Want a peek behind the curtain to see what the Wizard of Oz gets up to?

This is it.

I’d class this as a minimal playbook in what I do when working with companies to construct both meaningful and practical AI upskilling programmes. Nothing to do with perfect prompts or AI-first operations. This is about understanding the human a little more.

Here’s a little preview of the daily discussions I have with fellow humans:

Me: Generative AI can’t do that.

Client: That’s crap. It’s useless!

Me: No, your understanding of how GEN AI works and tool selection for tasks is crap.

Obviously, I didn’t say it quite like that.

Many drink the Kool-Aid from social media. Expecting Gen AI to do everything.

Why discovery is so important

A core part of my work with upskilling L&D teams starts with a discovery conversation.

When it comes to AI upskilling these are incredibly revealing for 3 reasons:

1. I get an idea of what they know vs what they think they know about Gen AI
2. Discover what they believe Gen AI can help with at work
3. Unpack what non-AI tech they already have

These 3 things tell me a lot about a team’s knowledge and maturity level.

It’s safe to say that number 1 is where so many people fall.

↳ You can’t use a tool intelligently if you don’t understand how it works.

My toolkit

In addition to my super-savvy performance consulting skills, I use two tools in particular:

1. My AI skills readiness model
2. A no-nonsense set of 9 questions to cut through the BS

Let’s explore these ↓

The Generative AI readiness model: Where are you?

Ok. Time to get clear on where you are today.

I’m 99% certain as of 2024, you’d do well just to reach ‘Awareness’.

The forward thinking teams and individuals will be aiming for ‘Exploration’. Everyone is still figuring this out. Take your time at the ‘Awareness’ stage.

Phase 1: Awareness

You recognise the importance of Gen AI but have limited understanding or practical experience. Here, the focus is on building basic knowledge about what AI is, its potential benefits, and the various technologies involved.

Phase 2: Exploration

You actively engage with AI concepts through learning and experimentation.

You may start small-scale pilot projects or experiments to understand how AI can be applied to their processes or products.

Phase 3: Adoption

This signifies a commitment to integrating AI into organisational processes or products. At this level, there is a good understanding of AI, and it’s being actively used to enhance operations, decision-making, or customer experiences.

Phase 4: Scaling

You’re fully prepared to integrate and scale AI solutions.

This means having the technical capabilities, strategic alignment, and organisational culture to support AI at scale.

→ Learn how to use this model to improve AI skills in your company with a 6-step playbook.

9 questions to design practical AI skills programmes

I use these with my clients, but you can use them with your stakeholders.

Adapt these to your specific needs. Context is everything!

1. What specific tools or technologies are you currently using in your workflow that involve AI or machine learning?
   
   Why: This helps you understand their current technological landscape and familiarity with AI.
   
   
   
2. Can you describe some of the tasks or projects where you think AI could have the most significant impact?
   
   Why: This question aims to identify specific areas where AI can be applied effectively.
   
   
   
3. What are your main goals or objectives in integrating generative AI into your processes?
   
   Why: Understanding their level of GEN AI know-how is an essential part of your discovery.
   
   
   
4. Are there any challenges or pain points in your current workflow that you believe AI could solve?
   
   Why: This question helps in identifying problems that AI could potentially address.
   
   
   
5. How do you envision generative AI changing your role or the work of your team?
   
   Why: We use this to encourage teams to think about AI’s potential to transform their daily tasks and overall workflow.
   
   
   
6. What level of understanding and hands-on experience does your team currently have with AI and machine learning technologies?
   
   Why: You can’t create a great programme without knowing where they are today.
   
   
   
7. What are your expectations for this workshop? Are there specific skills or knowledge areas you hope to gain?
   
   Why: A killer question. Now you’ll understand how real expectations of GEN AI tech is.
   
   
   
8. How do you plan to measure the success of integrating AI into your workflow?
   
   Why: Does any of it actually count if it’s not measured?
   
   
   
9. What kind of follow-up support or resources do you believe would be helpful after the workshop?
   
   Why: The learning never stops. Help teams think long-term about this tech
   
   

Final Thoughts

Gen AI tools have opportunities and limitations.

