What does practical Generative AI application in L&D look like?

I read a great devils advocate take on AI in L&D from Ross Dickie in the L&D Dispatch.

My fellow namesake focused on the lack of (current) meaningful applications in L&D.

This got my old neurons firing.

→ FYI, read Ross’s take in the Dispatch newsletter – don’t forget to subscribe.

The question of “AI being practically and meaningfully applied in L&D” is very contextual.

Practical applications could be those that streamline mundane time-consuming tasks. Whereas meaningful ones might provide deeper understanding of performance effectiveness through data analysis.

Both can co-exist in the same task.

Contextual Considerations For AI in L&D

The application of AI in L&D isn’t one-size-fits-all. It’s crucial to evaluate:

• What constitutes practicality and meaningfulness?
• Are these measures universally agreed upon?
• How does an meaningful AI application look in different scenarios?

For instance, a large enterprise company may find little meaningful application in using ChatGPT for copywriting, whereas a solo L&D professional in a growing start-up might find it invaluable for enhancing their work. It’s all about perspective – each organisation views technology through its unique lens.

Generative AI tools present a new set of opportunities to enhance human capabilities.

However, too much thinking is finite right now. The natural human decision is to find ways to do more things, not necessarily better things.

The possibilities for practical and meaningful applications are endless given your specific context.

The Ferrari Dilemma

Imagine owning a Ferrari but only driving it up and down your driveway.

Firstly, why would you do this?

More importantly, this is what most teams do with GEN AI tools today. We’re equipped with powerful tools (akin to a Ferrari) but frequently fail to explore their full potential.

This is not new for our industry.

Many incredibly digital tools have arrived over the last 30 years. Some we’ve maximised well, whilst many others we’ve hardly scratched the surface with.

Hopefully, you don’t let GEN AI fall into this abyss.

(A certain someone has a course to help you with that 😉)

Tailoring AI to Your Needs

In integrating AI into L&D, consider the following:

• Understand your context: Not every tool, methodology, or framework fits every situation. What works for others may not suit your specific needs.
  
  
• Assess tasks for AI Collaboration: Focus on tasks in your workflow and identify where AI can genuinely add value. It’s about task-first, not tool-first. Here’s a 2 minute video showing you how to assess your tasks for AI collaboration.
  
  
• Avoid unnecessary comparisons: Your journey with AI in L&D is unique. Don’t get sidetracked by industry benchmarks if they don’t align with your context.

In sum:

Applying meaningful AI in L&D is less about chasing the latest trend and more about understanding and leveraging these technologies within your specific context.

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