QualiZeal, a global AI-native Quality Engineering (QE) and AI Assurance partner, released the HFS Take 5 report, Reliable AI at Scale Requires Trust Engineering, Not Legacy QE. The QualiZeal x HFS Research study surveyed 101 U.S. enterprise quality and technology leaders across nine industries.

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The research reveals a widening enterprise AI assurance gap: AI is entering production faster than organizations are building the ownership, validation, and trust measures to govern it.

Key findings:

  • AI adoption is ahead of assurance: 57% have generative AI in production and 36% have agentic AI systems live, while formal AI validation frameworks are in production at only 31%.

  • AI quality accountability is fragmented: 29% report distributed ownership with no single owner; 8% say no one is clearly accountable.

  • Purpose-built AI testing remains limited: Only 15% have a purpose-built approach; 52% test probabilistic AI with frameworks designed for deterministic software.

  • Accuracy is measured more than trust: 88% measure model accuracy, but only 47% bias and fairness, 37% explainability, and 35% regulatory compliance evidence.

  • QE’s mandate is expanding faster than investment: Only 24% say budget and headcount have fully kept pace.

“Enterprise AI has crossed the production threshold, but assurance has not kept pace,” said Pradeep Govindasamy, President and CEO, QualiZeal. “Enterprises need clear ownership of AI quality, purpose-built validation, and continuous evaluation of fairness, explainability, safety, and regulatory evidence. AI assurance must become a distinct discipline—built on strong QE foundations, but designed for probabilistic and increasingly autonomous systems.”

“Enterprises have moved AI into production faster than they have built the means to prove it works,” said Hansa Iyengar, Practice Leader, HFS Research. “Half of them still test probabilistic AI with frameworks designed for deterministic software. This means they can show a model is accurate but not that it is fair, explainable, or compliant. An AI system that cannot be audited or explained is a liability that grows with every release. More test cases won’t fix this. Enterprises need to name an owner for AI quality, score every system against a trust scorecard before it ships, and fund quality engineering for the expanded role it now has.”

The report recommends clear AI quality ownership, broader trust scorecards, and QE investment aligned with the expanded mandate.

QualiZeal’s ValidAIte™ enterprise AI assurance platform supports this shift by helping organizations evaluate trust, safety, governance, and compliance readiness across the AI lifecycle.

About QualiZeal

QualiZeal is a leading global AI-native Quality Engineering and AI Assurance partner delivering business-critical assurance for enterprise IT and AI. Founded in 2021, it serves 70+ global enterprise clients, including Fortune 500 organizations, supported by 1,000+ professionals globally. Its enterprise-grade innovation ecosystem includes ValidAIte™ for AI assurance, QMentisAI™ for agentic Quality Engineering, and NexaAI for trust-first AI development.

For more information, visit https://qualizeal.com.

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