Category: Artificial intelligence

I’m fortunate that a lot of organisations reach out to me to support on their learning technology efforts.

As I’ve been exploring the AI-verse for the past 4 years and sharing what I know in public, the types of requests I now get are very different.

95% are all about AI enablement.

I feel like I’m in a Pokémon style evolution moment. I think it’s already happened as I now spend my days, outside of writing my newsletter, with organisations focusing on crafting AI enablement strategies for L&D teams to deploy.

This role didn’t exist 2 – 3 years ago and I think it’s one that’s going to gather pace as a key part of the modern workforce over the next 5 years.

They say (whoever they are) that life is about moving forward, learning and adapting to the times.

It’s hard to disagree with that.

We see it with bands, we see it in sports and we see a lot in TV and film as well. As humans we experience change every day, sometimes several times a day. It’s natural but it’s also something that a great deal of us struggle with.

That includes me.

You already know how crazy the past decade has been. We started it with a pandemic of crazy magnitude, which we haven’t seen in the last 100 years, and then we swooped right into the huge AI revolution.

So, it’s safe to say in 2026 that nothing is really the same but everything is the same too.

How I fell into this twilight zone of AI enablement

(Your age will dictate if you understand my little easter egg in the title here. Yes, I’m getting old.)

Of course I’ve experienced this in my own little slice of the world.

I’ve been in L&D for over a decade now. 

I’ve been working with different types of technology in tech/HR/L&D focused roles for about 17 years so I’m used to change.

That’s the reason why my Steal These Thoughts newsletter exists.

I want to help L&D and HR professionals, or people professionals as they’re called nowadays, to truly understand and leverage modern technology in their work. The whole mission of that is to simplify tech as much as possible. 

Of course I’m still doing all of that but it feels like there’s been an evolutionary jump in the last few years. 

You see, there was a time when all people used to ask me about was LXPs and LMS’s. 

• How do we build them? 
• How do we deploy them? 
• How do we make them successful in organisations? 

That’s what I focused on from a technology perspective in our industry.

Now in 2022 when we started to see the emergence of generative AI and then quite quickly moving into large language models, a lot of that conversation completely changed. 

No one asks me about LXPs or the LMS anymore.

This also changed a lot of my own journey because I became very curious, as tech nerds do, about generative AI and specifically looking at large language models and the potential applications of them in the L&D space.

I am continually high on what I call “The enormity of the possible”, and Gen AI has been my drug of choice to explore this.

So, I did what any good tech nerd does: I jumped in, I experimented, and I shared all of that stuff with the world.

That’s where I’ve been focusing my last few years. 

This quickly shifted to how I can help L&D and people teams understand this technology, leverage it wisely, and then enable all of the teams in their organisation too. Of course if you read any of my work, I’m really trying to do that in a more human-focused way as possible as opposed to the AI bros that I like to poke fun at. All they seem to want to do is automate their life away with AI and sit on a beach somewhere and sip a pina colada. 

Now with the huge economic and societal change that generative AI has brought, there’s been a shift, I suppose we could call it, in my own career identity. 

I tell people I’m a learning technology strategist, but what I’ve come to find in the last few years, in particular, is that external people identify me as an AI influencer or AI expert. None of these things are actually true. Not in my mind, anyway. 

I use AI every day. Maybe I know a little bit more than the average individual because I’m an early adopter, but I by no means know anything about how to create a LLM or do any of that stuff. 

But what I do know is how to simplify technology for the everyday human and to help them leverage that. 

I think that has always been a big part of what we do in L&D anyway. 

Now add on top of that helping people, in general, to think through their skills, to think through the tasks they do, and to help them think about the workflows that they either partake in or they build themselves at an individual, team, and organisational level.

That’s where I do a lot of my best work.

What I’ve learned now is that has evolved into this kind of new role of what I call an AI Enablement Architect. 

AI Enablement, not Adoption

About a year ago, I wrote an article where I basically went into Nostradamus mode and prophesied that AI architects, both from a technical perspective and a non-technical perspective, would be the next big role in the future of work. 

It came about because, at that time, I’d been asked in a particular month so many times across LinkedIn direct messages, my emails, or through my newsletter, “Are you an AI architect?” I always said “no” because I don’t look at myself as a technical individual.

I am very much non-technical when it comes to the developer side of AI technologies but it kept coming up. 

What this data showed me was that this was an emerging need which could become a role. You can read that article to get my view on non-technical and technical AI architects at that point.

