At Sauce Labs, we’ve spent a lot of time listening to software leaders around the world. What we found in our 2025 Software Testing Vibe Check Report is thought-provoking but promising: 72% of software leaders believe that agentic AI will be trusted for autonomous testing by 2027—a remarkable vote of confidence in technology. But are engineering leaders really on a path to reach this milestone?
Beneath this data point lies a more nuanced story about trust, timing, and the delicate balance between industry’s belief in AI’s potential and how leaders are calibrating the dials to drive the right outcomes for their business. This article breaks down key takeaways from our latest research to help you answer the question: how should you align your AI-driven software quality journey?
AI still isn’t in the circle of trust
We have seen confidence in AI for test automation grow even as our survey was in progress. Still, each organization’s path is unique, and only 9% of leaders currently trust AI to fully deliver test automation. Many leaders are starting by identifying the most critical workflows that take hours to solve manually and can be done faster using AI.
Nearly one-third of respondents (30%) expect AI to reach trustworthy status by 2026, suggesting that many organizations are actively planning for AI-driven testing implementations within their next planning cycles. Another 33% place their bets on 2027, pushing the confidence threshold within two years.
This staged timeline reveals engineering leaders are simultaneously excited and cautious about their AI initiatives. Leaders aren't simply jumping on a hype cycle—they're actively experimenting, learning, and looking for the right AI capabilities to add value and align with their quality standards and risk tolerance.
The message is clear: adopt early, but thoughtfully. Begin piloting AI-driven testing capabilities, but always keep a human in the loop before production.
Teams must leverage AI's analytical power to manage the complexity, scale, and most critical bottleneck in modern software development – the overwhelming volume of test data that slows decision-making and delays releases. By bringing AI-driven automation to surface the best actionable insights, engineering leaders can accelerate testing cycles to deliver quality outcomes. At the same time, doing so frees up human ingenuity to focus on critical thinking, problem-solving, and delivering exceptional user experiences. Ultimately, this symbiotic partnership between humans and AI will define the next frontier of competitive advantage.
The autonomy paradox
Here's where the data gets particularly revealing. While 72% of leaders expect to trust autonomous testing by 2027, only 56% believe the ideal approach should rely primarily on AI agents. Meanwhile, a commanding 85% favor a hybrid approach combining human and AI strengths.
This isn't necessarily a contradiction—it's pragmatism in action. Engineering leaders recognize that the trustworthiness of AI tools and the ideal use cases to drive value for their organizations are not the same. Trust in AI's abilities doesn't mean removing humans entirely from the process. Instead, leaders envision AI as a powerful amplifier of human expertise rather than being a replacement for it.
This hybrid model makes strategic sense. AI agents excel at scale, speed, and tireless execution—running thousands of test scenarios without fatigue, catching edge cases that humans might miss, and analyzing vast datasets in seconds. Humans, meanwhile, bring contextual understanding, ethical judgment, and the ability to ask "what if" questions that might not exist in training data. The future of testing isn’t human or AI – it’s the orchestration of both.
Where AI shines brightest (today)
Not all testing tasks are created equal when it comes to AI readiness. Our research highlights three areas where software leaders have the highest confidence in agentic AI today:
Anomaly detection (61 %): AI's pattern recognition capabilities make it exceptionally suited for identifying deviations from expected behavior. Where a human tester might review hundreds of logs looking for irregularities, AI can process millions of data points, flagging outliers that warrant human investigation.
Behavior analysis (59%): Modern applications involve complex user journeys and intricate system interactions. AI agents can simulate and analyze user behavior patterns at a scale impossible for manual testing teams, uncovering issues that only emerge under specific conditions or usage patterns.
Data analysis (53%): Software testing generates enormous volumes of data—test results, performance metrics, error logs, and coverage reports. AI's ability to synthesize this information, identify trends, and generate actionable insights transforms testing from a pass/fail exercise into a continuous intelligence operation.
What's notable about these high-trust areas is their analytical nature. Software leaders trust AI most for tasks involving pattern recognition, data processing, and systematic analysis—exactly where machine learning algorithms demonstrate clear advantages over human capabilities.
Bridging the trust gap
So how do we close the gap between 9% current trust and 72% projected trust by 2027? First, AI models must demonstrate consistent and repeatable accuracy across diverse use cases and testing scenarios. One-off successes won't build institutional confidence—leaders need evidence of sustained accuracy and dependability.
Second, the industry needs to understand how AI makes its decisions. When an AI agent flags an issue or declares a build ready for production, teams must understand the reasoning behind those decisions. Black-box AI (vague decision-making processes) will never achieve enterprise-grade trust.
Third, organizations must develop new skills and processes. The shift to agentic AI testing isn't just a technology upgrade—it’s about new mindsets, processes, and roles across DevOps, QA, and product teams.
The road ahead
The message from software leaders is clear: AI autonomy in testing isn't a question of "if" but "when" and "how." By 2027, we expect AI testing to reach a level of maturity that makes full autonomy feasible—but only for organizations that start preparing now.
At Sauce Labs, we see this as a partnership between humans and AI agents—a reimagining of quality in which machines handle scale and complexity, and people guide judgment and creativity. The future belongs to those who thoughtfully augment organizations through optimal collaboration between human insight and machine intelligence.
Final thoughts
As a product leader, I’m convinced that trust in AI won’t be built by technology alone—it will be built through transparency, accountability, and shared success. Our mission at Sauce Labs is to empower teams to harness AI responsibly, so they can deliver faster, higher-quality experiences with confidence.
If you’d like to explore how organizations are evolving toward AI-driven testing maturity, download our full 2025 Software Testing Vibe Check Report or visit our Sauce AI Hub to learn how we are shaping the future of Software Quality Intelligence together.
