In my previous post, I described an experiment where Jira became the single source of truth and AI agents handled large parts of the testing and development workflow.
The interesting part wasn’t that AI could generate tests.
The interesting part was what happened to the people.
As the agents became more capable, I noticed something unexpected: the value of technical QA was no longer at the user story level.
It was moving higher.
Much higher.
The User Story Is No Longer Enough
For years, many technical QA engineers have worked primarily at the user story level.
A ticket arrives.
Requirements are reviewed.
Test cases are created.
Automation is implemented.
The story gets validated.
This model worked because humans were responsible for translating requirements into technical implementation.
Now AI can do much of that translation.
An agent can read a Jira story, generate tests, generate code, execute the tests, fix issues, and repeat the process faster than most teams can coordinate a refinement meeting.
If an AI can understand and execute a user story, then spending all our time validating individual stories becomes less valuable.
The bottleneck moves elsewhere.
The real challenge becomes understanding whether all those stories together create the right product.
The New Responsibility: Understanding the Product
When development becomes increasingly autonomous, somebody still needs to understand why the work exists.
AI is becoming very good at answering:
“What should be built?”
It is still much weaker at answering:
“Should we be building this at all?”
This is where technical QA becomes critical.
Instead of focusing primarily on story-level validation, we need to understand the epic, the customer problem, and the business outcome.
A QA engineer who understands the entire epic can ask questions that no AI agent can reliably answer:
Does this solution actually solve the user’s problem?
Are we introducing inconsistencies across journeys?
Are we creating unnecessary complexity?
Are the acceptance criteria optimizing for the right outcome?
Could multiple stories individually pass while the overall experience still fails?
Those questions exist above the story level.
That’s where future QA work increasingly lives.
This Shift Is Not Just for QA
The same change applies to developers, architects, and other technical specialists.
Historically, technical expertise was often measured by how effectively someone could implement a specific task.
As implementation becomes increasingly automated, understanding context becomes more valuable than producing code.
Developers need deeper product understanding.
Architects need deeper business understanding.
QA engineers need deeper customer understanding.
The people who thrive won’t necessarily be the ones who write the most code.
They will be the ones who best understand the problem space.
AI Rewards Context
One thing I’ve observed while experimenting with AI-driven development is that the quality of outcomes is heavily dependent on context.
The better the context, the better the result.
A detailed epic with clear business goals produces significantly better outcomes than a collection of disconnected user stories.
An AI agent can generate thousands of lines of code.
It cannot magically infer organizational priorities, customer frustrations, regulatory constraints, or business strategy.
Humans still provide that context.
In fact, context may become the most valuable contribution humans make.
The Rise of Product-Oriented Technical Roles
I don’t think technical QA is disappearing.
I think it is evolving.
The future technical QA engineer looks less like a traditional tester and more like a product-focused quality leader.
Someone who understands:
Customer journeys
Business objectives
System architecture
Data flows
Risk areas
Cross-team dependencies
Instead of asking:
“Does this story work?”
They ask:
“Does this epic create value?”
That’s a much bigger question.
And it’s also a much more strategic one.
What Teams Should Start Doing Today
If your organization is adopting AI-assisted or autonomous development, start encouraging technical people to participate earlier in product discussions.
Don’t wait until implementation begins.
Include QA in epic definition.
Include developers in product discovery.
Expose technical teams to customer feedback.
Encourage everyone to understand why work is being done, not just what needs to be built.
Because the reality is simple.
AI is steadily reducing the cost of implementation.
The ability to understand problems, connect ideas, and evaluate outcomes is becoming more valuable every day.
Final Thoughts
For a long time, technical QA has been associated with validating individual requirements.
In an autonomous development world, that is no longer enough.
The future belongs to people who can connect stories to epics, epics to products, and products to customer value.
AI can execute a story.
Humans need to understand the bigger picture.
That is why technical QA needs to shift up.
And honestly, so does the rest of the technical organization.