Today though, where I find myself in this space is specifically working with L&D and people teams, and even teams outside of that from time to time, to specifically look at AI enablement.

Now, for me there’s a big difference between AI enablement and AI adoption. 

AI adoption is about making people use tools.

It tracks vanity metrics like how many licenses are distributed across an org, number of logins and how many cat pictures Eric generated this month (damn it, Eric – think of the tokens).

AI Enablement is a different game.

It’s about equipping humans with knowledge, skills, and frameworks to use AI effectively in their work.

My fellow L&D teams can help with the latter in soooo many ways, but they’re being held prisoner by their organisation to the former.

Having access is not the same as delivering value with it, imo.

What does an AI Enablement Architect do?

The obvious question, of course.

What sorcery am I up to?

So while the main way I contribute to the world and the L&D industry is through researching, analysing, and bringing new ideas through my newsletter, I’m also being asked by a huge amount of organisations to support them with AI enablement. 

When we say those words, it can seem a bit confusing, so let me break down what I actually do with organisations, in case you are someone that might want to work with me on this, but also if you’re an individual who is curious about what someone does who works in AI enablement.

Predominantly, my role is to work alongside teams, so I look at myself as an AI enablement architect that helps them with:

1/ Identifying where they are today

What I mean by that is, from an AI maturity perspective, before you can do anything with organisations, you need to uncover exactly where their teams are: what do they know, where are they struggling, and what are those pain points?

Some of the ways I do that is by generally doing a diagnostic assessment across the teams, departments, and even the organisation to get an understanding of their current skills with AI and their level of knowledge in their own human skills as well.

Usually, what comes out of that is that we’ll get all of this data, and I’ll spend some time with the organization to understand the results.

We’ll put together an action plan around that so they know where to focus their time and money to get the best ROI.

2/ Building the enablement strategy

Once we have the data that allows us to map the organisation’s current AI maturity, what I’m able to do is build a practical, step-by-step plan for how a company will roll out not just the AI tools themselves (that’s not really what I’m doing), but actually how we enable people to use those AI tools in their everyday work.

This comes in a variety of different approaches.

Mostly, that’s looking at the data that we’ve already got about AI maturity, but it’s also looking at workflow opportunities across the organisation.

What I mean by that is what are the workflows that people are using today in their physical space, and then look at them to understand how we could reshape them with AI, or how could we support them with AI.

What I will then do is look at what’s the best way to actually enable people in the organisation:

1. Is it up-skilling internal L&D teams to do that? 
2. Is it bringing in outside help? 
3. Is it a mix of both, or is it building an entirely new solution? 

The output of that is that I’ll build a clear strategy that maps out the entire enablement journey for whatever population of the organisation needs it.

That is bespoke on a local level in teams, departments, and organisations.

This is incredibly helpful because what it’s allowing you to do is get clear ROI on the efforts that you’re doing from an enablement perspective on the technology that the organisation has access to.

3/ The on-demand AI enablement Architect

What’s different here is that instead of running like a one-off workshop or a couple of workshops in an organisation, I’ll stay with them as kind of this embedded consultant while that full rollout happens that we’ve built from an enablement perspective.

I might have monthly or weekly check-ins. We might just do sound boarding calls every quarter. There’s also on-demand support as things shift as they always do.

What that gives organisations is access to up to date and relevant information, and my ability to provide direction, because I’m working with so many different organisations on enablement strategies.

I can give them the experience they may not have in-house right now to accelerate what they want to do and help them when the plan does change, because it will change with AI.

Ultimately, it helps them ensure that all of the new habits that we’ve done actually stick.

In not so much of a nutshell, that is what I do in this AI enablement space right now.

Like I say, I didn’t plan on doing this. It’s been completely accidental.

It’s been purely driven through requests from probably people like you, who might be reading these words right now or even listening to them.

It’s interesting to see how things are shifting. The reason why I share this is because I am really clear that in order for L&D to be successful, I believe that we need to move from production to enablement.

I think for L&D’s relevance and for its credibility, it needs to stamp itself in the organisation as the AI enablement function.

We’ve seen a lot of this already, and I’ve written a lot about this, with a lot of companies merging their people teams and parts of their product and technology teams to make overall AI enablement departments.

This is kind of like I say, a bit of an evolution in terms of the work that I’m doing. If it sounds interesting to you and if you’re looking for someone to help you with AI enablement in your company, obviously, reach out to me.

