The Enterprise AI Playbook: Strategies for Scaling AI in Quality Engineering | Testμ 2025

Testμ Conference 2025
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From this session, one key takeaway for me was how crucial it is to design and implement feedback loops that bring insights from production straight back into your model improvement and process changes. What happens often, as shared by the panelists, is that teams get caught up in retraining their models but forget to close the loop meaning, they miss out on important feedback from developers, defect outcomes, and business KPIs.

For me, this really hit home. Those feedback loops are what separate a one-time success from something that becomes a long-term, sustainable capability. Without them, AI just becomes another project instead of a core asset that continually evolves and adds value. It’s all about continuous refinement and adaptation, making sure you’re not just building something that works today, but something that will keep working and improving in the future.

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During the TestMu 2025 session, I had the chance to hear from industry experts like Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta, as they shared some really insightful thoughts on scaling AI in Quality Engineering. One thing that really stuck with me was when Dror pointed out the often-overlooked step businesses miss after proving a pilot: investing in operational hardening.

So, what does that actually mean? Well, pilots are usually all about proving the AI’s accuracy and showing that it works in a controlled environment. But once you’re moving to production, it’s not just about accuracy anymore; it’s about reliability, auditability, and predictable costs. These are the elements that are often underfunded or neglected.

Dror emphasized the importance of focusing on things like monitoring to ensure the system keeps running smoothly, model/version governance to track how changes to the AI models are handled, and cost forecasting to predict and control expenses effectively. Without this operational focus, things can easily fall apart as the system scales.

To put it simply, after proving the AI works in a pilot, businesses need to make sure they set up a strong foundation that will allow them to handle production smoothly, stay within budget, and avoid any unexpected hiccups down the line.

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Absolutely! The panelists really highlighted some important QA mechanisms for striking the right balance between automation and expert oversight, especially for human-in-the-loop systems. Here’s what I took away:

First off, it’s crucial to set up some gating policies. This means certain high-risk items should always have an AI suggestion, but a human approval is necessary before moving forward. Think of it like a safety net – AI can make the initial recommendation, but the final call needs to come from an expert to ensure there are no unexpected risks.

Another key practice is having immutable audit logs. These are essentially detailed, unchangeable records that track everything – from the inputs that went into the system, to the version of the model being used, and even the confidence level of the AI’s predictions. It’s all about ensuring transparency and accountability, so that you can always go back and see exactly what happened at any stage.

Additionally, it’s a good idea to implement SLA-based escalation paths. In simple terms, this means if something goes wrong or doesn’t meet specific quality standards, there’s a clear, time-based process in place for escalating the issue to the right person or team. This helps avoid bottlenecks and ensures fast resolution of critical issues.

Finally, and this one’s really important – don’t forget about periodic human sampling and bias checks. Even the best AI systems can drift over time or become complacent. Regularly checking for any biases or drifts in the model is essential to keeping things on track, ensuring the AI is still performing as expected and not introducing any unintended issues.

It’s all about creating a solid framework where automation and human expertise complement each other, giving you the best of both worlds.

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I had the chance to attend the panel at TestMu 2025 with Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta, and they shared some really insightful strategies on balancing AI-driven automation with human expertise in testing.

One key takeaway was to leverage AI for the repetitive stuff like triaging, generating candidates, and spotting patterns in the data. This is where AI shines because it can handle large volumes of data quickly and efficiently. But, and here’s the important part, we still need humans for the judgment calls, especially when things get a bit fuzzy or when we have to make decisions based on ethics or context. AI isn’t perfect, and that’s where our expertise comes in.

Another thing they emphasized was the importance of rotating reviewers. This keeps fresh perspectives coming into the testing process and avoids any blind spots that might develop over time. Also, humans are irreplaceable when it comes to exploratory testing AI can’t replicate the creativity and intuition that testers bring when they’re thinking outside the box.

Lastly, it’s all about feedback loops. Having visible feedback channels means that the AI’s output can be constantly validated by real human input. This creates a dynamic, iterative process where both AI and human expertise improve over time.

It’s all about finding that sweet spot where AI does what it does best, and humans focus on the areas that need a bit more nuance and expertise. Balancing the two really is the future of testing!