Spending the last year teaching 300+ students through my online course and working with 20 companies has validated a HUGE revelation for me:

There is a serious misconception about how GEN AI tools work that’s crippling many from maximising them.

Use these methods to craft meaningful upskilling programmes for AI in your work.

If you’re reading this and thinking “I want to become AI confident”.

I can help you with that. Learn more about my no-fluff AI upskilling services for Business (or please recommend me to a friend 😉).

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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 see the same trend of 2-3 new AI reports every week.

They never say anything that new. People are mostly using AI to create more content but at speed.

We’re expecting people to just figure out how to do better with tools.

My experience has taught me that it rarely succeeds. You need to show people the enormity of the possible. There’s a controversial Steve Jobs quote I reflect on when new tech lands:

“Some people say, “Give the customers what they want.” But that’s not my approach. Our job is to figure out what they’re going to want before they do. I think Henry Ford once said, “If I’d asked customers what they wanted, they would have told me, ‘A faster horse!'” People don’t know what they want until you show it to them. That’s why I never rely on market research. Our task is to read things that are not yet on the page.”

Steve Jobs

Our industry (and many others) has fallen into the trap of a faster horse!

We acquire a powerful tool only to use it to 10x the very things that cripple us. Certainly not working smarter. The thing is it’s not your fault.

Too many people keep writing reports when we need to show the way. I’m bringing my own contribution to this with my weekly LinkedIn videos and YouTube tutorials.

This leads me to today’s big thought to explore together → examples of unexpected ways AI can increase your critical thinking skills.

AI is not killing your critical thinking

AI has been getting a bad reputation with critical thinking skills of late.

Here’s the thing: AI is not killing your critical thinking – You Are!

While click-baity headlines try to shock you, digging a little deeper gets to the truth of the matter. Those who see a regression in critical thinking skills have a high over-reliance and trust in AI.

Typically they fall into 3 columns:

• Don’t have the necessary level of competency with critical skills without AI
• Are lazy and can’t be bothered to analyse AI’s work
• Seek instant gratification, and blame ‘lack of time’ for the inability to think critically

AI is only as good as the human using it.

In this case, don’t blame the tool when you’re in control. The choice to forgo critical thinking is a human one.

AI can both amplify and destroy many skills, the choice to do that is yours.

As an example of amplifying critical thinking skills with AI, here’s a little story…

I hate writers block

I can only describe it as staring at a wall with no door.

You’re constantly wondering “How do I get through this thing”. It’s a mixed bag of emotions.

Confusion leads to frustration, which leads to disenchantment.

One day, for no apparent reason, I flipped open my browser to find myself on ChatGPT. The loveable, divisive and often misunderstood digital conversationalist of our time.

I love AI, but I’m not in love with it.

Writing is so personal to me that I let nothing touch the edges of my words and thus the thoughts captured within them. Yet, I found an unexpected value in our little AI friend.

Our conversations, despite how dumb they were from time to time, were firing my neurons so much that the annoying wall was crumbling quickly.

In some way, my conversations with AI were making me think deeply, critically and more meaningfully.

I had to consider:

• How could I translate what I was trying to overcome to this digital being?
• What were the right words?
• Which examples would illustrate the mythical wall to a non-alive creation?

It turns out there’s a lot more thinking involved when working with AI than you would first assume.

I believe that’s a good thing.

Unbundling yourself from the framework of search engines is vital if you want to make valuable use of your local AI friend.

The first conversation was simple, but it sparked curiosity

ChatGPT has no idea what writers block is.

Not in the way I experience it as a human. This is actually an advantage. It’s not overcome with the emotions of frustration and the want to bang your head off a table.

It’s far more stoic in its assessment, which is what you need.

This became an asset when I was ideating around the first live event I planned to host in my city. I was brainstorming how I want to position the event and what I want it to achieve.

Two problems hit me:

1. I couldn’t make sense of the endless streams in my head
2. I was doing an awful job at trying to explain that to ChatGPT

I decided to flip the script.

Since I’d shared a bunch of context (more like ramblings) with ChatGPT already. I thought it would be better if it could ask me questions to clarify my thinking instead.

So that’s what I did.

I asked my little digital companion ”Ok, let’s try a different tactic. Ask me key questions to help create a compelling offer.” 