And I hope this has been helpful in terms of just giving you a view into the shifting roles and tasks that are out there now. There’s been so much hype about the AI jobpocalypse and all of this stuff going on, but actually what destroys stuff also can create stuff too.

I think this is one of the roles that is quickly emerging, and we’ll see departments created around these roles too.

The skills, mindset and behaviours most useful for this role

As L&D pros, I know this will be the question everyone asks.

Here’s how I see it right now.

• Digital intelligence. Specifically around generative AI technologies. A no brainer, of course. You don’t need to be an AI engineer, but you do need to understand fundamentally how the tech works so you can map that to your enablement guidance. It also goes without saying, you need to be well versed in the current AI landscape.
• Storytelling. I’m going to cheat here because I’m using this phrase as a catch-all for being able to market your ideas and capabilities, sell your proposed solutions and position stakeholders.
• Performance consulting. Yes, you still need this because performance is life. Without a clear “why are we doing this?”, expect everything else to fail.
• Workflow Design. This is where a lot of the gold is and is currently being underserved. Knowing how to help teams deconstruct the things they do on repeat to build a workflow and then analyse that to see if it can be redefined with AI at the core or enabled by it with automation instead.
• AI Fluency. A buzzword? Maybe…but, I think it’s one that has good meaning behind it once you get unstuck from the marketing wrapper. It’s not about using tools, but about how you choose to use tools and their outputs in an intellectual, ethical and common sense capacity.
• Human Instincts. I covered this in quite a bit of detail recently. I look at this as an advantage in all your work.

Of course, there’s more and the usual underpinning of human skills that you need just to navigate life in general.

Final Thoughts

My current thinking is this can and will be seen as a stand alone role, but it can expand to include AI enablement partners that form the human side of the team with the architect.

Of course, all enablement architects today will already be using agents within their team setup (as do I).

That’s it for now, folks. I hope it’s helpful, and I’ll talk to you in the next one.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

I’ve noticed an interesting shift between casual users of AI, aka those who mostly use chat, and power users of AI, who are using it for completing tasks and light-mid automation.

And that is the use of context and skills.

When I say skills, I don’t mean the “skills” we cover in L&D but teaching skills to AI. It’s becoming an emerging requirement of meaningful use of AI. Without understanding how to feed AI your context and deconstruct skills that AI can use, you’ll be stuck firmly in the ‘average’ user level.

I don’t believe that’s good for you, your skills or your career.

If we want L&D to be taken seriously as “The AI enablement” function in the workplace, you need to add this to your talent stack.

Things just keep changing so fast, right?

We’ve come to expect that with AI but the impact it continues to have on how we work and complete tasks is so disruptive, even I find myself questioning my workflows more than a few times a week.

A few weeks ago, I sat down to record a walkthrough on creating skills using the skill editor in Claude.

All I could keep thinking about was “never did I imagine I’d be sitting here explaining how to deconstruct a skill to artificial intelligence”, only a decade ago most tech I was using could only handle static automations.

Before we go on, let me explain what “skills” are in the context of AI, specifically LLMs.

AI wants to learn all the skills

As AI becomes more ingrained into life and work, it’s only natural that it’s evolved to learn how to mimic our workflows to complete tasks.

That’s basically what skills are.

A file with a set of instructions, context, and guidelines that enables LLMs to extend their capabilities beyond the usual chat, that most of the population utilise daily. These are written and exported in a format called Markdown.

Don’t worry, it’s nothing complicated.

A Markdown file is a plain text document that uses a lightweight markup language called Markdown to add formatting. These files typically have the extension .md. Basically, they’re a package that LLMs can read much better than typical formats like docx and PDF.

You don’t need special software to create a .md file.

You can compile all of the contents AI needs to know in a standard word doc or Google doc. All you need to do is save/export as a .md file. Easy, I promise.

The content inside this Markdown file for an AI skill usually includes:

• What the task is
• How the task should be done (aka your workflow)
• What “good” and “bad” outputs look like
• Examples for the AI to imitate
• Things the AI should avoid
• Reference materials the AI can pull from

For Claude users, it will use that skill without being prompted to.

It knows to do this by reading the tasks you ask it to do and mapping that against available skills in your library. Pretty smart, right?

As a skill file is ultimately just a text file written in markdown. Any LLM that accepts file uploads or pasted text can use it as context. But, it won’t automatically activate and map it like Claude does.

Right now, most people are not really leveraging AI’s full capabilities

If you take a snapshot of how L&D pros are using AI right now, the vast majority are typing prompts into ChatGPT, Claude or your poison of choice, and praying that something good comes out.

