- Study of 101 US enterprise quality and technology leaders finds 37% of enterprises running AI in production have no named function accountable for AI quality.
Hyderabad, India, November11, 2026: QualiZeal, a global leader in Trust-first, AI-powered Quality Engineering and digital transformation, and HFS Research, a leading global research and advisory firm, have recently launched “HFS Take 5 x QualiZeal,” a joint research study examining whether enterprises are equipped to validate AI systems in production and how Quality Engineering (QE) must evolve for the AI era. The study surveyed 101 enterprise quality and technology leaders in the US.
The study reveals a widening gap between the pace of enterprise AI adoption and the mechanisms needed to assure it. While 57% of enterprises run generative AI in some form of production and 36% have agentic systems live, formal AI validation frameworks remain largely in pilot stages. Only 15% have a purpose-built approach to AI testing, while 52% are testing probabilistic AI systems using frameworks designed for deterministic software. As AI becomes more autonomous and business-critical, the study points to a shift from traditional QE towards trust engineering.
Commenting on the collaboration, Kalyan Konda, Co-Founder, Executive Director & CEO, QualiZeal, said, “Enterprises have moved AI into production much faster than they have developed the discipline to govern it. Through our work with HFS Research, we wanted to put hard numbers behind this gap and understand how organizations are addressing it. The larger issue is that ownership of AI quality is no longer just a testing question; it has become a boardroom question. The study shows that 37% of enterprises are running AI in production without a named function accountable for its quality. That is a significant gap, and it reinforces why organizations need clear accountability and fit-for-purpose validation as they scale their AI initiatives.”
The study also shows that accuracy is proven, but trust is not. While 88% of enterprises measure model accuracy, far fewer measure bias and fairness (47%), explainability (37%), or can provide regulatory compliance evidence (35%).
Speaking about the findings, Madhu Murty, Co-Founder & Head of India Operations, QualiZeal, said, “Quality Engineering is going through a fundamental shift. Earlier, the focus was largely on assuring software. Now, with AI becoming integral to enterprise systems, we are being asked to assure intelligence itself. And quality engineers have a very important role to play in this transition. The study shows that 82% of enterprises expect the QE role to grow significantly. That is why we are investing in AI-skilled quality engineers and building roles that are relevant to this new environment. We don’t see AI and automation replacing quality engineers. We see them elevating the role of quality engineers into that of trust engineers.”
“Most enterprises can prove their AI is accurate. Far fewer can prove it is fair, explainable, or compliant, and that is the test regulators, auditors, and customers will apply. Accuracy gets AI into production, but trust is what lets it scale. Closing that gap doesn’t require another tool. It requires a named owner for AI quality, testig AI as the probabilistic system it is rather than as conventional software, and giving QE a budget that matches its expanded mandate,” said Hansa Iyengar, Practice Leader, HFS Research.
