Using AI in L&D For Performance Enablement
Applying AI in L&D is a clear and practical guide that helps Learning & Development professionals harness artificial intelligence to improve learning outcomes. Josh Cavalier repositions L&D from course delivery to performance enablement, showing how AI can be used to personalize, measure, and scale learning that genuinely changes behavior.
That is where I truly resonated with the book. Think about the outcomes – not the activity. The course delivery could be done in a million ways, but what matters is the improvement in performance.
Rethink Learning & Development from course delivery to performance enablement
Three Big Ideas Everyone Should Know—and How to Apply Them
1. Reframe L&D as Performance Enablement
Josh challenges L&D professionals to become Human-Machine Performance Analysts. The goal is no longer just delivering content—it’s solving business problems using AI-informed insights.
How to apply it: Start by analyzing where learners drop off, struggle, or disengage. Use simple tools like ChatGPT or AI transcription software to analyze learner feedback and identify patterns. This diagnostic approach allows you to redesign learning with performance outcomes in mind.
2. Use the 4D GenAI Model to Structure Content Creation
The book introduces a four-dimensional model of generative AI:
- 1D (Text)
- 2D (Audio/Visual)
- 3D (Simulations)
- 4D (Real-time adaptive systems) This model helps you decide how sophisticated your learning solution needs to be. How to apply it: If you’re short on time or budget, use 1D and 2D tools to automate video narration, create scripts, or localize content. For critical roles or high-risk tasks, use 3D simulations or adaptive practice environments.
3. Measure What Matters—Behavior and Business Impact
Traditional L&D measures completion rates. This book calls for a shift to tracking applied skills and business outcomes.
How to apply it: Use a 30-60-90-day framework to assess behavior change over time. AI tools can help detect trends and correlations between learning experiences and performance metrics, enabling more confident decisions about program success.
Why L&D Professionals Must Read It—and Who Else Will Benefit
L&D professionals need to read this book because it bridges the gap between AI technology and learning practice. It demystifies how to get started and focuses on building influence by demonstrating value. It’s especially useful for instructional designers, digital learning architects, and learning strategists trying to modernize their function.
This book is also highly relevant for HR leaders, transformation teams, and operations heads who want learning to be measurable and aligned with business performance. Anyone responsible for capability building in today’s complex, digital workplace will find the book both timely and usable.
Three Suggestions for the Next Edition
- Include cross-industry case studies beyond tech and large enterprises.
- Integrate behavioral science frameworks to strengthen learning design.
- Provide a simple AI readiness assessment tool to help L&D teams evaluate where they stand and how to plan next steps effectively.