That’s where everyone starts, but I feel like too many are stuck here.

Knowing how to craft clear and reusable skills that AI can utilise is going to separate you from the majority of people around you. There’s also an added unforeseen benefit here, and that’s forcing you to explain and think about how you complete tasks today, and what that workflow looks like end to end.

It might not have been something you’ve consciously focused on as you just do the task.

So, AI forcing you to do that feels like a good thing for your metacognitive capabilities. Plus, you have to continue to maintain that skill within AI’s system. It’s not a one and done event.

These and MCP’s (Model Context Protocols), that we covered in a previous conversation, are the combo that will enable you to do more with AI than just “chat”.

The TL;DR: Invest time in understanding how to craft skills for AI + provide context. You’ll build more leverage and efficiency with AI this way.

How to build your first skill.md file

To start, identify a workflow that you do on repeat.

It can be anything, I’ve shared one of mine in the next section if you want to check that out to get your own creative juices flowing.

Now follow the steps below:

How I use skill.md files in Claude to support my YouTube production

You may know I have a YouTube channel.

A lot of people do. I, however, know nothing about how to be successful on YouTube. I like making videos but building the packaging (thumbnails, titles) and distributing them (timing) is not my strong point.

This is where I ask AI to help me.

I write scripts, shoot and edit the video, and then I have a set of Claude skills (some might call these agents, but meh!) that produce video titles, write SEO/AEO friendly descriptions and advise on thumbnail selection. I built these skills through the best practices of YouTube strategists I’ve met and countless articles, and podcasts.

These enable me to extend my capabilities and Claude’s for something with meaningful benefit for my business.

You could do the exact same thing in your L&D work.

The beauty of skill files being built in a .md format is that you can take them anywhere because every LLM reads .md files. This is another good reason why it helps to build skill files. You can package them and take them with you to the next tool you use.

You don’t have to be held prisoner to any LLM if you build a library of skill based .md (markdown files).

A little experiment for your week

Don’t overthink it.

Pick one task you do every week that you can deconstruct into a workflow to use in your skill.md file.

Then:

• Open a blank doc.
• Follow the instructions in the “how-to” section above or watch my video, also shared above, to build your file.
• Save it as a .md file.
• Drop it into Claude (or whichever LLM you use)

Well done, you created your first skill.md file for AI.

Right now this works best with Claude (yes, I’m Claude-pilled), but you can upload markdown files to any LLM. I’d add them to your project folders, memory or custom instructions.

Final Thoughts

So, there we have it, folks. 

Your first entry, should we say, into the world of building skills for AI. This is by no means comprehensive, and if it is something that you want to get deeper into, let me know by hitting the reply button. 

I’m pretty big on this being a key skill for this year and probably 2027, but with all of these advancements, who knows what’s going to happen?

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

Much of the world’s AI conversations focus on tools.

• Who has the best one?
• Which model is better than which?
• How fast can it do ‘x’?

While AI bros care about this, the general population (mostly) doesn’t. We just want to know – what’s useful, how can I use it and what results can I expect. I know it doesn’t sound like much to ask for, but it’s damn hard to get a straight answer on it.

We’ve talked a lot about those topics already.

As we have with preparing yourself for adoption with the right mindset, behaviours and even unbundling yourself from existing (or now historic) frameworks.

Despite all this coverage, we’ve not touched upon one area that I feel is incredibly important – how will you actually work, live and progress in this AI era?

I assume this doesn’t come up in many conversations.

However, if you really want to get serious about “What does the future of work look like?”, you have to be clear on how you will use, co-exist and stand out in a career marketplace disrupted by AI.

The Career Advantage for 2026

I’m going to frame this part as ‘building your advantage in the AI era’.

We’ve talked so much about AI fluency in L&D, and the importance of human skills alongside it but there’s a 3rd piece missing from our stack (if i can call it that), and that’s instincts.

I’m not sure “instincts” is quite the right framing for this, but it’s the best I can think of as I type these words (got a better one? Tell me). It’s a collection of stuff that doesn’t fall into the common categories of skills, capabilities and behaviours that we often use.

These are the things that I feel have increased in value in the AI era.

I mean they were valued before, but they’ve become like a stock that’s tripled overnight.

I believe that having AI Fluency + Human skills + Instincts is your winning combo as we navigate this Godzilla sized tsunami of uncertainty and disruption caused by AI.

