Market Insight: Increasing Trust And Adoption In AI For Risk Management
Bill Pennington
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Executive Summary
AI adoption within regulated functions remains constrained by persistent trust concerns. Buyers continue to cite limitations around contextual grounding, explainability, data security and auditability in current AI models and their outputs, pointing to the need for a more architectural approach to embedding AI within risk management platforms. This report outlines how vendors can address these trust gaps through targeted, high-impact design features, such as linking to the source materials underpinning AI outputs. It also provides guidance for both vendors shaping AI product roadmaps and buyers seeking to evaluate the reliability of AI-enabled risk management platforms.About the Authors
Mahum Khawar
Analyst
Mahum is an Analyst at Verdantix, specializing in AI integrations within risk management software and operational resilience. She advises technology buyers and software vendor...
View ProfileBill Pennington
VP Research
Bill is VP Research at Verdantix, where he leads analysis on the evolving and interconnected landscapes of EHS, quality, AI and enterprise risk management. His research helps ...
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