Event Details

via Zoom Webinar

January 28, 2027

1:00–2:00 pm EDT

Learning teams are under pressure to produce more content, faster, with flat budgets and limited subject matter expert time. AI makes that possible, but it also introduces risk most organizations have not planned for: content that is fluent, polished, and wrong; unclear ownership of AI-generated material; and decisions no one can explain when an accreditor, client, or auditor asks.

This session presents a practical, governance-first workflow for using AI across the content development life cycle without taking on unmanaged risk. Rather than focusing on tools or prompt tricks, it focuses on the system around them: where AI can responsibly accelerate work, where expert human review must stay mandatory, and how to build verification into the process instead of bolting it on at the end.

Participants will walk through each stage of development, from sourcing approved, source-of-truth material and structuring objectives, to drafting, assessment generation, and media production, to a final quality and risk review that catches inaccuracies, outdated requirements, and bias before release. Real examples from AI-generated video, simulations, and adaptive content in live client programs show where AI changes timelines and capacity, and where it creates exposure that must be managed. The framework is tool-agnostic, so organizations can apply it with whatever platforms they choose.

Learning Objectives

Participants will be able to:

1. Identify the key risks AI introduces into learning content production, including accuracy, ownership, bias, and accountability.

2. Map their current development process to pinpoint where AI can safely accelerate work and where expert review must remain required.

3. Apply a quality and risk review checklist to AI-assisted content, including source verification and an audit trail that holds up under review.