Employees spend significant time each day searching for information rather than applying it. For some, this can approach two hours daily. Traditional knowledge systems return lists of articles and place the burden of searching and interpretation on the employee, increasing cognitive load and slowing performance at the moment of need.

This session shares a case study from one of Michigan’s largest credit unions that evolved its knowledge base into an AI-enabled answer engine embedded directly into workflow. Instead of returning documents that force employees to search, read, and decide under time pressure, the system interprets intent and performs the synthesis on their behalf, delivering concise, contextual answers grounded in approved content. The impact was measurable: Time to answer was reduced by 92%, making responses nearly 10× faster, while boosting accuracy from 82% to 99%! Beyond speed, the system reshaped how learning happens at work. Employees can request explanations in simplified language, step-by-step checklists, or concise summaries, transforming static content into adaptive microlearning moments. You will leave this session with a practical framework for evolving existing knowledge bases into workflow learning systems that can measurably improve performance at your organization.

In this session, you will learn to:

• Analyze the difference between article-driven knowledge systems and AI-enabled answer engines
• Identify design and governance principles that allow AI to reduce cognitive load without sacrificing trust
• Explore how adaptive explanation formats support learning in the flow of work
• Apply time to answer and accuracy as performance-based learning metrics

This session is designed for learning and development professionals who are familiar with knowledge bases, performance support, or AI concepts but have not yet implemented AI-enabled workflow learning at scale. Attendees should understand foundational L&D and knowledge management principles but do not need technical AI expertise. The session focuses on strategy, design decisions, governance considerations, and measurable outcomes rather than coding or system architecture.