L&D teams trying to and/or being responsible for total company AI adoption is a fools errand.
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 do have a part to play, and recognising where we can best support is critical.
So, lets explore 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?
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 at 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 should 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 deploy them.
Too many forget the ‘when and where’ part at their own peril.
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, and 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 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: How to reshape what we do and become AI-native
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 now, folks.
There’s much to say, of course, but only so much attention span I can ask you to give.
I’m thinking of expanding some of this thought into a long-form video. If that sounds like something you’d like to see, let me know.
In the meantime, some additional resources to explore on this include:
- Build your AI confidence with this strategy
- The ultimate guide to using (or avoiding) AI
- How to meaningfully drive AI adoption
Before you go… 👋
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