Key takeaways:
- Embed AI into existing workflows before creating entirely new ones. The strongest examples focused on accelerating work that teams were already doing rather than inventing new processes. Our members described using AI to support curriculum development, content refreshes, communications, manager coaching, stakeholder intake, and action planning. A common thread was using AI to remove friction from existing work while keeping humans responsible for refinement and decisions.
- Align AI capability expectations to business needs. Several teams are moving away from universal AI training and defining proficiency by role, task, or audience. Examples included role clusters, AI fluency models, leadership enablement programs, and proficiency frameworks. These approaches help create clearer expectations about where AI skills matter most and what success looks like for different groups.
- Address the barriers to scale before expanding use cases. Many organizations no longer struggle to find potential applications for AI. Instead, they are working through governance requirements, security reviews, tool restrictions, uneven access, and unclear ownership. Several members described these operational constraints as a greater challenge than identifying the next AI opportunity.
The Story in Brief
“The challenge is no longer finding AI use cases. It’s creating the conditions that allow successful ones to scale. Members shared practical examples of how AI is helping accelerate work, but they also surfaced a reality many organizations are facing: access, governance, and ownership are determining what’s possible.” — Noelle Peart, Membership Director for the Learning & Development Board
Picking Up the Story
Moving Beyond AI Experimentation
L&D teams no longer need convincing that AI workflows can save time. Learning & Development Board members shared examples ranging from curriculum development and instructional design to voice generation, content refreshes, and communication planning. In many cases, AI is already helping teams increase capacity without increasing headcount.
But the conversation revealed a more significant shift. The most advanced organizations are no longer treating AI primarily as a content creation tool. They’re embedding it into workflows: helping managers interpret employee survey data, guiding action planning, gathering stakeholder requirements before projects begin, and supporting performance conversations.
The result is a new set of questions. As AI workflows becomes more embedded in how work gets done, organizations are being forced to determine who owns enablement, how success should be measured, and what role human judgment should continue to play.
“We want people to use the tools, but there is a real skill that needs to be developed around discernment with AI.”
“We’re using AI agents to help managers interpret the data, help coach them through any reactions they have with the data, and then create some action planning.”
Focus of the Discussion
AI Workflows are being embedded into everyday operations
The more advanced applications are moving beyond content creation. Members shared examples of AI agents that help managers interpret employee survey results and create action plans, intake agents that gather stakeholder requirements before requests reach L&D teams, and tools that support performance conversations and development planning.
As these use cases mature, the challenge shifts toward scale. Leaders described wide differences in access, governance, and AI maturity, alongside questions about proficiency, ownership, and measurement.
