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 TODAY’S THOUGHTS ☠️

Hey there 👋,

Throughout the Gen AI explosion, one question has always occupied my mind: Everyone's asking L&D to lead (or support) AI adoption, but who's equipping L&D?

And that’s the problem I work to solve for you and organisations.

I mean, you can't support your company's AI adoption if your own team is still trying to figure it out. Plus, the world is mashed up with so many terms that people interchange but probably shouldn’t, like:

• AI Adoption

• AI Transformation

• AI Fluency

• AI Literacy

I get the confusion.

Depending on where you work, the meaning of and use of these terms can be vastly different.

What truly matters is how you can contribute meaningfully to adoption, skill building and application of AI across your organisation. I’ve learned a lot about this these past 4 years.

So, today, I’m sharing a mini playbook on how L&D teams can shape the environment for meaningful use of AI at work.

Get your tea or beverage of choice ready 🍵.

We've got lots to discuss!

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 IN THIS DROP 📔

• Meaningfully contributing to AI adoption

• The essential 3 pillars where teams need the most support

• The AI adoption failures I wish I knew

TOGETHER WITH AI ENABLEMENT FOR L&D

Everyone's asking L&D to lead AI adoption — but who's supporting L&D?

I’ve spent years helping learning and development teams cut through the hype, build real confidence, and learn the practical AI skills you need to thrive in your role.

And the good news is…I’m scaling my services with AI enablement specifically for L&D teams.

That means I’ll be supporting you, the L&D practitioner, directly with my quarterly AI Native L&D Pro half day intensive to explore all parts of the AI-verse in L&D.

I’ve just released the registration for June’s workshop, but be quick, I only have 10 places.

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 THE BIG THOUGHT 👀

How L&D Can Support Meaningful AI Adoption

There’s a ton of talk about AI adoption.

It’s odd because the validation of “adoption” has many definitions, depending on the context and environment. The common pitfall is to measure adoption as ‘use of AI tools’ alone.

As we know, with previous technology, usage alone doesn’t mean meaningful adoption.

Setting what adoption looks like in your organisation is not a task for the L&D team.

We both know this is stupid, yet I see too many instances of companies trying to “train” their way into successful AI adoption.

Of course, L&D teams alone aren’t going to make any organisation achieve meaningful AI adoption (however you measure that).

Yet, we have an opportunity to contribute to long-term and meaningful adoption of AI across workforces as part of a wider collaboration in a community.

Let’s talk about that…

It takes more than access

Let’s go beyond the veil of BS we see online.

Access to an AI tool alone means nothing, and putting on one hour lunch and learns to “make people learn AI” is a comical up-skilling strategy.

If you’re a long-time reader, you’ve heard me become a broken record when I talk about what it takes to nurture meaningful and long-term change. We have much to consider with context, culture and constraints in every environment. No two workplaces are the same, that’s why the cookie-cutter “adoption frameworks” make me laugh.

They’re a good point of inspiration, but you shouldn’t follow them like a strict set of instructions.

Saying that, what is it we need to consider beyond tools?

Read on…

People, Systems and Tools

As you’ve probably guessed, launching new technology and tools alone rarely leads to meaningful adoption.

There’s a bigger ecosystem at play.

We have to consider:

1/ People

Where are people today, and how do we meet them?

Everyone will have a different understanding, maturity and receptiveness to something new and unknown. In AI’s case, we have a mix of emotions from “will this take my job” to “I want it to do all this stuff I hate doing”.

The most difficult part of a change process is people, because we’re all so unpredictable.

2/ Systems

Quite simply, how we work today.

What are the tried, tested and trusted conscious and unconscious systems we have in place? This covers both how we execute tasks and how we think about executing those tasks (deep, I know).

We each follow different types of systems in our day to day.

Understanding what these are and how AI will impact those is key to this change.

3/ Tools

The part you’re most likely more familiar with.

Here, we consider the tools in use today alongside new ones being deployed, and how to bridge the gap in both understanding and knowing when and where to use them.

Too many forget the ‘when and where’ part at their own peril.

Something I’ve noted more these past 6 months, is just how little teams know about leveraging the tools they already have. They run off to use the latest trendy tool without realising they have access to the same features already.

I’ve worked with companies who’ve had LLMs for a few years, and look shocked when I show them features with their tools they’ve had all this time but never used.

Where you can add value

For us to recognise where we can provide support and drive value, we must recognise what’s changing.

I think this framework from BCG can help recognise the moments where performance support is most needed with AI transformation.

They propose it for navigating AI transformation at scale.

Through an L&D lens, I see this as a conversation point of what to map against when focusing on how best to support workforces.

It’s built on two key dimensions:

1️⃣ AI Maturity

It progresses from tool-based adoption by individuals to workflow transformation, to full, agent-led orchestration. Most organisations, and even teams within them, operate across multiple stages at once, not in a linear path.

2️⃣ Workforce Impact

This spans how tasks are executed, to what skills are needed, to how teams are structured, to how organisational culture must evolve to support new ways of working.

While this covers the wider transformation AI brings across businesses, it acts as a roadmap for L&D.

A roadmap is often what we need because it’s not uncommon for senior leaders to treat “training” (as they call it) as a boomerang that’s thrown at will when they decide people need to know stuff.