Then came this gem ↓

I was impressed, that’s for sure.

Challenging AI’s perspectives widened my own

I tend to do these kind of exercises a lot.

Often, I’ll feed a conversational AI tool a piece of my work or an idea I’m working on.

I’ll ask it to:

• Poke holes in my thesis
• Highlight anything I could have missed
• Opportunities to view the topic from a different cultural viewpoint

These are some of my favourite ways to enhance my human skills.

I can see issues from angles I might not have considered before. That is a useful tool, imo.

Try it yourself with these ideas:

More unexpected benefits

Now, what I didn’t expect in these interactions with AI was a spillover into human conversations.

Spending so much time thinking about how can I explain ‘x’ to AI improved both my thought process and the structure of conversations with humans. In short, my ability to convey ideas and assess the level of detail needed improved.

It was odd to notice, but very much welcomed.

Interestingly, Google has discovered the same in experiments that showed how AI is reshaping how we learn through metacognition. I wrote about this experiment when we explored the real impact of AI on our skills.

One of the standout quotes from the research perfectly aligns with today’s exploration:

“In a world where AI can generate content at the push of a button, the real value lies in understanding how to direct that process, how to critically evaluate the output, and how to refine one’s own thinking based on those interactions.”

Ben Kornell, managing partner of the Common Sense Growth Fund

AI doesn’t like one-liners

Spending the last 25 years using one way to surface content online has built a strong auto-pilot in us all.

Working with generative AI is the total opposite of searching for content on Google.

One sentence questions stuffed with keywords don’t work here. You need to give conversational AI tools like ChatGPT lots of context and clear instruction. A prompt is just a instruction to a database after all.

It takes a lot more work to get a decent response from these tools than social media portrays.

Here’s an example of that in action with a report I was distilling ↓

Notice how I don’t just say: “Review this report”.

Prep the mind, perfect the prompt

Full disclosure: There’s no such thing as a perfect prompt.

They’re often messy, don’t always work every time in the same pattern and need continuous iteration.

Saying that, you can do a lot (and I mean a lot!) to set yourself up for success. Here’s a (sorta framework) I use to help think critically before, during and after working with AI.

How to think critically with AI

Step 1: Assess

Can AI even help with your task? (It’s not magic, so yes, you need to ask that)

Step 2: Before the prompt

• What does the LLM need to know to successfully support you?
• What does ‘good look like’?
• Do you have examples?

And, most importantly, don’t prompt and ghost.

Step 3: Analyse the output

• Does this sound correct?
• Is it factual?
• What’s missing?

Step 4: Challenge & question

I’m not talking about a police investigation here.

Just ask:

• Based on my desired outcome, have we missed anything?
• From what you know about me, is there anything else I should know about ‘x’? (works best with ChatGPT custom instructions and memory)
• What could be a contrarian take on this?

Step 5: Flip the script

Now we turn the tables by asking ChatGPT to ask you questions:

Using the data/provided context or content (delete as needed), you will ask me clarifying questions to help shape my understanding of the material. 

They should be critical and encourage me to think deeply about the topics and outcomes we've covered so far. Let's start with one question at a time, and build on this

This is a powerful way to develop your critical skills and how you collaborate with AI.

🧪 Experiment to try

Feed your AI tool of choice with an example of your work.

This could be a workshop, email or a proposal. Whatever you’re working on right now. Make sure it’s not something sensitive if you’re not using enterprise AI tools.

Then ask the LLM to:

• Review your work and provide an abstract on what it thinks the topic is.
• Suggest points that could be improved and how.
• What could be missing?

Feel free to add your own too.

👩‍💻 Prompt playground

A prompt for the above could look a little like this:

# Context

I'm a [insert role] creating a [thing you're building].

I've attached a document which is an outline of my work so far. I want to improve this, specifically looking at:

- [suggestion 1]
- [suggestion 2]
- [suggestion 3]


# Task

Your task is to review my work and provide the following:

- A maximum 100 word abstract on what the document is about
- Points that could be improved
- Anything I'm missing about the topic
- Analysis of the tone, style and structure

Be direct in your feedback. Challenge me when needed. Your goal is to help me uncover blindspots to improve my work.

If you're unsure of any of the above points, ask me questions to clarify your outputs.