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Absolutely! During the TestMu 2025 panel session, Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta shared some really insightful ideas on building trust and transparency in AI-powered testing.

So, one of the key points they highlighted was the importance of explainability when it comes to AI-driven decisions. Instead of just trusting the AI’s predictions blindly, enterprises should provide clear, understandable explanations. This could include things like confidence scores, where the AI shows how sure it is about its decision, and providing a short rationale behind its suggestions. Even better if you can show minimal reproducible examples that demonstrate how the AI came to its conclusion!

Another thing they stressed was the need to expose metrics like false positives (FP) and false negatives (FN). This transparency helps teams see where the AI might be making mistakes, and that’s a critical part of building trust. It’s not just about the AI being accurate; it’s about making sure you can measure its performance and know exactly where it’s working well and where it needs improvement.

Lastly, shipping dashboards that connect AI suggestions to actual outcomes is a game-changer. By showing how AI recommendations are directly linked to the results, you can prove the value of these systems in real-time. This level of transparency, combined with measurable performance data, can help build trust a lot faster than just throwing around claims of accuracy.

In short, it’s all about giving people the tools and the data to understand and evaluate how AI is making its decisions. That way, they can feel confident in the process and know exactly what’s happening behind the scenes.

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Absolutely! During the session at TestMu 2025, Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta shared some really insightful thoughts on how AI can significantly enhance test coverage and predict defects in large-scale systems.

The key takeaway? AI can really take your testing to the next level by combining telemetry data with change metadata. What does that mean in simple terms? Well, it helps AI to zero in on the high-risk areas of your code essentially, the spots where bugs are more likely to pop up. Instead of manually testing every little part, AI can prioritize those critical paths that need attention.

Even better, AI can synthesize tests for different variants of your system, helping you cover a lot more ground without the usual heavy lifting. And if you’re running large regression tests, AI is great at spotting flakiness trends, so you can catch those random issues that tend to sneak through when you’re scaling up.

The best part? It expands test coverage in ways that would be nearly impossible for humans alone. This kind of automation really frees up your testers to focus on more strategic tasks, while the AI does the heavy lifting to ensure you’re not missing anything crucial.

So, in essence, AI gives you better, faster, and more comprehensive test coverage, while also helping you predict where issues might crop up, especially in complex, large-scale systems. It’s a game changer for quality engineering teams working at scale!

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Absolutely! During the panel discussion at TestMu 2025, the speakers shared some valuable insights on how to prioritize which QA processes to automate with AI. Here’s the lowdown from the session:

When it comes to AI in QA, the key is to start by automating the tasks that are high-volume, repetitive, and deterministic. Think of processes like flaky-test detection, test generation for stable modules, regression orchestration, and triage. These are the tasks that take up a lot of time and are perfect for automation because they don’t require much human intervention and are predictable.

By automating these areas first, you’ll quickly see a return on investment (ROI). Plus, it frees up your team to focus on more complex and high-value tasks, like exploratory testing or strategic planning. So, the goal is to remove the mundane work, letting humans step into more impactful roles.

In simple terms, you want to let AI handle the stuff that’s boring and repetitive, so your team can work on what truly matters!

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Absolutely! I had the opportunity to attend the session at TestMu 2025 where Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta shared some really practical insights on scaling AI in Quality Engineering. One of the key takeaways was about how enterprises continuously update and retrain their AI models to keep up with the constantly evolving needs of software.

Here’s the breakdown: Enterprises use something called model/version pipelines. It’s like having a roadmap that helps them manage how their models evolve. They start by implementing data versioning think of it as keeping track of different versions of the data used for training models. It’s crucial because, as we all know, data changes over time.

Another important part is drift detection. This is like having a watchdog that monitors if the model’s performance starts to go off-track, especially when the data it was trained on starts to shift. To ensure the model stays relevant, enterprises also schedule regular retrains. These are incremental updates to fine-tune the model with fresh data, so it continues performing at its best without needing a complete overhaul every time.

For big updates that could cause a major shift in how the model behaves, they use gated releases. This means they roll out updates in phases and keep an eye on the impact before fully committing to the change. It’s a way to minimize risk while upgrading the system.

