Most L&D teams already have artificial intelligence (AI) somewhere in their workflow: auto-captioning, transcript search, first-draft scripts, scenario generation. Fewer can point to which of those uses changed a result. Getting meaningful results from AI tools depends on the decisions surrounding them: what goes in, how data is handled, when a person needs to intervene and who ultimately owns the output.

This session examines four applications of AI in learning that hold up under real production conditions, and what each one requires to get there: clean inputs, clear data-handling rules and a named reviewer who owns what ships.

Debbie Richards builds AI literacy and adoption programs for enterprise organizations and develops AI curriculum for ATD. Sponsored by Camtasia, this session draws on what she has seen work and stall across those engagements. Attendees can expect worked examples, a model for placing human review inside AI-assisted work and an honest look at the applications that did not survive contact with real workflows.

Key Takeaways:

• Identify four categories of AI application in learning work and the input quality and review conditions each one needs before it produces something usable.
• Apply a human-AI-human loop model for deciding which steps a person must own before, during and after an AI-assisted task.
• Recognize four patterns that cause AI projects to stall inside learning teams and the signals that show up before they do.
• Ask the right data-handling questions about any AI feature in a learning tool, including on-device versus cloud processing and what that means for regulated content.

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Speaker

Debbie Richards, Technology Consultant, Creative Interactive Ideas Debbie Richards is founder of Creative Interactive Ideas, where she advises enterprise learning teams on AI adoption, and chief learning advisor at Cognota. She facilitates ATD’s AI for L&D Certificate program and authored the ATD TD at Work guide, “Become a Strategic Learning Architect With AI.” She is currently building ATD’s Master AI certificate program along with a series of workshops on AI-assisted design, scenario writing and agent building. Her work centers on performance-first practice: separating the problems training can solve from those it cannot and keeping human judgment in the loop as AI absorbs more of the production work. A past president of ATD Houston, she speaks regularly at industry conferences and works with enterprise organizations across a range of industries.