Present these bullet points back to me with:

- a header
- Bullet point summary
- numbered suggestions (if applicable)
- examples for the suggestions you recommended. Ensure to include links to these materials

Confine your review to my [topic].

Let's begin with our first task.

Write a 100 word abstract about the topic of this document as you understand it.

[Example] Unearth better insights from data

Final Thoughts

The key to preserving and enhancing skills is you.

You’re the main character of your choices. If AI even has a chance of killing your critical thinking skills, you need to have a good level in the first place.

→ 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.

Despite what the social ‘experts’ tell you, it’s never as good or as bad as it seems.

This applies to our current cult-like craze of Generative AI tools.

In reality, AI won’t change L&D as much as you think.

Having participated in what feels like hundreds of sessions about practical AI applications, how to intelligently assess tools and answer burning questions from the audience.

There are 3 things I find myself repeating often:

1. AI won’t change the role of L&D pros as much as you think
2. Most L&D pros haven’t figured out what to do with the time AI could give them back
3. AI needs a ‘human in the loop’ to work well

Let’s unpack all 3 thoughts.

Everything’s changing, but not that much

Too many of us have an inflated view of current Generative AI capabilities.

The way some people talk to me, they think it can do anything they want. Sorry to be the party killer, but it can’t. These perceptions come down to lack of understanding the fundamentals of how generative AI tools work.

Which is why we see too many overstatements of the importance of GEN AI on social media.

We expect it to do more human stuff. But, you don’t want that. You should be doing the human stuff and using AI to enhance the low-level tasks that stop you from maximising your human capabilities.

Of course, no popular online person will say that. It’s not dramatic enough.

The business of people

A quote in one session with L&D researcher, Don Taylor, made “Tools change but the role doesn’t”, resonated with me.

This is right on the money.

Our role has been and still is to enhance skills and the performance of a workforce. I don’t see this being massively disrupted with GEN AI tools. It’s good to remind yourself that GEN AI is just one of the tools at your disposal.

It’s not your whole strategy for learning and performance.

We’re still in a business of people. That business needs a unique human touch as it involves figuring out problems other humans experience. Try explaining that to ChatGPT.

To navigate the complexities of humans you need awesome human skills.

What are those skills exactly? These, my friend:

• Critical thinking
• Emotional intelligence
• Analytical judgement
• Decision making
• Explaining, talking and emphasising like only a human can

In my time so far, I’ve seen very few people get to senior roles on technical skills alone.

If they have, a lack of strong human skills has been their blocker to going further and even performing where they’re. You might have examples of this in your workplace too.

The future is still human-powered.

If you want to do well, get clear on the fundamentals of new technology and leverage it with your unique human skills.

Perhaps, that’s a formula we can all adopt.

What to do with all that time AI saves you

There’s an obsession with using AI to save time by speeding up tasks.

Yet, no one asks – at what cost to quality?

It seems too many will sacrifice quality for speed. Where does that leaves us? Nowhere good that’s for sure.

Building on from that, what do we intend to do with the x amount of hours saved?

Stare into the abyss, create even more pointless content or do more human stuff. I’d recommend (and hope) you do number 3. We’re in the business of people, after all.

I like to think we spend more time doing that performance consulting we all rave about at conferences. You know, building relationships across the business to give us the real insight on performance blockers.

AI can’t do all that (yet).

I’m asked weekly, “What will L&D pros do in a world of AI and automation?”

The answer is the same thing: Build relationships, focus on performance and enable the environment, systems and culture to enable people to do their best work.

Somewhere along the line we lost sight of the uniquely human practice we nurture.

→ It’s not your fault.

If you have a $400 billion learning technology industry determined to sell you products, it’s hard for your mind to compete with those marketing budgets. This doesn’t mean tech is not useful.

Digital technology is a incredible enhancer to what we do. But, it’s not the only thing we do.

So, the answer to how do I navigate the AI tech wave to future-proof my L&D career is quite simple:

1. Always invest in your digital intelligence aka understanding the fundamentals of the latest tech
   
   
2. Be more human

Since our last conversation on this, the smart folks at BCG gathered data on this very point.

As a reminder from the edition where we unpacked this data, we can see on average people are saving 5+ hours when introducing AI tools intelligently.

