rewards consistency. Building models that understand your product, code, and defect history takes time. The payoff, however, compounds.

“AI today is a co-pilot, not a replacement.”

The most effective organizations approach AI adoption through three lenses:

1. Value clarity – knowing exactly which QA challenges are worth automating or augmenting.
2. Data discipline – ensuring clean, accessible, and contextual data.
3. Human oversight – building governance to validate, not just generate, outcomes.

This triad of clarity, data, and accountability is what separates hype from value.

Why accountability must stay human

As AI systems become more autonomous, the boundaries of responsibility blur. When an AI agent generates a faulty test suite or misses a defect, who is accountable - the engineer or the algorithm?

The answer cannot be left to chance. Responsibility for quality must remain human. AI can execute, learn, and adapt, but it cannot own intent. Christian Heib, Global SAP Testing Practice Lead at Nagarro, thinks so too: “AI today is a co-pilot, not a replacement. You are not relieved of your duties or responsibilities, at least not yet.”

Forward-thinking QA leaders are already defining trust boundaries: clear zones where AI can operate independently, where it can suggest but not decide, and where human sign-off is non-negotiable.

This is not bureaucracy; it is intelligent control. It ensures that AI acts as an extension of human judgment, not a substitute for it.

Transparency and traceability must be built into every AI-driven test process. Without auditability, even the most sophisticated systems risk becoming black boxes. And black boxes do not build trust.

“AI is closing the feedback loop between code changes and quality insights.”

The real ROI: Intelligence at scale

So, what does success look like?

It is not about replacing testers or cutting headcount; it is about scaling decision-making capacity.

AI excels where patterns are consistent and data is rich: test impact analysis, defect clustering, and predictive maintenance. Enterprises that invest in structured data and telemetry see measurable benefits - shorter regression cycles, faster triage, and better release confidence.

Ryan Bolt, Global IT Software Engineering Director at Boston Consulting Group (BCG), summarizes this shift succinctly: “AI is closing the feedback loop between code changes and quality insights. We’re no longer waiting for weekly regression tests to know what broke.”

For example, machine learning models can correlate code churn with defect density, identifying risky modules before testing begins. NLP-based systems can analyze requirements and auto-generate test cases aligned with user stories. Intelligent triage engines can cluster similar bugs and suggest likely owners, cutting resolution time dramatically.

But these wins depend on one precondition: discipline. AI amplifies whatever system it is applied to. If your data, processes, or test assets are fragmented, AI will only scale that fragmentation.

The message is clear: before you make AI smarter, make your QA cleaner.

Video snippets from Nagarro's expert panel “AI in Testing – what works, what doesn’t”,
October 8, 2025 featuring Mercedes-Benz, Boston Consulting Group (BCG), and Nagarro

Change management is the hidden metric

The biggest barrier to AI in testing is not technology; it is emotion.

QA engineers are trained sceptics. Their instinct is to doubt, verify, and question. When told that an algorithm can generate test suites or write code, the first reaction is rarely excitement - it is scrutiny.

That is not resistance; it is quality thinking in action. The key is to channel that scepticism into structured experimentation.

Successful AI programs create safe sandboxes - places where testers can play with AI tools, validate results, and build intuition without fear of failure. When testers see their insights reflected in model improvements, trust grows naturally.

Leadership must also shift its messaging. AI in testing is not a replacement narrative; it is a redesign narrative. The goal is not fewer testers; it is more capable testers.

As Mridul Latka from Mercedes-Benz says, “AI will not get rid of testers. It reduces the burden and automates tasks, but you still need people who understand the application and its context.”

AI is not taking testing jobs away. It is taking testing drudgery away.

Building the modern testing ecosystem

For AI to deliver real value, it must be embedded within an ecosystem of observability, collaboration, and governance.

Forward-leaning QA organizations are evolving along five dimensions:

1. Continuous intelligence – moving from periodic testing to continuous risk assessment using AI-driven analytics.
2. Agentic systems – deploying AI “co-workers” that act autonomously on defined tasks like test generation or impact prediction.
3. Data engineering for QA – treating test data and logs as first-class citizens of the AI pipeline.
4. Ethical guardrails – embedding explainability, bias detection, and accountability mechanisms into every test workflow.
5. Cultural enablement – building learning pathways for testers to evolve into AI orchestrators.

