Over the past year, AI has changed the conversation around software development.
Much of the focus was initially on speed. Teams are using AI to write code, generate documentation, create test cases, and accelerate delivery. Alongside that, we’ve seen companies reduce headcount and position AI as a productivity multiplier.
At the same time, I’ve noticed I’m seeing more Director of QA and Director of Quality Engineering roles appear across the market.
That kind of felt contradictory. If AI is making software development faster and more efficient, why are companies investing in senior quality leadership?
After a conversation with an engineering manager, I think the answer is becoming clearer.
AI Makes It Easier To Build Software. It Does Not Make It Easier To Understand Software.
One of the biggest misconceptions in the AI conversation I see among my recruiting peers is that generating code and understanding systems are the same thing.
They are not.
Modern applications rarely exist in isolation. Behind a single feature are business rules, integrations, APIs, governance requirements, security controls, and dependencies across multiple teams.
AI can help create code quickly.
It cannot automatically understand every relationship that exists inside a complex system.
That distinction becomes more important as development cycles accelerate.
The faster software is produced, the more important it becomes to understand where it can fail.
Why Quality Engineering Is Moving Up The Value Chain
For years, QA was often viewed as a support function rather than a strategic one. In many organizations, quality teams were expected to validate software after it had already been built. It was the last stop in the software development lifecycle.
AI is changing that equation.
When software can be created faster than ever before, quality is no longer just about finding defects. It becomes about understanding risk, analyzing dependencies, and identifying problems before they reach production.
During our conversation, Andrew shared concerns about organizations embracing AI-driven development without investing in the quality practices needed to support it.
One example came from a recent fintech interview where he learned teams were heavily relying on AI-generated code despite lacking a mature quality organization.
In highly regulated environments, that creates risk.
The issue is not AI itself.
The issue is assuming AI-generated output is automatically correct.
The Problem With Blind Trust
This applies to testing as much as development.
Many teams are using AI to generate test cases and automation scripts. Those capabilities can be useful, but they are not a substitute for critical thinking.
Andrew shared that teams he worked with experimented with AI-assisted testing and discovered an important limitation.
The generated tests often covered expected scenarios but missed edge cases and system interactions that experienced engineers would investigate further.
The lesson was not that AI testing is ineffective but that AI should be treated as an input, not an answer.
Quality engineering still requires people who understand how systems behave under real-world conditions.
Faster Releases Create New Pressure
Another point that stood out was the relationship between AI and release velocity.
Many organizations expect AI to increase productivity. Yet some are simultaneously reducing engineering staff and quality resources.
That raises an important question.
If AI helps teams deliver more software, who is responsible for validating that additional output?
Andrew pointed to release environments where teams shipped hundreds of deployments per year. In those situations, quality processes become more important, not less important.
Without sufficient quality coverage, faster development can create larger testing backlogs, increase production risk, and introduce technical debt.
Speed alone does not create business value.
Reliable software does.
Why I Think We’re Seeing More QA Leadership Roles
The increase in Director of QA and Director of QE roles may be a signal that organizations are beginning to recognize this challenge.
Many of these leaders are not being hired to manage larger testing teams.
They are being hired to evaluate quality practices, identify gaps, and help organizations adapt to a development environment that is moving much faster than before.
Companies know they have a problem.
Many are still trying to determine what the solution looks like.
My thanks to Andrew Kordula for taking the time to share his perspective and experiences. The conversation gave me a lot to think about regarding where quality engineering is headed!