AiThority Interview With Summer Weisberg, CEO,Testlio

Take us through Testlio’s growth journey?

Testlio’s evolution over the past 13 years mirrors the evolution of technology itself. We began with a fundamental question: How do we leverage the collective intelligence of a global community to ensure flawless digital experiences? As the “app economy” matured, so did the definition of quality. It was no longer enough for an app to simply load; it had to perform flawlessly across every possible permutation of device, location, and language. This shifted us from a testing provider to a partner in Digital Quality.

As market demands grew more sophisticated, so did our specialized capabilities. Over the last four years, we’ve introduced new offerings driven by both consumer expectation and global regulation:

Accessibility (A11y): With the European Accessibility Act setting a new standard for accountability, we’ve integrated specialized experts to ensure applications are inclusive by design, not just by coincidence. 

Payments & Global Commerce: The rise of the “borderless” economy requires a level of real-world validation that laboratory testing simply cannot replicate. We introduced payments testing spanning crypto, alternative payment methods (APMs), and cross-border transactions. You simply cannot simulate a failed payment in a local market from a desk in San Francisco. 

Usability: In a world of infinite choice, friction is the enemy of retention. I personally will delete an app quickly if the design isn’t beautiful and easy to navigate. We’ve scaled our usability testing to ensure design isn’t just “clean,” but intuitive for users.

Also Read: AiThority Interview With Arun Subramaniyan, Founder & CEO, Articul8 AI

Today, we find ourselves at the most significant frontier yet: The AI Revolution. For Testlio, it represents a two-fold strategic reality:

Testing the AI-Powered Frontier As companies embed AI at a frantic pace, the risk profile changes. These systems must be safe, compliant, and revenue-protective. We’re seeing growth in “Human-in-the-Loop” validation for AI.

Intelligence Derived from Experience: We aren’t just testing AI; we are fueled by it. Our LeoAI Engine™ is the culmination of 13 years of proprietary data, encompassing over 2.6 million test cases and insights from 600,000+ devices. Throughout 2025 and into 2026, we are investing heavily here to drive efficiency and insights so quality can keep pace with innovation. 

Our platform handles the heavy lifting of speed and data synthesis, empowering our global experts to focus on the high-level context and human insight that machines simply cannot replicate.

We’d love the top highlights of Testlio’s latest launch – LeoInsights?

When I talk to Engineering and QA leaders, the conversation usually circles back to the same problem: Data Fatigue. They have plenty of dashboards, but they still can’t answer with confidence: “Is this release safe?” and “Are we actually getting better?” Quality data is trapped in silos, reporting is manual, and by the time reports hit desks, they’re already stale.

That’s why we launched LeoInsights. It does a few things that I’m particularly proud of:

It replaces the “Manual Report”: It generates executive summaries that translate technical bugs into actual business risk. No more digging through spreadsheets to explain why a release is delayed.

It spots the “Invisible” Risks: The LeoAI Engine™ flags outliers in your data that a human would likely miss until a customer reports a crash.

It provides a yardstick: This is the big one. Every leader I meet asks me: “How do we compare to the rest of the market?” LeoInsights provides benchmark data so they can see where they actually stand against their peers.

What should engineering and business teams be aware of when using AI powered intelligence features to drive Quality Assurance (QA) output and processes?

AI excels at pattern recognition, data synthesis, and repetitive analysis, but human judgment remains essential for interpreting context, understanding user intent, and making nuanced quality decisions.

Start by establishing clear metrics for success before implementing AI-driven Quality Assurance (QA) tools. What specific outcomes are you improving? Faster release cycles? Better bug detection? Reduced manual overhead? AI works best when teams know what problems they’re using it to solve. 

Secondly, AI systems require validation and oversight. Build review processes to verify AI outputs align with business objectives and quality standards. 

What’s broken in most modern QA and testing workflows?

Traditional QA workflows weren’t built for the speed and complexity of modern software development, especially as AI becomes embedded in more applications.

First, AI is fundamentally changing the game. As companies embed AI into their products (recommendation engines, generative features, autonomous decision-making), quality can’t just be a final checkpoint. It has to be baked in from the start, during design, development, and AI model training itself.

