Category: Artificial intelligence

I know a lot of you reading this aren’t all sitting in huge corporations with millions of dollars at your disposal to take advantage of AI in both your learning design and local team level workflows.

The good news is you don’t need all that cash.

Not unless you’re a token hungry demon that uses AI for everything, but that’s a different problem to solve.

So I want to show you how to meaningfully leverage AI as a small L&D team or even a small business for less than $50 a month.

Okay, I’m going to break down six use cases of AI that you can apply across your small team or maybe a small business. I’m not going to recommend loads of tools because I really don’t think you need loads of tools.

The tools I’m specifically going to focus on are:

• NotebookLM
• Claude
• Sana Agents
• ElevenLabs

I will label each of the use cases with my recommended tools, but don’t take this as you can’t do these things without them.

1/ Your Digital Brain

Tools: NotebookLM, Claude

This is probably my favourite use case of AI today.

I refer to this use case as a “Digital Brain”.

To be clear, creating a digital brain with AI is not to replace you. It’s a space that helps you store all of the things that you’ve collected like your notes, your insights, research, but also helps you continue to learn and grow by leveraging these.

Back in 2022, Tiago Forte introduced the world to the concept of a second brain in his book “Building a Second Brain”.

My TL;DR (too long; didn’t read) overview is it was a system to help people organise all of the information spread across their life into a digital space like Notion and Obsidian. We could say note-taking on steroids.

While the idea is intriguing it still felt like a storage house rather than something living, unless you put in a lot of effort to make it that way.

Thankfully, AI changed this.

A lot of the challenge with creating knowledge management systems, which usually comprises things like your ideas, your notes, quotes, and operating processes, is that they are just lost in the web of tools that we’ve been using in the workplace and on personal devices.

I’ve always been jumping between so many tools in my personal stack. I’ve used Google Docs, I’ve used Notion, and I use a Mac-only app called Bear. These are mostly static libraries too.

Google and Notion now have AI baked into the interface, but I’m not totally convinced that’s a good thing.

What I love about AI is that I have the ability to put all of those assets in one place, not only as a storage locker but a space where I can revisit, I can ask questions, I can have conversations, I can generate new ideas and new ways of thinking out of everything that I’ve collected. I can even use these tools as a production studio to amplify and expand on those assets.

I hate using this word because it’s overused these days, but it really has been a game-changer.

What I recommend, if you want to create your own digital brain, is to use a tool which I love and pretty much have been in love with for quite some time called NotebookLM.

NotebookLM, from Google, is a space where you can do so much stuff. It would be a disservice for me just to say it is a space for research and analysis only. NotebookLM lets you create assets called notebooks, which are basically like project folders.

You can add up to, depending upon how much you’re paying Google, 500 sources at a time in one notebook, and you can have multiple notebooks, so you’re not restricted. But it doesn’t only store these assets, it gives you the opportunity to build from them in a production studio.

In this production studio, you can create:

• Audio overviews, which are basically podcasts of your notebook
• Slide decks
• Reports
• Blog posts
• Study guides

If you haven’t used NotebookLM before, I have a free video series on YouTube which will give you every single tutorial you need.

How to get started

In terms of creating a digital brain, NotebookLM is a great starting point, because you can create notebooks for lots of different categories.

One of the ways that I really enjoy using it is, of course, continuing to sharpen my skill set. I have some notebooks that have been running now for probably two to two and a half years that contain all the latest research and documentation around many topics.

One focused on generative AI skills, another on core human skills, and what I do is use those notebooks to store all of that research. I go back and have conversations about what the research told us a few years ago versus now, what insights can I uncover in that comparison, and also to break down all of the latest stuff that’s going on.

As I say, it is an incredibly underrated use case.

As a small team or a small business, you could use NotebookLM to store information, learn from it and share these notebooks with your team to collaborate on too. It’s a great opportunity for you, your team or clients to work on notebooks together for research or projects, onboarding, strategy development and performance reviews as well.

This is probably, for most of you reading this, going to be one of the biggest advantages that you can spend your time on now.

Get started with this by taking half an hour this week to explore my video series and start experimenting.

2/ Employee onboarding

Tools: NotebookLM, Claude

Yes, I’m going to mention NotebookLM again.

Onboarding is a shared experience for every single organisation. It doesn’t matter if you’re a team of five people or a team of 5,000 people. This is an incredibly pivotal process and milestone in the life of your organisation and individual employees.

You want to give the best experience possible, right?

There are some great opportunities for AI in this space, so let’s explore those.

I have seen lots of small teams and companies use notebooks for employee onboarding. You can bundle any documentation, and the important stuff that people need to know about the organisation to get started in their company in the first few weeks, and let your employees have a conversation with that document.

This is helpful because there are always loads of questions that we want to ask as new joiners, but we don’t always feel confident in asking a manager or the HR team. Sometimes there isn’t time, and things fall through the cracks.

Another idea, and this is kind of a two for one here, is that you could create a very simple AI assistant.

Now you can do this with Claude, or you could use something like Sana Agents as well, where you can create an assistant or an agent, as people like to call it nowadays, because it seems those words have interchanged.

This can act as an onboarding buddy.

It doesn’t replace your onboarding experience. It is another tool in the ecosystem of tools that you put in that onboarding experience (an example of how this works in practice).