Lastly, automation plays a big role here. AI models can trigger retraining automatically if there’s a data skew (when the incoming data starts to look different from what the model was trained on) or a performance drop. However, for those major updates, they make sure there’s a human sign-off. After all, while AI can do a lot, the final call is made by the people who understand the business impact.

This strategy of keeping the model fresh, flexible, and responsive to change is what really helps enterprises stay ahead in the fast-paced world of software development.

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You know, I was lucky enough to attend a panel at TestMu 2025, where Dror, Janakiraman, Khimanand, Mobin, and Vikul shared some fantastic insights on scaling AI in Quality Engineering. One thing that really stood out to me when they talked about the biggest mistake companies make after a successful AI pilot was about operational integration.

It’s easy to get caught up in the technical side during the pilot phase. Everything looks great on paper – metrics, performance, and all that good stuff. But here’s the kicker: when companies try to scale beyond that pilot, they often run into some nasty surprises. Things like unexpected costs, security gaps, and a lack of clear ownership are what trip people up.

What they highlighted was that companies often forget to build strong operational foundations like setting clear ownership of AI systems, defining service level agreements (SLAs) for the models, managing cost controls, and making sure everything goes through security reviews. If you don’t get those right from the start, you might end up dealing with unexpected billing issues, security vulnerabilities, or even finger-pointing when things go wrong.

So, yeah, scaling AI isn’t just about having the best tech – it’s about getting those practical, operational details sorted out to ensure long-term success. Trust me, this is one “play” that’s easy to overlook but makes all the difference when you move from pilot to full-scale.

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Absolutely! After attending the session with Dror Avrilingi, Janakiraman Jayachandran, Khimanand Upreti, Mobin Thomas, and Vikul Gupta at Testmu 2025, the topic of rewiring the SDLC for predictive quality really stood out to me.

If you’re thinking about how to make predictive quality a reality in your organization, the key is to start capturing change-context telemetry. Let me break that down for you – it’s all about tracking and linking things like code commits, pull request (PR) metadata, deployment details, and runtime telemetry.

Why does this matter? Well, when you can connect all these dots, you’re essentially setting yourself up to predict risk based on specific changes in the code and which teams are responsible for those changes. This way, you can foresee potential quality issues earlier in the process and take action proactively.

So, the first step is to make sure you’re collecting this data – it’s the signal that will guide you toward better quality predictions in the future!

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When it comes to integrating diverse AI tools into your existing QA setup, especially when dealing with legacy systems and CI/CD pipelines, the best approach is to keep it simple and flexible. One strategy that really stood out in the session was the idea of using a thin adapter layer. Think of it as a bridge that helps your new AI tools and older systems communicate smoothly, without causing any major disruption.

Here’s how it works: You containerize your models or APIs (kind of like packaging them up neatly), then you standardize on simple contracts like REST or gRPC. These are easy to work with and act as the common language between old and new systems. Next, you build light orchestration adapters that map the older legacy data to the new AI system’s format. This way, you don’t need to replace everything at once.

The key here is to take an incremental approach. Start by replacing one small piece at a time and slowly grow the confidence in your new AI tools as they seamlessly integrate with the old ones. This way, you’re gradually shifting without a big bang transformation.

It’s all about making small, manageable updates that allow you to get the benefits of AI without completely overhauling your entire infrastructure all at once!

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Absolutely, that’s a great question! If we’re not careful, there’s definitely a risk that engineers might end up spending more time testing the AI itself than the product. But the key here is automation specifically automating the validation process. Instead of manually going through everything, you can reduce that review overload by automating sanity checks.

Another smart approach is to rank the outputs by risk, so engineers only focus on the high-value, high-risk areas that really matter. This way, they’re not caught up in repetitive tasks and can spend more time on the things that truly need their attention. It’s all about making sure the AI testing process is optimized and streamlined, so the engineers can work smarter, not harder!

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I attended the TestMu 2025 panel, and something that really stood out to me was how AI is completely changing the role of Quality Engineering (QE) in organizations. Traditionally, QE was all about reacting to bugs that showed up late in the development process. It was a bit like playing defense finding and fixing defects after they happened.