Second, there’s the reporting challenge. Quality data lives scattered across bug trackers, analytics platforms, CI/CD systems, and app stores. This fragmentation makes it nearly impossible to demonstrate ROI or move away from being seen as reactive. For mature enterprises shipping daily or weekly, the problem is operationalizing that data in time to actually impact fast delivery cycles. QA can’t afford to be reactive anymore. Signals need to be instant to influence product decisions at the right time.

How can engineering teams build more seamless QA and testing processes that are quick and more accurate?

Bake quality into every phase of the software development lifecycle. Shift from QA to QE (Quality Engineering). QE embeds quality practices throughout development rather than treating testing as a final gate. This means involving testers in design reviews, establishing quality metrics early, and building automated checks into CI/CD pipelines.

Balance AI and human expertise strategically. AI is transforming QA in many ways including generating test coverage at scale and dramatically improving speed. But AI can’t do everything, and it fails often. It can’t judge intent, context, or downstream user impact. Knowing when to use AI versus humans is critical. What are your guardrails? How do you scale human expertise as more AI is introduced into workflows? The teams that succeed will be those who thoughtfully integrate AI as a force multiplier while preserving human judgment where it matters most.

Leverage real-world testing conditions. Access to a global and diverse testing community provides coverage across geographies, languages, and use cases that in-house teams cannot replicate at scale. Unified dashboards, real-time alerts, and executive-ready reporting can also help teams identify issues faster and communicate impact more effectively. 

A few thoughts on the future of AI and its impact on software cycles?

AI is fundamentally changing what “quality” means. Quality is no longer just about whether something runs correctly, but also whether it behaves safely, fairly, and appropriately across diverse real-world conditions.

The role of QA professionals is evolving, not disappearing. AI automates routine regression checks and generates test coverage at scale, but it can’t judge intent, context, or downstream harm. New specialized roles are emerging such as AI Output Reviewers, Bias Evaluators, and Model Safety Testers, where human judgment serves as the accountability layer.

Diversity in testing is essential to catching the issues that matter most. AI failures manifest as bias, hallucinations or harmful responses, and performance degradation across specific user cohorts, devices, or regions. These issues only surface in real-world conditions with diverse user populations that internal teams cannot replicate at scale. You shouldn’t just test with internal developers or a narrow group of users not representative of your actual user base. 

Five thoughts you’d leave our readers with before we wrap up?

Quality is a Competitive Strategy, Not a Checklist. I’ve always believed that in a crowded market, quality is your only real defense. When user experience is the primary differentiator, the companies that consistently ship reliable, well-tested products are the ones that win.

But here is where many leaders get it wrong: they treat quality as a binary “pass/fail” metric. Does it work, or is it broken? If that’s your only lens, you’re missing the bigger picture.

True quality is holistic. It’s about whether the experience is intuitive, accessible, and beautiful. Automation is great for speed, but it’s blind to nuance. It can’t tell you if a workflow feels clunky or if a design choice is alienating users.

AI is a tool, not a strategy. If you don’t have a solid quality engineering foundation, AI will just help you make mistakes faster. It requires the same intentionality we applied to DevOps or Cloud migration. Yes, it can accelerate test generation, spot patterns in defect data, and handle repetitive checks at a scale humans can’t achieve but only if you’re thoughtful about where you plug it in.

You have to define your guardrails early. Decide where human judgment like context, empathy, and accountability remain non-negotiable. The goal shouldn’t be “AI-led” testing. There must be a clear strategy that AI for speed and humans for the high-stakes decisions that a machine simply shouldn’t make.

Think Human, Think Globally: The biggest trap is assuming that because your code is universal, the user experience will be too. An app that flies on the newest phone in London might crawl on a mid-range device in São Paulo or fail entirely on a local carrier in Jakarta. In 2026, “global” means real-world chaos, cultural sensitivity, and true inclusion. With mandates like the EAA now in force, global testing is your best defense against non-compliance. 

Start with your data. You can’t improve what you don’t measure. Establish clear quality metrics, unify your testing data, and build visibility across your organization. 

Empower your QA teams. The shift from cost center to strategic partner starts with giving quality teams the tools, data, and executive visibility they need to demonstrate impact.

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[To share your insights with us, please write to psen@itechseries.com]