The ‘human in the loop’

If this was one of those Marvel films, this would be the time when the superior spandex laden superhero appears to save the day.

You might have heard of the concept ‘human in the loop’ if you’ve been super nerdy in your AI research.

If you haven’t, the term refers to human input into the development, training, and operation of AI systems. It’s about collaboration between man and machine, not one or the other. I believe this is the best way to work with these tools.

That’s why when I’m asked “Will AI take my job in L&D?”, I reply “It depends”.

It depends if you’re building a human in the loop (HITL) with AI assisted tasks, and the answer is – you should!

The HITL approach leverages that collaborative approach I mentioned to improve accuracy, reliability, and adaptability of AI tools. You (the human) are the key ingredient in working with AI. If you’re human skills suck, AI won’t help you much.

As humans, we provide key context.

AI can do many wonderful things but it can’t apply those contextually. Not right now, anyway.

So, if you’re sitting their worried about AI taking your job – Don’t. Until SkyNet rises and starts building Terminators, you have a clear place in the flow of work.

The infinite timeline

I get its hard to see this when social media is ablaze with inflated stories.

Most people use Gen AI tools for creation. That’s less than 5% of their potential in my eyes. In reality, they’re overall potential is greatly untapped.

I compare the current state of AI use for work to giving a Ferrari to a 5 year old. People don’t have the skills, experience or know-how to use it effectively.

That will change.

We’re talking years here not days. I keep going back to this image from Oliver Wyman with the scaling model for AI adoption and ROI. Time is on your side.

Prompt playground: Try it yourself

Copy and paste this into your AI assistant of choice to think critically about the future of L&D.

###Context###

I'm a learning and development professional who wants to explore the opportunities and implications for generative AI in my industry.

I want to cultivate a diverse set of views on both the good and bad of this technology for helping humans learn and grow. This should include how the traditional L&D industry could change.

###Task###

Your task is to help me cultivate a balanced view as shared in my context.

To begin, I want you to provide a general overview of the potential impact (good and bad) of generative AI technology in corporate L&D. 

###Task 2###

Next, you will ask me questions to explore my own thoughts on the topic. Don't be afraid to challenge my views. Your goal is to encourage me to find a balanced viewpoint that considers many points of view.

Begin with the overview and provide the sources from which you used to create your response.

Final Thoughts

A few resources to help you

1. If you want to learn practical applications for AI tools in your work as a L&D pro I have a 2-hr online course to support you. 300 L&D pros have taken the course to accelerate their skills.
   
   
2. Zero-cost insights, guides and tutorials on using AI in your work in my AI For Work Lab.

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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.

Did you know the number of Workplace L&D Tech and EdTech companies that advertise AI as a core feature has exploded by 255% since 2022?

So we could say every company is an AI company.

But where does this leave us, the humble L&D budget holder, trying to figure out who has the good stuff to help us reshape what we do?

That’s what we’ll explore today with 10 essential questions to ask suppliers about their AI capabilities.

This is part 2 of my “making smart tech purchases in the age of AI” series.

I’m aware that’s a terrible title, but my tea just ran out.

I recommend you check out part 1 to build your pre-game approach, aka before you talk with any tech suppliers.

The goal of the series is to help you:

1. Pick the right partners to explore
2. Know how to assess the AI capabilities of tools and platforms with smart questions
3. Make the best purchase for your organisation, team and goals

You can access part 3 with an in-depth guide to choosing the best supplier and platform combo.

Technically, you can use all of this playbook series without attending a conference this year. My mission is to make sure you spend your L&D tech budget on the right stuff, not more stuff.

The AI takeover

Yes, you know it, and I know it – every learning platform has AI.

But what does that really mean?

With generative AI evolving so fast, something you purchased last week could be outdated by next month. While it’s hard for suppliers to keep up, it’s good to understand their approach to AI-powered features.

To help you with this, I’m giving you my 10 essential questions to ask suppliers about their AI capabilities.

This list is a combination of curation from this Josh Bersin article, my experience from multiple implementation projects, and too many chats with AI and deep chats with fellow humans.

I like to think it’s been battle-tested!

Btw, you don’t need to ask all 10. Use whichever works best based on your context.

