AI Is Not a Tool. It’s a Maturity Test.

AI is not entering our organisations as just another tool. It is changing the pace at which we build, decide and learn. And that pace exposes something many companies would rather not see.

AI is not a technology problem. It is a maturity test.

If an organisation has struggled with agility in the past, it is likely to struggle with AI as well. Not because the models are insufficient, but because decision processes are slow, responsibilities are unclear and silos still dominate collaboration. AI amplifies whatever is already there. If alignment and adaptability are weak, speed will not fix them. It will magnify them.

For QA, this shift is significant.

There is a common fear that engineering will disappear. It will not. What will disappear is average engineering. AI can generate code, but it cannot replace deep system understanding. It does not truly grasp architectural trade-offs, long-term maintainability or quality under uncertainty. These capabilities become more important, not less. In a world of generated code, the ability to decompose problems, reason about risk and design resilient systems becomes a key differentiator.

The real disruptor, however, is not intelligence. It is speed.

AI accelerates creation to a point where generation can easily outpace governance. If code, test cases and even architectural suggestions are produced in seconds, traditional approval structures begin to collapse under their own weight. When quality remains a late-stage gate, it simply cannot keep up. Organisations then face a choice: slow everything down, or rethink how quality works.

Quality can no longer be treated as a checkpoint at the end of a process. AI systems are probabilistic. They evolve. They produce slightly different outputs each time. This means quality must become a continuous loop: observing, learning, adapting. Instead of asking whether something is “done,” we must ask how it behaves over time, under variation, under pressure.

For QA professionals, this changes the role fundamentally. It is less about verifying fixed outcomes and more about shaping feedback systems. It is about designing observability, defining meaningful guardrails and understanding where uncertainty matters most. Testing becomes less about finding defects in static artefacts and more about managing risk in dynamic systems.

There is also a structural dimension that should not be ignored. AI shifts power within organisations. Those who control the platforms, the data and the infrastructure shape what can be built and how quickly it can evolve. If QA remains at the edge of these discussions, it risks becoming reactive. If it steps into them, it can influence how quality, governance and speed are balanced.

At Agile Testing Days, this is the deeper conversation we need to have. AI does not replace QA. It challenges QA to grow. It asks us to move beyond gates and checklists and to design quality as an evolving capability within fast-moving systems.
The question is not whether AI will change our work. It already has. The real question is whether we are ready to change with it.