These elements turn AI in testing from a collection of tools into a self-improving ecosystem. The goal is not just to test better, but to make quality measurable, traceable, and anticipatory.

Small starts, fast feedback

One of the most consistent lessons from early adopters is to start small but start right.

Choose use cases that are low-risk, high-visibility, and data-rich. Requirements analysis, test data generation, and regression optimization are natural starting points.

As Nagarro’s Thomas Schweiger advises, “Look for where your team still spends a lot of manual effort. Address those repetitive tasks first - that’s where AI can deliver quick wins.”

The pattern that works best is simple:

• Prototype quickly, even if imperfect.
• Measure rigorously, tracking time saved, accuracy gains, and failure rates.
• Iterate publicly, sharing wins and learnings across teams.
• Scale deliberately, expanding only when results are stable.

AI in Testing is less about one breakthrough and more about compounding marginal gains. Each improvement in test accuracy or cycle time adds up, building an organization that learns faster than its competitors.

The future role of QA: From guardians to guides

The next generation of QA professionals will look nothing like their predecessors. They will no longer be guardians at the end of the pipeline; they will be guides steering the flow of intelligence throughout it.

Their core skills will shift from test execution to

• designing prompts and frameworks for AI agents
• validating and curating AI-generated insights
• defining ethical and operational boundaries, and
• translating model outputs into business impact.

This is QA as AI governance in action - the meeting point of engineering precision and ethical responsibility. When done right, testing becomes not just a gatekeeper of quality but a designer of trust.

"AI is democratizing testing intelligence across the entire software delivery chain."

The human multiplier

Perhaps the most profound insight emerging from the AI testing wave is that human creativity, not code, is the ultimate differentiator.

AI can find bugs, but only humans can define what “good” means. AI can learn patterns, but only humans can decide which patterns matter.

 “The real story isn’t just speed, it’s inclusion. AI is democratizing testing intelligence across the entire software delivery chain.”, says Ryan Bolt from BCG.

The future of testing belongs to organizations that respect this partnership: AI for acceleration, humans for direction.

The moment testers stop fearing AI and start training it, they stop being replaceable. In a world where systems write, test, and deploy themselves, the highest value will belong to those who can question, guide, and improve those systems continuously.

That is not the death of QA; it is its renaissance.

From Jarvis fantasy to QA reality

Towards the end of the expert talk, Mridul Latka used a metaphor that stuck with the audience: the age of Jarvis. In the Marvel universe, Jarvis is the ever-present AI assistant to Iron Man – analysing, simulating, advising – but never replacing Tony Stark himself.

AI in testing should be seen the same way. It is there to augment the people who understand the business, the product, and the risk, not to take their place. When QA leaders embrace AI as their Jarvis – a powerful assistant that amplifies their judgment instead of overriding it – they unlock its real value: better decisions, made faster, with more confidence.

Rethinking quality for the AI era

The evolution of testing mirrors the evolution of software itself. It is moving from code validation to system intelligence. The winners will be those who see beyond automation metrics to focus on insight, adaptability, and trust.

Here is what that looks like in practice: measure success not in test coverage but in the speed of learning, and the value of the insights it enables. Judge quality not by defect count but by decision confidence. See AI not as an operator but as a collaborator.

The organizations that adopt this mindset will move from quality assurance to quality intelligence - an adaptive, predictive discipline that learns from every build, release, and user interaction. The rest will remain stuck in cycles of false efficiency, mistaking motion for progress.

The way forward

AI in testing is not about replacing testers; it is about redefining what excellence looks like. It is about freeing human intellect from repetitive verification to focus on higher-order judgment. It is about shifting from reactive quality control to proactive quality design. And it is about ensuring that the systems we trust are not just functional but ethical and explainable.

The testing organizations that lead this shift will be the ones that recognize a simple truth: the future of AI in QA is not artificial. It is deeply, profoundly human. 

Watch the entire panel talk moderated by Nagarro's CTO Thomas Steirer here.