What has value in the AI age?

Big question and no one simple answer.

With ‘AI bros’ and false prophets everywhere, we need to have smart instincts.

Here’s my two cents: Taste, context, curation, expertise and a unique point of view are what’s most valuable in a sea of AI-slop.

I’ve bundled these under the label “Instincts”.

I think these, along with strong human skills and understanding how to wield AI meaningfully, is what will set you apart in the years to come.

So, my “simple answer” is to cultivate these alongside your use of AI.

Let’s unpack those instincts in a bit more detail:

• Taste: Knowing what is “good,” not just what is “trendy”.
• Context: Knowing why this solution works for this specific situation.
• Curation: The ability to sift through the AI-generated noise and pick the “signals” that matter.
• Expertise: Having the prior real-life experience that backs up the credibility of the above, because if you haven’t actually done ‘the thing’, why would people listen to you?
• Unique Point of View: Some would call this “your voice”, I, on the other hand, just call it “something worth saying”. Anyone can repost an idea, but only the critical thinkers find a new way of looking at them. A UPV only comes from people with experience.

While these instincts complete the magical 3 part combo to stay sharp in the AI era, we need to cover how to keep yourself accountable so you’re making the right decisions.

That leads me to our next point…

Craft a philosophy + guiding principles with AI

If you stand for nothing, you’ll fall for anything.

Not sure who originally said that but it feels right for this next bit. Over the past few weeks I’ve emphasised that the conversation is no longer about “who is using AI” because that’s over.

Everyone is using AI.

The question now becomes “How are you choosing to use AI?”

Not using AI is stupid. Don’t fall into that trap because it is a fundamental element of how we now live and work. You know I’m not the type that endorses the blind use of AI, which is why you need a philosophy + guiding principles to work well with it.

If our combo of AI Fluency + Human Skills + Instincts = keeping your mind sharp, your skills at the cutting edge and standing out in a workplace that AI is quickly eating up, then our personal philosophy and principles are what help us make the right decisions with those capabilities.

Oh, and don’t leave this to be imposed on you by the ‘tech/AI bros’.

WTF is a philosophy + principles

Let’s keep this simple

• Philosophy = how you work with AI
• Principles = how you make decisions with AI against your philosophy.

Defining these makes sure you make the best choices in how you work with AI.

The kind of choices that keep your skills sharp, your mind firing on all cylinders and a career you have control over.

Here’s my philosophy + principles for working with AI

Every L&D team I’ve been a part of had a clear philosophy.

It’s what we stood for, believed in and how we did business with one another and our customers.

For it to work, everyone needed to buy into it, and to keep it healthy. Each member needed to use a set of principles built from it so we could apply it daily.

While the word ‘philosophy’ can sound fancy, academic and a tad mysterious, it doesn’t have to be.

A philosophy is setting out how you do ‘x’.

In the case of my L&D teams, it was “this is how we work and why we do it”. The same goes for working with AI. We need to define our philosophy of “This is how I work with AI and why I do it this way”.

For the L&D teams, our philosophy was simple but not always easy to constantly maintain in a corporate world. Yet, being hard doesn’t mean it wasn’t worthwhile or the right thing to do.

Here’s an example of one:

“We believe in crafting experiences and solutions that positively impact performance by asking the hard questions to uncover the real problems our organisation faces. We do this through building credible, consultative and helpful partnerships to equip our people with the right skills to navigate the modern workplace.”

I get it might sound a bit corporate, but it’s a simple light to keep coming back to.

This is what we do, how we do it and why we do it. And…if we’re not doing that, then we’ve F^$ked up.

We want to do the same thing for working and living with AI in all its forms. You only get the outcomes you want if you know the what, why and how.

Here’s my ever-evolving philosophy for working with AI:

“I work with AI as a collaborative partner to analyse, identify and improve upon ideas by selecting the best tasks to augment and not outsource my human thinking and creativity. My use of AI is to always amplify human skills and not automate them, which I do by staying adaptable with learning and refining what I know with the latest innovations.”

Moi

It’s not perfect, but it’s a start.

My main goal is to make smart use of AI to remain relevant in my career, yet keep my human skills sharp so I have a USP which (as of now) AI technologies will find hard to replicate or replace fully. That’s my play, anyway.

By having this written down and top of mind, I keep myself on a path which I’m confident will keep me employable, happy and hopefully, a good human being.

To make sure I keep on that path, I need principles that help me apply and reinforce this approach daily.

