Crafting AI Explanations for Every Role in Your Enterprise

Summary: Different enterprise roles need different types of explanations for AI outputs.

Enterprise AI is a hot topic, but many organizations still struggle to achieve meaningful employee adoption and usage. This article focuses on what it takes to develop and deploy AI solutions that both organizations and employees can trust. In particular, it examines how AI explainability helps the people building enterprise AI (developers, system administrators, and domain experts) understand AI-system behavior, build trust in it, and support AI adoption. Because these technical roles bring different goals, expertise, and contexts, explainability cannot be one-size-fits-all.

AI Explainability in the Enterprise

Helping users understand how AI systems work is a core best practice for building trustworthy tools and products. Common approaches include creating traceability, source attribution, and explanations of reasoning and steps.

AI explainability is the degree to which an AI system’s decisions are understandable to humans. It helps users see how and why an outcome was reached.