But with AI, we’re seeing a massive shift. Now, QE is all about being proactive. Instead of waiting for bugs to surface, AI helps us predict where problems might pop up before they even happen. We can spot potential risks early on, prevent regressions, and even provide feedback during the design phase to make sure quality is baked in from the start.

This means that QE is no longer just a checkpoint at the end of the process. Instead, it becomes a continuous, data-driven partner that works hand-in-hand with product and engineering teams throughout the development cycle. It’s like having a guide that ensures everything stays on track, rather than just catching mistakes at the last minute.

AI truly empowers us to move from being reactive to being proactive, making quality a seamless part of the entire development journey.

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Great question! From the session at Testmu 2025, here’s what we learned about overcoming the “pilot-purgatory problem” that often holds AI initiatives back from going beyond the initial prototype stage.

First, securing executive sponsorship is absolutely key. Having leaders who genuinely believe in the AI vision and actively champion it can make all the difference. It sets the tone across the organization and helps get the necessary resources and support.

Then, it’s essential to assign cross-functional owners. AI isn’t just a tech problem—it’s a company-wide initiative. You need product, data, engineering, and QA teams all involved, collaborating to make sure the project stays aligned with business goals.

Speaking of goals, always tie your AI pilots to business outcomes. Don’t treat them like isolated experiments. Instead, make sure that whatever you’re building directly impacts the bottom line, whether that’s improving efficiency, reducing costs, or increasing customer satisfaction. This ensures the project feels relevant and motivates everyone to push forward.

Next, incentivizing adoption with measurable KPIs is a game-changer. Setting clear, tangible goals like how much time will be saved, how much accuracy will improve, or how much revenue will be impacted helps keep the momentum going. It turns AI adoption into something that’s not just a nice-to-have but a must-have.

Finally, don’t let AI outputs sit in separate dashboards that no one really looks at. Instead, integrate AI outputs into your team’s daily workflows. Make it a natural part of their routine, so it becomes second nature to rely on AI for decision-making. This helps AI move from a “project” to a “tool” that people use every day.

In short, it’s about creating a culture that doesn’t just experiment with AI but truly embeds it into the company’s DNA. With the right strategies, AI becomes an enabler, not just a pilot.

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During the TestMu 2025 session, the panelists gave some really valuable insights on how to scale AI models for quality engineering across enterprises. One key takeaway that stood out to me was how critical it is to focus on data governance from the get-go. Here’s what I took away from the conversation:

To make scaling AI models successful, organizations need to get serious about their data platform. This means building a solid foundation that includes things like data lineage, cataloging, schema checks, and access controls. It’s not just about storing data; it’s about knowing where it came from, how it’s structured, and who has access to it.

You should also define clear owners for your data, set data quality SLAs, and put in place policies around masking and anonymization. That way, you ensure your data is not only accessible but also trustworthy and compliant. Data governance shouldn’t be an afterthought it’s a fundamental piece of the puzzle, and when done right, it can really unlock the power of AI in your quality engineering processes.

It’s all about being proactive, setting the right foundations, and treating data governance as something essential, not optional. Without this, scaling AI can quickly hit roadblocks.

Hope this helps!

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Scaling AI in Quality Engineering is about expanding in three key ways: breadth, depth, and maturity.

When we talk about breadth, we’re referring to covering more areas or domains in your testing pipelines. So, as your AI system scales, it starts addressing a broader range of testing needs across different platforms or environments. This helps ensure your testing is comprehensive, covering everything from front-end to back-end, web to mobile, and beyond.

Next is depth. This is where things get more specialized. Rather than using a one-size-fits-all approach, you’d have AI models tailored to each specific domain. For example, an AI model for UI testing might differ from one used for security or performance testing. The goal is to have expert-level models that perform well in their niche area.

Then, we have maturity, which is about optimizing the process. As you scale, you need to start implementing practices like MLOps essentially the DevOps for AI which helps with automating workflows, monitoring models, and ensuring they’re working efficiently. It also includes cost control, because as you scale, managing resources effectively becomes key.