They’re actually pretty simple:

• Don’t outsource my thinking, go to the brain first (the original OS)
• Do outsource tasks that don’t require my unique human capabilities and will give me more time to use those capabilities
• Keep doing the tasks that give me joy and exercise the skills that give me an advantage over the market. An example of this is writing.

There’s more, but you get the picture.

Final Thoughts

Ok, friend.

Shaping the future of work means being ready to meet it, and you can only do that if you do what we in our industry do best.

Prepare yourself to survive and thrive in it by bringing a great combo of skills, wisdom and instincts.

That’s how you invest in your career and support your business/teams.

We might not be able to plan for the next 5 years but we can certainly tackle the next 6 – 12 months (unless the robots come, then we’re all f**ked).

TL;DR

• Build a combo of AI fluency, Human skills + Instincts to thrive over the next 2-3 years
• Have a clear philosophy and set of principles in place to work with AI that will keep you smart, human powered and employable.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

After reading an impactful newsletter by Greg Isenberg this week.

I’m even more convinced about the power of knowing what not to do with AI.

You see, 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.

And that’s not AI’s fault, it’s ours. We’re naturally becoming lazy as we indulge in the path of least resistance and instant gratification.

The urge to explore this dawned on me several times last week across my work with clients. I get paid to help companies with AI enablement. Part of that involves identifying best fit tools and how to leverage them. At least, that’s what I’m most tasked with.

No one asks me about keeping the human, and could we say humanness, at the core of it all.

But I bring it to the table, anyway.

So, today, we’re exploring what not to do with AI, and why your future self will thank you for it.

The question no one is asking about AI in 2026

I was sitting in my overpriced home office chair talking to 100 people on a call about how to leverage AI as a strategic thought partner, and not just an automated engine that runs like a conveyor belt for company requests.

The usual questions came down the channel.

• “Can this create a slide deck for me?”
• “How do I get it to write this report?”
• “Can it create these images?”

All basic, always the same and at this point in my time in all this, 100% predictable.

I’m not saying this is a bad thing. After all, the workplace has conditioned us to execute, execute and execute (tasks btw, not people). We want to know what end product we will get, and if it’s worth the time to learn how to use another AI tool.

My answer to all those questions is very different in 2026.

“Yes, it can do all of that. To varying degrees of quality depending on a few factors. Yet, the more important question is: do you want it to do them?”

I’ve heard more noise at a morgue than the wall of deathly silence that greeted me.

I get it. They thought they were here to learn how ‘x’ tools can make them do ‘x’ things faster and to the same level that they would do. Not a philosophical analysis on working with AI. 

Despite that, I chucked the question into the void.

There were a few murmurs but I wasn’t expecting any answers from such a large group. No one wants to be the person that says “AI can do my spreadsheets for me, but I love Excel!”, I feel you, human.

My point is the choices we make have consequences.

I’ve spoken about these in this very newsletter for years now.

From strategies to stop AI hijacking your mind to the hidden impact of AI on your skills to the growing skill erosion problem in society.

I even gave a talk last year at DataCamp about “Does AI help or harm skill building?”

The choice you make with AI every day

I’m kinda obsessed with the psychology of all this.

That’s also the reason why I keep sharing this Jurassic Park quote every week: “Your scientists were so preoccupied with whether they could do it, they didn’t stop to think if they should.”

The follow-up to that very quote from Dr Ian Malcolm talks about the aftermath of such choices: “‘Ooh, ah,’ that’s how it always starts. But then later there’s running and screaming.”

Hopefully we have no running and screaming. Yet I’ve made my point.

↳ You have a choice.

Yes, you can create a presentation with x app.

But what do you lose?

• The blank page thinking of “how do I start”
• The chain reaction when one out of the blue idea leads to another
• The ‘aha’ moment when your brain spots a beautiful visual idea
• That killer story arc you spotted as you structure your thoughts, notes and slides

Maybe it’s all of this, maybe it’s some and maybe it’s none.

Don’t get it twisted, AI is amazing.

I use it every day. I would be stupid not to. It’s no longer an advantage to use AI because everyone does. Spitting feathers on social media on who used AI to do what is pointless.

The real power is in how we choose to use AI.

This is where you have all the power, but my god, do you need to have the restraint of a monk to not follow the herd down the same path.

A common sense framework for a senseless time

I get everything I’m telling you is counterintuitive to what all the “AI bros and thought leaders” say on every social platform.

