AI usage in software testing is rapidly increasing while in-house expertise lags behind
The pace of AI adoption in software testing has accelerated fast. According to Applause’s 2025 “State of Digital Quality in Functional Testing” report, AI adoption has doubled this year, from 30% in 2024 to 60% today. But while the tools are being deployed at speed, most organizations don’t have the people capable of using them well. Four out of five say they lack the necessary expertise to manage AI testing internally. That’s a systemic gap that closes the door on efficiency gains and increases dependency on outside providers.
If you’re pushing AI into your testing workflows but don’t have the team who really understands what the tech is doing under the hood, you’re likely chasing surface-level results. You’ll automate some tasks, sure, but the deeper improvements, better test coverage, faster feedback loops, smarter prioritization, won’t happen without skill. This makes hiring, training, or partnering with the right expertise non-negotiable if you want to compound returns over time.
At the same time, 87% of organizations still find their testing environments unstable, and 85% say they don’t have enough time allocated to meaningful QA. These challenges haven’t disappeared just because AI has been introduced. If anything, they become more visible at scale.
Ignoring the skills problem slows progress. Solving it, on the other hand, is how you actually unlock the productivity gains AI offers. The organizations that succeed here aren’t necessarily the ones that adopt the most AI, they’re the ones that know how to make it part of how they operate.
AI’s critical role in test case generation and automation is accompanied by uneven application across testing processes
Right now, AI is doing the heavy lifting mostly in one area: generating test cases. Around 70% of organizations use AI for this purpose. That’s strong uptake, and it makes sense, test case generation is repetitive and pattern-based. AI handles it well. Automation of test scripts comes next, with 55% integrating AI here, while 48% are using it for analysis and recommendations on how to improve.
But beyond those high-usage areas, adoption thins out. Capabilities like test case prioritization, autonomous execution, and adaptive test automation, these aren’t as widely used. Given what’s possible, that shows a maturity gap. The technology is available, but many teams haven’t fully integrated it across the board.
There’s an opportunity here for C-level leaders who want operational edge. You don’t have to chase every feature. But when AI starts making decisions, prioritizing tests, adapting in real time, even healing broken automation flows, you’re looking at a long-term reduction in operational drag. It’s not about flashy tech. It’s about eliminating bottlenecks and creating more predictable, scalable testing.
For organizations that are already invested in AI, but aren’t seeing deep results, it’s likely not a technology problem. It’s an application issue. Closing that gap starts with aligning your QA strategy for full coverage, not just surface automation. The few going all-in on these tools and making them part of everyday workflows are already separating from the pack.
Human oversight remains indispensable, particularly when managing the complexities of agentic AI
Agentic AI is evolving fast. These AI systems don’t just automate, they act. They initiate tasks across systems, adapt in real time, and operate without constant human commands. That speed and autonomy come with clear upsides, but it also creates real risk. When something goes wrong, it can spiral quickly if there’s no human visibility baked in.
According to Applause’s latest report, about one-third of organizations are already applying crowdtesting to maintain human-in-the-loop (HITL) safeguards. This signals a growing awareness that even though systems are intelligent, they still need human judgment involved, early, often, and continuously. Organizations that rely entirely on autonomous execution without accountability are creating fragile processes that can’t respond well when unpredictable variables hit.
Rob Mason, CTO of Applause, makes this point clearly: “Agentic AI requires human intervention to avoid quality issues that have the potential to do serious harm, given the speed and scale at which agents operate.” This is not a theoretical concern, it’s operational. Your AI can’t be trusted to manage risk alone, and if it encounters an unforeseen issue, it needs humans built into the loop who are empowered to assess and address problems before they spread.
If you’re rolling out agentic AI at scale, the absence of human review is exposure. But when you design workflows with embedded oversight from the start, from QA design to iterative testing, you get speed and control. That’s where the value compounds.
Integrating quality assurance earlier in the software development lifecycle (shift-left approach) is becoming a best practice
More organizations are moving quality upstream. According to Applause’s findings, only 15% of companies restrict QA to a single stage of the development lifecycle. That figure was 42% last year. This is a clear trend: executives are realizing that pushing testing earlier into the design, planning, and maintenance phases gives teams a head start on catching and resolving issues.