Now, scaling doesn’t just mean adding more AI tools; it involves using a mix of different automation techniques. Depending on the task, you might use deterministic automation for tasks that follow strict rules (like regression testing), ML models for tasks that require learning from data (like predicting test failures), or even LLMs (Large Language Models) and AI agents for more complex tasks, like generating test cases or automating test creation. It’s all about picking the right tool for the job to get the most out of your AI setup.

So, scaling AI isn’t just about throwing more AI at the problem it’s about being strategic in how you expand and optimize across different areas and tasks. It’s a journey of continuous improvement and smart integration!

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During the panel at TestMu 2025, Dror, Janakiraman, Khimanand, Mobin, and Vikul shared some really practical insights on aligning AI with your quality engineering processes. The key takeaway was that it all starts with connecting AI use-cases directly to your business KPIs.

For example, think about things like reducing incidents, improving Mean Time to Recovery (MTTR), or speeding up release velocity. Once you’ve mapped AI to these metrics, it’s crucial to keep track of them regularly.

What’s also really important is making this a continuous process. You don’t just set your KPIs and forget about them. Regular quarterly reviews are essential this is where you can reassess priorities, adjust your AI strategy, and make sure your focus aligns with the business impact you’re driving. It’s all about staying flexible and making sure AI is delivering the value it’s meant to.

In a nutshell, keep measuring, keep reviewing, and keep adjusting this way, your AI efforts stay in sync with your broader business goals and continue to drive tangible improvements.

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During the panel session at TestMu 2025, Dror, Janakiraman, Khimanand, Mobin, and Vikul shared some fantastic insights on how to smoothly integrate AI-powered testing tools into legacy QA workflows without causing major disruptions.

One of the key takeaways was to start small and non-intrusive. Rather than overhauling your entire testing process overnight, begin by introducing AI as an assistant. For example, use AI to suggest tests or help triage issues. This way, your team can ease into the new tools without feeling like they’re forced to change everything at once.

Another great piece of advice was to keep your existing sign-off flows in place. This is crucial for maintaining confidence in your process while you experiment with new tools. You don’t want to disrupt your team’s comfort zone right off the bat.

The panelists also recommended providing adapters basically, creating ways for AI to fit into your current systems seamlessly. Think of these as bridges between old and new tools.

And finally, the idea is to take it slow. Don’t rush into replacing entire workflows. You should only start making bigger changes once you’ve gathered enough stable evidence showing that AI is adding real value and boosting reliability.

So, it’s all about gradual integration. Start small, show value, and then scale at a comfortable pace. That way, you’re not rocking the boat too much, but still moving toward a more AI-powered, efficient testing process.

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Honestly, after sitting through that session at TestMu 2025, my answer is: it’s a bit of both but the “real” story is much bigger.

A lot of teams do start by treating AI like upgraded automation… you know, faster scripts, smarter test generation, things like that. But when enterprises really lean into it, AI stops being a fancy tool and starts becoming part of how quality is continuously built and reinforced.

What the panel really drove home was this idea of predictive quality AI spotting risks before they break something, helping teams prioritize what actually matters, and learning from every release to make the next one stronger. That’s not automation with a new name. That’s a completely different way of building trust in software.

So yeah, the early steps feel like automation 2.0, but once the feedback loops and intelligence kick in, it genuinely reshapes how enterprises think about quality altogether.

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Honestly, one thing that stood out to me in the session was how simply the speakers explained this. They said you don’t sell AI in QA by pitching “AI” at all you sell outcomes.

The way they put it was: Start small. Show something that saves money or reduces risk within weeks, not years.

For example, pick a tiny, high-impact POC maybe reducing flaky tests, catching regressions earlier, or cutting down a chunk of manual effort. Track the hours saved, the incidents avoided, and the actual cost of running the AI piece (cloud, model usage, anything relevant). Then put that in front of leadership in plain numbers.

When senior managers see, “Hey, this saved us X hours last sprint and prevented a production issue that would’ve cost much more,” the conversation changes. They can literally see the payback happening inside a quarter instead of some big futuristic project.

That’s what the panel hammered on: Don’t ask for budget first. Earn it by proving value fast. Once they see real ROI, the purse strings loosen on their own.