Instead, I’m looking at the long game. I’m asking what happens in 5 years from now if I stop doing ‘x task’ entirely.

I can’t tell you what will happen.

No one can. 

You do have a choice on how you shape that future, and ultimately, what you would like it to look like. That’s why I loved the suggestion that finished the newsletter I was reading by Greg.

Greg shared an exercise we’ll call the “Non-negotiable.

The funny thing is I’ve been doing this natively so I kinda feel ’seen’ in the fact that I’m actively finding ways to not use AI for every task. 

That’s not because I don’t believe AI can do it, because it can.

It’s mostly because I enjoy doing so many of the tasks I get to do, and I don’t want AI to take my joy from that. Plus, I’m a deep thinking kind of guy (not that you would guess). I’m actually convinced my therapist gets more from me in our sessions than I get from them with my deep introspection, analogies and thought patterns.

Skills, joy and your expertise: You don’t need to trade these for AI

I’m fully on board to keep doing the things that keep the most powerful operating system (your brain, fyi) we have sharp alongside AI. 

I want both my brain and AI to be at their best. Not me being reliant on only an external source. I’d add another component to Greg’s output here of sharpening your thinking, and that’s joy.

Do more of the things that you enjoy.

I know there’s probably some secret Excel lovers reading this, and you know what? If you love dropping data in those cells and pivoting the sh*t outta that table, then you go do that, friend.

Don’t hand that over to AI.

I take this same approach to how I work.

There’s a bunch of stuff I don’t wanna do or is out of my expertise, and AI really helps me here. So I dive in to collaborate with my digital bud.

Then I have my “non-negotiables.” 

For the most part, AI could do more of my tasks but I’m choosing not to let it. That’s not to say I don’t use AI to help me in the process of these tasks, I just don’t automate or delegate to it.

I find too often that we default to ‘automate’ rather than ‘how can we work together’.

One example of what I’m not gonna give AI, is this newsletter you’re reading right now. I love writing this thing. I mean, not all the time, especially when I’m up against the inescapable force of depleting time and I gotta get 1500 meaningful words out the door.

But I wouldn’t change it for the world.

To write is to think and to think is to write.

That’s how I stay sharp. Every week I open a blank doc and drop my unfiltered thoughts in. I leave, come back and drop some more. 

That’s how it starts. 

I’ll then continue to ponder and procrastinate on my words and the message I want to share with you across the next 5 days. I’ve been writing online in some form for 15 years, so I’ve developed human based systems from great writers and thinkers that help me shape my thoughts.

It’s an art and science.

AI only sees my writing when I think it’s 70% there.

Mostly to make sure my spelling, grammar and structure are on point. But also to give me a counter view I might not have seen or unearth an angle I might have missed. 

Sometimes it does this well, often it does not. We spar and disagree about my overuse of sarcasm and analogies, yet we always get to a product I’m happy to put my name on (for the most part, there have been clangers over the years).

So while I don’t start nor end with AI on this task, it helps me sharpen my thinking in the ways I don’t use it.

Specifically, by doing all that pondering and procrastinating that’s led us to this sentence right now.

That is the beauty, joy and challenge you don’t want to lose.

Final Thoughts

This feels like a good place for us to end today.

TL;DR (too long; didn’t read):

• It’s very important to know what not to do with AI
• Use common sense to keep your mind sharp
• Don’t ditch the tasks that give you joy (I got your back Excel lovers!)
• AI is not a god. Disagree with it, let it help you but at the end of the day. Do what’s right for you.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

It’s crazy out there right now folks.

The LMS is dying (apparently), Roger, the latest automated AI workflow, is going to replace my content engine and make me trillions of dollars while I sleep (allegedly), and Ninja claw, Tigerclaw or Dragon claw will do life for me so I can just wander around asking “why am I here?” (how fabulous).

Jokes aside (I do love them, though), I’ve never seen such a typhoon of pressure on the everyday human.

So, I think we all need to…BREATHE.

Better? Great.

What we can focus on is: What actually matters to us? 

I believe that part of the answer to that is our skills. I get it’s hard to build the “right” skills to work with AI. That’s why I focus on skill sprints of 3-6 months these days because so much is expiring, evolving and emerging all at once.

In this one, we’re exploring 3 high-value AI skills you need in 2026.

What if your AI skills from last year are already outdated?

I know that’s a scary question, but damn does it move fast.

One week I’m happy making my AI assistants smarter with internal knowledge files and now I’m running around extending their capabilities with MCP’s, plugins and all sorts of sorcery.

