3 Unexpected Insights I Learned About How L&D Teams Are Using AI Today

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 TODAY’S THOUGHTS ☠️

Hey there 👋,

A few weeks back, I released phase one of my findings from a survey I conducted with my newsletter audience (you) to understand:

• What AI tools do you trust and use today

• How do you use these tools for meaningful ROI

• The habits, behaviours and choices that have emerged using AI as a co-worker

• Weird, wonderful and surprising use cases

You can get the full analysis of that here.

I’m using this post to share a bit more about what I learned from those findings.

I like taking time to reflect on something I’ve published and revisit it to see if I can uncover any angles I missed. This is the purpose of what you’re reading right now.

So today, we’re exploring 3 unexpected insights I learned from my latest survey about how L&D teams are using AI.

Get your tea or beverage of choice ready 🍵.

We've got lots to discuss!

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 IN THIS DROP 📔

• Goodbye content factories

• Shadow AI use is rising in L&D

• Where I still get the most value from AI

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 THE BIG THOUGHT 👀

3 Unexpected Insights I learned About How L&D Teams are using AI today

I’m not going to keep you in suspense.

As I revisited and reflected on the original analysis, and stalked the data once again, these 3 insights caught my eye:

1/ Practitioners are ditching the Content Factory mentality with AI

Let’s be real, the true story of AI with L&D these past 3 years has been about doing more and faster with what the industry feels safe in - content.

That should surprise no one.

Until companies and the industry rewire what we do and how we do it with AI as a layer, this will continue for some time. Despite that, I was hugely encouraged to see the most sophisticated users in the dataset progressing beyond this.

These users aren’t asking AI to just create, they’re asking AI to argue with them.

I get that sounds weird, but it’s actually useful. Smart L&D pros are working with LLMs to challenge them, question assumptions and identify blind spots. All in the pursuit of doing their best work.

One respondent even went as far as to share that they create customised AI assistants to review their design work with a sceptical eye to help draw out those blind spots as somewhat of a sparring partner.

Here’s a simple set of questions to get AI to interrogate your work:

• Challenge my assumptions about ‘x’

• What’s another way to look at this insight?

• Here’s my thoughts, am I missing anything here?

2/ Teams are using mandated and shadow AI stacks

While most respondents have access to a suite of AI tools at work, they’re not huge fans of them.

Many respondents reported poor performance due to instances of approved company AI tools not being on the level of the widely available paid models that many use personally. It might be no surprise that Microsoft Copilot took most of this hate.

It seems many have access to Microsoft’s flagship AI product, yet they’d rather not.

This has created a lot of friction, and I’m sure it’s not exclusive to L&D. What I found in the data is that teams will use their mandated company AI tools for very little, and instead, engage with external tools as they provide much better quality.

These external tools can be classed as “shadow stacks”, aka tools being used in secret to complete work.

Look, I’m not the police, so it’s not for me to tell you what to do.

It’s just fascinating that some people are willing to take risks with company data with these tools in the pursuit of doing stuff faster. So, if you’re doing this, it seems you’re not alone.

3/ Experience and hierarchy influence AI’s value

I cut the data to see how AI is used across different job levels, and the results revealed more than I bargained for.

Chief Learning Officers and Managers shared how they are utilising AI to accelerate administrative tasks so they can reinvest value into coaching teams and business partnering. No surprise that with the sheer madness of meetings at this level, AI meeting tools were frequently mentioned as a key enhancer too.

Plus, many CLOs praised the access to an “on-demand digital consultant” that enables them to compile lots of data in one place to support informed decisions, and acts as a thought partner on a variety of topics as another +1.

Now, Freelancers and Consultants realised some of the same benefits, yet are far more focused on using AI as a surrogate team to support in a variety of scenarios.

Some of these included:

• AI as a strategic partner to sense check ideas

• Utilise AI production tools to compete with larger agencies with high-end client production

• Support with client research and crafting client proposals

Essentially, covering several workplace roles like marketing, sales and more with AI.

Then we have individual contributors, who, for the most part, are deploying AI to overcome the “blank page paralysis” and do a lot of, if not all, the heavy lifting with course creation.

Responses in this category ranged from utilising LLMs for “initial training course outlines” to “scripts for voiceovers”. One of the more intriguing ones I came across here is where a respondent created a website using HTML even though they had no idea how to code. Another example of AI enabling humans to perform tasks outside their skill set without developing the skills.

Final thoughts

There you have it, friend.

Three more insights to give you a better view of how the industry is shaping its use of AI.

Find the original publication of the survey analysis here.

If you’re an L&D team that wants to learn more about this research and how you can leverage AI and tech intelligently in 2026, reach out to me. I’m in the midst of planning my H1 activities with a number of organisations on this topic.

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 VIDEO THOUGHTS 💾

Where I Still Get The Most Value From AI

One of the most common questions I hear from teams is: Where can AI actually help me at work?

The mistake most people make? Jumping to the tool first instead of defining the problem. AI can be a powerful productivity booster, but only if you know which tasks to apply it to.

In this video, I break down a simple decision-making framework to help you identify where AI can save time, improve quality, and let you focus on more meaningful work. Plus, I’ll share three real-world use cases from my own workflow, with specific tools you can try.

Enjoy 😊.

Till next time, you stay classy, learning friend!

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1. Ideas you’d like covered in future editions

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