As artificial intelligence moves beyond experimentation and into core business operations, industry focus is shifting from what models can generate to whether their outputs can be understood, verified and used responsibly, experts said.

The shift comes as more companies integrate AI into key workflows, making transparency, traceability and human oversight increasingly important in high-stakes business environments.

Wang Lifei, an enterprise AI expert whose research focuses on workflow and interface design, said trusted AI should be seen not only as a technical or compliance issue, but also as a human-centered design challenge.

"In enterprise settings, trust is not built by making AI sound more confident," Wang said. "It comes from helping users recognize structure, understand uncertainty and intervene when necessary."

Wang's research, presented at the 33rd International Conference on User Modeling, Adaptation and Personalization and ACM/IEEE Human Robot Interaction 2025, proposes two mechanisms designed to make AI systems more visible and actionable for enterprise use.

One is a node-tree interface that allows users to trace, revise and reorganize AI-generated outputs more efficiently, addressing the limits of standard chatbot-style interactions when handling complex tasks. The other is a confidence-rating interface that highlights certainty levels and their contributing factors, enabling users to better judge when an output can be trusted, when it requires verification and when human review remains necessary.

Findings from Wang's studies showed measurable improvements at the interface level. The node-tree approach outperformed standard chatbot interactions in exploratory and decision-oriented tasks, while the confidence-rating design led to more evidence-based recommendations.

Experts said the findings reflect a broader shift in enterprise AI adoption, with attention moving beyond model capability toward accountable decision-making, effective human intervention and more reliable deployment at scale.