The framework above provides a view of where the friction/pain points/ problems exist in the cycle of change. That’s where we should focus.

Map it out

I mentioned before not to blindly follow frameworks, and that advice is the same here.

This view from BCG is a useful foundation for each of us to think about “where can we add value”, but it will look different for each environment.

So, I’d recommend you map out what your organisational journey looks like today.

Explore the 3 pillars of tasks, talent and teams across your business and identify how/where AI is starting to and might impact these. It’s here that you will uncover the friction and pain points where we can be of most service.

Some of that will be through tooling, no doubt.

Yet, I feel pretty safe in saying you’ll be spending a good deal of your time navigating changes within people and systems.

Examples of AI adoption and transformation in action

Ok, I want to share some useful case studies and “what good could look like”, but that’s hard to quantify. Especially in L&D, where the measurement of ‘adoption’ is different to many.

The default I’m seeing, just like all other learning tech, is measured on - engagement.

But we both know that logging into a tool doesn’t = capable and driving value with those tools.

So, I’m going to share two examples with you that I find interesting.

I’m not saying they are the ‘way’. They’re good reads to see what large organisations are doing, learn a bit and decide if any of that is useful for you.

Notion’s AI Transformation Model

Why is a note taking and productivity app providing AI transformation services?

No idea.

Yet, I found their open-sourced AI transformation model released this month interesting. Not only for the fact that it looks remarkably similar to one I built for L&D over a year ago (are you watching me, Notion?), but it felt more like a common sense approach.

Notion’s model respects that an organisation will not move at the same pace together.

It’s impossible when you think about it.

Each department has different talents, goals and systems. The pace of innovation with AI means that the level you are today might not be the same level next week.

You’ll find everything about Notion’s transformation model in this guide.

It comes with:

• An example AI-native assessment chart for each department

• Notions customer adoption playbook

• AI maturity levelling framework

• Customer case studies (a bit vapid, tbh)

Looks like they’ll be releasing more resources soon.

I’ll update these to this guide when they drop.

Zapier’s AI Fluency Rubric

I’m gonna be honest…I have no idea WTF a “rubric” is.

Zapier started releasing this in 2025, and V2 just dropped. I didn’t much like the last edition. While this isn’t really an adoption model, it does help us understand how organisations are assessing employee’s AI skills and use.

As Zapier were the first to market publicly with this type of solution, naturally many areas of the market started to use it.

So while I’m not saying if it’s good or bad (that’s for you to decide), we can learn from it.

You can find the full package on Zapier’s website.

🥷 The AI for L&D Transformation Model: Aka, how to reshape what we do and become AI-native

What a plot twist!

If you read this far, you’re about to get the good stuff.

I’ve transformed my 2025 article on “How L&D can transform with AI” into a new playbook packed with additional commentary and new resources.

It gives you the 4 levels of transformation I’ve seen teams go through (and continue to) these past 4-ish years now. It’s also layered with where I think the industry “could go” based on current technology.

In the playbook I break down:

• The 4 levels of AI transformation in L&D

• What teams demonstrate at each level

• A curation of my best resources to improve your capability with AI

It’s available right now for premium subscribers in the Cult of Thoughts community.

Maybe the best shadow drop I’ve done in a while.

Note: This is a V1 launch. As AI moves so fast, I’ll be updating this every quarter.

Final thoughts

I’m going to leave it here for this week, folks.

There’s much to say, of course, but only so much attention span I can ask you to give.

In the meantime, join the Cult of Thoughts to access the AI for L&D transformation playbook. You’ll get access to 25 + playbooks, exclusive events and the private Whatsapp community too.

→ If you’ve found this helpful, please consider sharing it wherever you hang out online, tag me in and share your thoughts.

 👀 ICYMI (In case you missed it!)

1. Will Claude’s new design tool change the production world of L&D?. In this episode of “Before you AI”, I give you my first impressions of Claude’s new visual design platform and how I built a quite stunning landing page in under 30 minutes.
   

2. You need to get clear on what not to do with AI. In this time of 'AI can do everything', knowing what you should keep doing yourself is not only powerful...it's CRITICAL. Because skill erosion is a real problem.
   

3. A common sense approach to security with AI agents on your devices. Here's a quick guide for my non-techy L&D folks who want to play safely with the latest tech.
   

4. The biggest project I’ve ever done just dropped. I've written an 8 part playbook on "How to launch a successful AI-powered L&D product in your organisation" exclusively for my Cult of Thoughts community. I’m releasing each part over the next 6 months. If you’re a paid member of the newsletter, you get access to the private Whatsapp group, monthly office hours and guest conversations.

 VIDEO THOUGHTS 💾

The AI Adoption Failures I Wish I Knew

Most organisations are racing to plug generative AI into learning & development, but 4 out of 5 projects still crash and burn.

In this video, I dissect research on The Root Causes of Failure for Artificial Intelligence Projects (RAND Corporation) and map the findings to everyday L&D realities.

You’ll walk away knowing exactly where teams go wrong, how to avoid “shiny-tool syndrome,” and what it really takes to turn AI into measurable impact.

Enjoy 😊.

Till next time, you stay classy, learning friend!

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