A.I. in L&D: Just Because You Can Automate It, Doesn't Mean You Should
My LinkedIn feed has become a parade of posts showcasing A.I.-powered learning solutions that promise to revolutionize everything from content creation to learner support. Webinars promote end-to-end L&D automation as the ultimate competitive advantage. ChatGPT prompts for instructional design flood my inbox daily.
The enthusiasm is undeniable—but so is the disconnect from reality.
The Problem: Enthusiasm Without Preparation
There's currently more hype around A.I. adoption in Learning & Development than there is genuine readiness. After auditing dozens of L&D functions across industries, I've observed recurring operational shortcomings that could significantly undermine effective A.I. implementation:
During a recent project with a multinational company eager to implement an A.I.-powered content creation workflow, we discovered their existing instructional design process had seven different variations depending on the department. Their content library contained thousands of documents with inconsistent metadata. Attempting to automate this chaos would have amplified inefficiencies rather than reducing them.
Petre Bica
Senior Digital Learning Consultant
"Automation without standardization is just faster chaos."
Intentional and Responsible Automation in L&D
When considering automation, it's critical to ensure that a specific, practical purpose is being served—and that this purpose benefits all key stakeholders in the organization.
A financial services client was determined to automate their onboarding program through an AI-powered chatbot. Yet in their enthusiasm to scale efficiently, they overlooked crucial human touchpoints that new employees valued. The result would have felt generic and robotic, undermining the cultural integration that was central to their retention strategy.
The pursuit of efficiency can't come at the expense of effectiveness.
Learning is fundamentally about human connection and transformation—elements that require careful consideration when introducing automation.
Where and When to Automate in L&D
Not all L&D processes are equally suited for automation. The most promising candidates typically include:
A healthcare organization successfully implemented A.I. for monitoring completion rates and automatically escalating compliance risks, but intentionally preserved human facilitation for leadership development conversations where nuance and emotional intelligence were essential.
Steps to Follow in Real Life:
- 1Start with problems, not solutions. Identify specific L&D challenges that need solving before considering A.I. applications.
- 2Ensure baseline standardization. Document and standardize your core processes using techniques from Robert Damelio's "The Basics of Process Mapping" before attempting to automate them.
- 3Ask "why automate this?" using criteria like repetitiveness, data-intensity, and scalability requirements.
- 4Begin with hybrid approaches. Implement A.I. as an assistant to human L&D professionals rather than replacing them entirely.
- 5Measure what matters. Define success metrics that balance efficiency gains with learning effectiveness and experience quality.
Final Takeaway
Before embracing A.I.-powered L&D solutions, ensure you've mapped your processes, structured your data, and identified the human elements worth preserving. The most successful automation strategies aren't comprehensive—they're intentional, focusing on augmenting human capabilities rather than replacing them entirely.
Petre Bica
Senior Digital Learning Consultant
"Implementing A.I. in L&D is not about replacing human expertise—it's about redirecting it to where it creates the most value. But you can't redirect what you haven't first understood."
What aspects of your L&D function are you considering automating? Have you established the necessary process foundations first? Share your experiences in the comments below.