My point being, sitting still for too long on the technical side of AI tools is becoming somewhat dangerous. I know too many people chasing after tools, but I’m more interested in cross-platform skills that enable high value from the majority of tools.

I have 3 today which I believe will serve you well across the never-ending spectrum of AI applications.

What are AI skills?

An engineer will have a different view on this vs an L&D pro.

I break AI skills into two components:

1. One component is “How do I use ‘x’ tool and it’s features”
2. The second is “How do I work with AI tools to get the best outcome?”, which would involve a host of metacognitive skills and behaviours.

AI ‘fluency’ and ‘literacy’ are better ways for us to frame “AI skills”

These frameworks acknowledge that using a generative AI tool, most popular being an LLM, is about more than the technical skill of using the tool alone. Personally, I like the term ‘AI fluency’ because the technology moves so fast. Which means both the tech skills and behaviours you need to work with it are forever updating.

For the purpose of today’s conversation, we’ll focus specifically on the technical skills side of this because I have written a lot lately on the behavioural side.

Know what’s expiring, evolving and emerging (The 3 E’s)

I do a quarterly skills review.

It used to be every half year, but AI changed that plan.

Your skills are an ever-flowing organism (best analogy I could think of in this moment). That means they never stand still, and without attention, they’ll degrade.

If your goal is to build a talent stack (a combo of your skills, behaviours and attitudes) that’ll keep you employed for the long term, you’d be wise to perform regular maintenance on yours too.

I use a simple framework I picked up from a Gartner report over a decade ago.

All you do is look at your current skills and ask:

1. What skills are expiring and no longer serve me and/or the world today?
2. What skills do I need to evolve to meet the demands of today?
3. What are the emerging skills I can get ahead of?

While I say simple, that doesn’t always equal easy.

You could even spin this as a bit of a life analogy too. We all need to know what to leave behind, what to double down on and what to keep a lookout for.

I’ve tagged each of the 3 AI skills I recommend you invest in with one of these categories to give you a sense of where it’s in your current skill cycle.

3 high-value AI skills you need in 2026

1️⃣ Prompting [Evolving]

No, it’s not dead, but it has evolved.

AI skills have such a short shelf life, and prompting was hailed to be the most important skill of the century. While I didn’t agree then (or now), it is the main way we interact with LLMs.

At its core, prompting is just inputting a query.

A lot of frameworks from 2023-2025 are no longer needed because models have become much more capable with memory and custom instructions. Prompting is also weird because no one template works and two people with the exact same prompt can get widely different outputs.

You still need to prompt, just less skilfully than before because of our next two skills.

Bonus: How To Learn The Meta-Skill Of AI Prompting

2️⃣ Context Layering & Context Engineering [Evolving/Emerging]

These sound the same but I’m framing them in two ways.

One for builders/engineers and the other for non-technical users.

For builders, context engineering is the science of high-signal curation. They’re not stuffing AI with every possible file. The goal is to build dynamic systems that “just-in-time” retrieve the exact tool, specific knowledge chunk, or agent-to-agent communication needed for the next step.

This is not too dissimilar from what end users like me, and you can now do.

As we start using more AI agents, knowing how to provide the right source, in the right format at the right time is critical. We already see these opportunities with memory, custom instructions, connecting our favourite apps and uploading multiple file sources into our chats.

Context engineering for the everyday human is about building the infrastructure around the AI, so it has everything it needs to make better decisions.

An art and science in itself.

We could say that prompting + context layering + skills/instructions = the 3 layers of a strong AI response.

Fyi, I have a guide and a video series on context layering/engineering coming in a few weeks.

3️⃣ Creating Skills for AI [emerging]

The best example of this is Claude Code and Cowork.

You can create a skills package which teaches the LLM how to perform a task. It’s a combination of context, instructions and guidelines that can repeatedly do the task with little oversight.

To make the best use of this, you’ll need to get comfortable with creating Markdown files. In these, you’ll unpack not only how to complete a task but why, and all the micro elements that make up the big choices. Then maintaining that skill just like you do with your own.

We’ll see this capability with more LLMs across this year. The great thing is you can take your .md files to any tool, so maybe we’ll all be building .md files now.

The real skill here is knowing how to break down a process for an LLM to understand with a combination of instructions, examples and evaluations.

Final thoughts

Ok, folks.

That’s my thinking out loud for the day.

Obvs, AI moves so fast, these are relevant skills right now, but maybe not forever.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.