As I mentioned, there are lots of questions that we have as newbies, and sometimes we don’t know where to find the answers.

So having a little always-on digital assistant where we can say:

• Hey, how do I get myself set up on the printer?
• Who do I need to talk to about this?
• Where’s the team for that?

We can shortcut some of that stuff with an AI assistant paired with the right data.

This is about supporting that process overall and filling in the gaps where they might exist. I hope it gives you some inspiration to think about how to use AI with onboarding.

3/ Performance reviews and 1:1s

Tools: NotebookLM, Claude, Sana Agents

I’m bucketing these two under the same category because they are kind of related.

I think the ideas will make more sense with this connection, but stay with me.

Let’s start with 1:1s. One of the ideas that I’ve always loved and would use if I were still in corporate is keeping a catalogue of 1:1 conversations within a tool like NotebookLM or within a project in Claude. I would do this as a team member, but I’d also do this as a manager.

I would have a running schedule of the notes from all of my conversations:

• TL;DR overviews
• Actions that I want to do
• General notes from these conversations

This gives me a holistic picture of how those conversations are going.

I can use Claude to dissect them and understand how I’m feeling about my own performance, and how people are feeling in their 1:1s too.

Over time, that becomes a way of tracking what performance looks like across a broader view.

Here’s how you could do this:

1. Create a project in Claude, or a notebook in NotebookLM, to create a space where you can put all those notes, all dated by the time you’re having those conversations, either for yourself or for you as a manager with your team members.
2. Add your files: Keep any feedback you get on yourself and/or team. Then you can have a conversation with your tool of choice to get a holistic view of how performance has been across a 12-month period, instead of relying on recency bias with whatever you’ve heard in the last two weeks.

4/ Strategy development

Tools: NotebookLM, Claude, ChatGPT or any LLM

I think this one is probably straightforward. You can achieve the same results in most of the AI chat (LLM) tools that are available today.

Too often I see people use AI only in a Q&A style, so you ask something, it answers and that’s it.

The real gold is in a long-form conversation where you provide context (emails, documents, your own thoughts/ideas) and explore these to uncover insights and create new points of view.

Those who do this are choosing to become wiser with AI, not just look smarter.

Let’s say you’re a small business owner who wants to formalise a strategy for the next year.

Here’s how you could work with AI:

1. Open your AI chat tool of choice
2. Create a project folder if using Claude/ChatGPT/Copilot or a notebook in NotebookLM
3. Feed it context with any documents on previous strategies, business results, customer feedback etc
4. Compound this by sharing your goals/ambitions/wishes for the business for the year ahead
5. Now, converse with your AI tool about past performance, what can be learned and steps you could take to achieve this year’s progress
6. Work with AI until you understand your options ahead

The goal is not to get AI to write your strategy.

Instead, you’ll use this interaction to learn more about your business and how to build a plan that makes sense for your business. Don’t expect AI to craft you a “great strategy” on its own.

Note: Steps 2 + 3 are vital to make this whole thing work. You need a centralised workspace to store your chats, documents and manage what your AI remembers. I don’t advise random chat threads that you struggle to find in three months’ time and your AI cannot recall when you need it.

This is an older video but the thinking behind it with context and conversation remains the same.

5/ Research + Analysis: competitor, internal and industry based

Tools: Claude, NotebookLM

You might already have used AI for this, but if you haven’t you certainly should.

Again, no shockers, Claude and NotebookLM are my go-to recommendations here.

AI has reshaped the way we surface research. Both in a good and not so good way (looking at you, hallucinations). It’s never been easier to make use of Deep Research features across most popular LLMs to uncover and analyse hundreds of sources in under 30 minutes (NotebookLM is my favourite to do this).

As a small team or business, this is another powerful weapon in your arsenal.

No matter if you’re keeping up to date on industry trends, exploring what others are doing to tackle a problem or scouting the competition. Each of us is doing some form of research and analysis almost daily.

There’s no need to take a course to understand how to do this either. Just visit your AI tool of choice, tell it what you want to achieve and engage in back and forth conversation until you get something that works for you.

Once more, I’d suggest creating a notebook for NotebookLM users or a project folder in Claude to keep all of this in one place.

6/ Customer support

Tools: ElevenLabs, Sana Agents

Getting to the heart of what your customers need and making that a reality is the goal of any great team and business.

Sadly, the support functions of many small teams and businesses can often be a let down. We have an opportunity for AI to make that experience better. Anyone, and I mean anyone, can now create an AI customer support assistant.

You don’t need to be technically minded, you just need to understand what makes a great experience and how to set up AI to enable that. This could come in the form of a text or voice based (or both) assistant that sits on your website and/or product. You could even take this a step further and build a fully capable voice agent trained on your organisation’s knowledge base that customers can call directly to resolve any queries.

Check out these videos to see those ideas in action.

Final Thoughts

Remember what I promised at the top: all of this for less than $50 a month.

Here’s roughly how that stacks up:

• NotebookLM: free tier is good but I recommend the $5 per month plan with Google Gemini Plus
• Claude: $20
• ElevenLabs: $6
• Sana Agents: $0 month with limits (paid plans available)

You don’t need a transformation programme or a six-figure budget. You need one or two tools, your own context and half an hour a week to experiment.

Pick one use case from this list and start there this week.

Go forth, human.

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

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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’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.