This shift-left strategy gets ahead of problems before they translate into real cost. Development teams aren’t waiting until the tail end of a sprint to test what they’ve built. Instead, they’re validating assumptions earlier, reviewing performance as features evolve, and tightening the feedback loop across multiple phases of development.
For leaders managing software delivery, this change isn’t just tactical, it’s structural. It reflects a mindset shift, one that prioritizes prevention over reaction and builds in confidence at every checkpoint. It also aligns with broader movements in engineering culture, where continuous testing and integrated QA lead to faster releases and fewer rollbacks.
C-suite leaders looking to improve operational efficiency need to embrace earlier QA as standard. It’s how you reduce iteration costs, strengthen reliability, and keep release timelines under control even as the complexity of digital products grows.
A strong emphasis on user-centric testing is essential for meeting modern digital quality expectations
User expectations are higher than ever. Functionality alone doesn’t cut it anymore, products are judged by how intuitive, responsive, and accessible their experiences are. This is why user-centric testing has become a top focus area. According to the latest Applause report, 68% of organizations prioritize User Experience (UX) testing, while 59% conduct usability testing and 54% follow up with user acceptance testing.
This approach reflects reality: a product that works technically but fails to deliver a satisfying user experience will not meet business goals. A strong UX is now a differentiator, tied directly to engagement, retention, and revenue. Testing for it isn’t just about clicking through features, it’s about understanding how real users experience your product across multiple environments and devices.
Leaders should treat UX as a core quality metric rather than an extra layer. Embedding user-driven validation across your testing pipeline ensures that products don’t just meet requirements, they meet expectations. And that’s where long-term value is created.
What’s emerging is a broader shift in how companies view digital quality. It’s no longer just QA’s domain, product, marketing, and design teams all have a stake in delivering user-centric outcomes. That convergence is worth leaning into because the companies doing it are already seeing stronger alignment between product quality and customer impact.
Customer satisfaction remains the key metric for evaluating software quality
Everything points to one truth: customer satisfaction is the final benchmark for product quality. Regardless of how advanced your testing tools are, manual, automated, or AI-powered, your product is only as good as the user’s experience with it. That’s why today’s testing strategies focus heavily on real-world usability and feedback mechanisms.
The Applause study shows that 90% of respondents conduct multiple types of testing aimed at sustaining high digital quality standards. The emphasis is on combining methods, UX, performance, accessibility, and payment testing, to build a complete picture of how well your product functions and how it is perceived.
Customer sentiment and direct feedback are now the top metrics guiding quality assessment across organizations. That shift puts user validation front and center, where it belongs. It gives teams clearer signals, faster response cycles, and a more accurate understanding of product performance.
For executives, this means QA is no longer about pass/fail, it’s about listening and responding. The stronger your connection to live user feedback, the more precise you’ll be in targeting improvements. That clarity drives competitive advantage. And as products scale across markets and devices, this kind of multifaceted, feedback-driven testing is how you stay grounded in what matters most.
Key takeaways for decision-makers
- AI adoption outpaces internal readiness: Organizations are scaling AI in software testing fast, but 80% lack in-house expertise. Leaders should invest in talent development or strategic partnerships to fully leverage AI’s potential and reduce operational risk.
- AI’s impact is limited by fragmented implementation: AI is mostly used for test case generation and basic automation, while advanced capabilities like self-healing tests remain underutilized. Executives should prioritize broader integration to maximize ROI.
- Human oversight is non-negotiable with agentic AI: As AI systems operate independently, human-in-the-loop mechanisms are essential to catch quality issues early. Leaders must embed oversight throughout the QA process to avoid high-impact errors.
- QA is shifting earlier in the development cycle: Testing is increasingly embedded from planning through maintenance, reducing late-stage fixes. Organizations should adopt a shift-left approach to improve speed, quality, and efficiency.
- User experience drives quality perception: UX, usability, and acceptance testing are prioritized across teams, reflecting growing alignment with user expectations. Businesses should expand user-centric testing to strengthen customer engagement and reduce churn.
- Customer feedback now defines quality standards: Satisfaction and sentiment are leading quality metrics, supported by multi-layered testing strategies. Leadership should integrate real-time user feedback into release cycles to stay competitive and responsive.
