Is “AI in Quality Engineering” just automation with a new label, or is it fundamentally reshaping how enterprises build trust in software?
How do you sell it to the senior managers who have tight purse strings and don’t have AI in QA in the roadmap or budget?
How much could we rely on AI while UAT and use case Testing , as most of it involves human behaviour?
What top 3 pain points are you seeing at enterprises currently preventing selling these magic tools?
As AI transforms test requirements, scenarios, and automation, how do we measure its success beyond productivity—ensuring compliance, ethical integrity, bias reduction, security, and long-term trust?
A recent study from MIT indicates 95% of GenAI projects fail at enterprise not due to the quality of the models, but lack of understanding and integration with the workflows. how can an enterprise choose the right use case of GenAI in QE projects?
How AI initiatives tied to measurable business outcomes and to define the success metrics upfront and track them?
What is the tipping point at which one should scale AI in testing, how long does it usually take to get there?
What is the single most critical, non-obvious ‘play’ from your new playbook for scaling AI that most companies get wrong when they try to move beyond the successful pilot stage?
What is the right balance between human testers and AI-driven testing?
Which skills are becoming most critical for data scientists as AI and automation expand ?
What are the challenges faced by you in adopting AI in SDLC/ STLC and how you have overcome those?
Do you think AI will eventually replace our current automation testing tools entirely?
How do we maintain AI solutons, for eg: the solution develop today can quickly get outdated in a month. So what is your suggestion to stay
What are the signs or metrics to monitor in order to declare a successful scaling of AI in QE?
Implementing AI is resulting in more work for the associates (review) in the project which is an alarming. How to mitigate this?
How do you validate or ensure the quality and reliability of AI-generated test cases?
What lessons have we learned from failed or stalled AI initiatives in testing?
The Enterprise AI Playbook highlights how adopting the right enterprise AI tool can transform quality engineering by improving efficiency, accuracy, and scalability. Solutions like Agentra empower teams with automation and intelligence, making AI a core driver for delivering higher-quality outcomes at enterprise scale.
What’s the biggest challenge enterprises face when trying to scale AI in quality engineering?
Hey everyone,
Great question, and honestly, this is something a lot of companies are struggling with right now.
The Real Problem: Getting Out of “Pilot Purgatory”
The biggest challenge isn’t getting AI to work in a test environment – most companies can do that just fine. The real problem is moving from those successful small experiments to actual production use across the entire organization.
Here’s a sobering stat: about 95% of AI projects fail when companies try to scale them from pilot projects to full deployment. That’s a massive failure rate. Companies get stuck in what people call “pilot purgatory” – they run test after test, get promising results, and then nothing. The AI never makes it into real operations.
Why Scaling Is So Difficult
Data Quality Issues
AI is only as good as the data it learns from, and most companies have messy data. Information is scattered across departments, outdated, incomplete, or stored in legacy systems that don’t connect. When AI tries to learn from this chaotic data, it produces unreliable results. It’s like learning from a dictionary where half the definitions are missing and the other half are wrong.
Companies that succeed spend significant time cleaning and organizing their data before scaling AI. But most skip this step because it’s expensive and not exciting. Then they wonder why their AI doesn’t work.
Not Changing How Work Gets Done
Many companies treat AI like a plugin – they add it to existing processes without changing anything fundamental. That doesn’t work.
The 5% that succeed redesign their entire workflow around AI capabilities. They don’t just add AI to what they’re doing – they rethink how work should be done when AI is available. This requires mapping processes, identifying where AI adds real value, and training teams on new ways of working. Most companies want AI benefits without organizational change, and that’s impossible.
The Skills Gap
Companies don’t have enough people who understand both AI technology and their specific business needs. These rare individuals can bridge technical teams and business stakeholders, troubleshoot problems, and identify where AI genuinely helps. Hiring data scientists doesn’t solve this – they often lack business context. Relying on business analysts doesn’t work either – they lack technical AI knowledge.
Cost and Integration Challenges
Running AI at enterprise scale is expensive: infrastructure, storage, maintenance, training, and integration all add up quickly. Without clear ROI, executives won’t approve budgets. And proving ROI is difficult when you’re asking for heavy investment in something unproven at scale.
Additionally, enterprise environments are complicated with legacy systems, multiple vendors, security requirements, and different departmental needs. Getting AI to work smoothly in this complex ecosystem is incredibly challenging.
What Actually Works
Successful companies start with data cleanup, redesign workflows around AI capabilities, bring in external expertise, focus on measurable wins, and invest in change management. They recognize that scaling AI is a business transformation project, not just technology implementation.
The human factor matters too. When companies scale AI, they’re asking people to change how they work, which creates psychological resistance. Successful scaling requires clear communication, early team involvement, addressing job security fears, showing quick wins, and providing adequate support.
The Bottom Line
The biggest challenge in scaling AI for quality engineering isn’t technology – it’s organizational. Companies must clean data, redesign workflows, find expertise, justify investment, manage integrations, and handle organizational change. The 95% failure rate exists because most companies underestimate these challenges.