Mastering AI in L&D: Tips for Success in 2025

AI is shaking up Learning and Development (L&D) like never before. The big question isn’t whether AI will change how we train people—it’s how we can use it to keep up with today’s fast-paced workplace.

In this article, I will answer a few fundamental questions for L&D regarding AI:

• How do I buy the right AI product for my needs?
• What are the main pitfalls I need to avoid?
• What are the primary use cases of AI in L&D?
• What is the role of data in ensuring success with AI?

Part 1: How to choose the right AI product in L&D:

I wrote an article on this topic recently. Below, I will be sure to highlight the steps and link the article so you can explore it fully at your own pace.

Step 1: Understand What You REALLY Need

Step 2: Learn the Basics of AI in L&D

Step 3: Ask the Right Questions

Step 4: Be Aware of Bias

Step 5: Get Your Data in Order

Step 6: Choose Tools with Clear Frameworks

Step 7: Prioritise Pedagogy

Step 8: Start Small and Scale

Reading list:

Part 2: AI & L&D | What to Avoid

I wrote an article on this topic a few months back. Below, I will highlight the steps and link the article so you can dive into all the details.

How NOT to work with AI as a learning team.

1. Do not Assume AI will solve all your problems.

2. AI is not a Learning Strategy

3. Only staying in the "L&D bubble"

4. Do not solve the wrong problems faster.

5. Use AI without thinking of your end users.

6. Jump right in with no upskilling on AI -

7. Do not assume AI is always ethical, without bias or privacy concerns

Reading list:

Part 3: Key use cases of AI in L&D

This article is a resource for teams to understand the current & future use of AI.

It is written by a learning nerd (myself) for learning nerds to understand how they can use AI to improve what they’re doing.

This article has three parts to it,

1. AI is NOT a learning strategy, it's an Enabler

2. Four key use cases of AI for learning teams

3. Where are we going... My observations on the future of AI

Reading list:

For specific info on AI & Skills, check out the article below.

Step 4: AI & Data Quality

The effectiveness of AI systems is directly tied to the quality of their inputs.

1. Data Quality and Accuracy: Think of AI like a chef. If you give the chef rotten ingredients, the meal is going to taste awful. If the input data is flawed, the AI's output will be too.
2. Trust and Reliability: If your data is off, AI's results will be unreliable, and trust will go out the window.
3. Continuous Improvement: AI is like a plant; it needs good care to grow properly. To get the best results, you have to keep auditing your data, making sure it’s accurate and represents the real world as closely as possible.

Want to dive deeper into this topic? Check out the article below.

Reading list:

Bonus resource:

As a bonus for finishing the article, here is a playlist of AI resources I built for L&D teams.