10 questions you must ask suppliers about their AI capabilities

Why these questions matter

I’ve crafted these questions keeping in mind that you’re probably a non-techie.

Depending on the size of your organisation, you can call in help (and def should) from any departments working in tech purchasing, machine learning and AI (of any kind).

But I’m aware that luxury is not available to all.

So, with that in mind, these questions will help you no matter your technical expertise.

Yet, if you want someone to help you with the research, identification and decision-making process with AI features in your platforms, you can book a consulting session with me to deploy my thoughts as and when you need them.

Anyway, shameless self-promo plug over – let’s focus on the questions.

1. What LLMs do you use and why have you selected these models?

Why this matters: Not all large language models (LLMs) are created equal.

Some are great at summarising complex topics, others are better at casual conversation. You need to know why they picked an LLM and how that supports the performance of features across the tool or platform.

You want a reasoned choice based on your industry needs, scalability, data security, and feature performance.

2. How do you select and manage the data for training your AI models?

Why this matters: The quality of data feeds the quality of the AI.

Now, this question is a little more technical and can leave you exposed without the necessary knowledge. So by all means, if you’re not comfortable with your understanding, remove this one.

For 99% of companies, they will use one of the large LLM providers in their stack.

Think of this like a wrapper. On the front-end, you see the brand you’re working with and beneath the surface the tech is powered by OpenAI, Anthropic or Microsoft.

This is standard – you just might not be aware of it.

In most cases, your supplier might have nothing to do with the data used to train the models. If they’ve purchased an off-the-shelf solution from one of the providers mentioned above, this will almost certainly be the case.

That is, unless they’re fine-tuning an instance of the model directly.

Fine-tuning means taking an AI model and giving it additional training in a particular domain. For example, a company may fine-tune one of OpenAI’s models on leadership principles to develop an AI leadership tutor.

This question can be converted to: Do you fine-tune your AI models or use them as delivered?

3. Can you detail the specific generative AI features your product offers?

Why this matters: “AI-powered” could mean anything from smart search to automatic course creation. You need specifics to know what you’re actually buying and whether it solves the right problems.

Ask about the concrete features tied to real user needs (e.g., summarising, content generation, personalised learning paths), and to help your internal team, of course.

Generative AI is only one part of the much larger AI family.

4. How is user feedback incorporated to enhance your AI models?

Why this matters: Again, use this like question 2.

If the supplier is using an out of the box AI model and not fine-tuning, they might have no control over this. But it doesn’t hurt to ask.

Knowing how feedback is used to improve the product is always useful. It doesn’t have to be linked to the AI model itself, it might be how the user interface works to enable you to make the most of those features.

5. Can we talk with customers using your tool and see how well it performs?

Why this matters: Real users don’t have a marketing agenda.

Conversations with existing customers will give you a balanced view. If you do get this opp, ask these people how they’re using features, why these features and the impact they’ve seen outside of “We can create more content faster”.

6. What kind of support and updates can we expect to keep the AI solution current and effective?

Why this matters: With Generative AI, standing still is falling behind.

Without regular updates, models degrade, and tools can quickly lose relevance. We want to avoid this by looking at products as ever-evolving. You want a partner that reflects this.

7. Can you share real-world examples where your tool has positively impacted learning outcomes?

Why this matters: You want proof, not promises.

An AI feature might look impressive, but unless it moves the needle on skills application and performance, it’s just noise.

Here, it’s useful to re-align with the core problem you’re trying to solve with this purchase. Seek to learn where these features directly link to this.

Tip: shut down generic statements like “customers love it” without any evidence.

8. How do your generative AI features stand out from what others are offering?

Why this matters: Everyone claims to be “AI-powered” now.

You’ll get these features anywhere today. So, what makes this supplier different?

What is the unique selling point they bring?

9. To what extent can it be customised to fit our unique needs?

Why this matters: No two learning environments are the same.

A rigid, cookie-cutter solution will create more problems than it solves. This is always an important conversation to have. As I said last week, you want a partner that supports your growth, not another provider who disappears after deployment.

10. What training and support are provided to help integrate and utilise these AI features effectively?

Why this matters: Buying the tech is the easy part.

Getting people to use it properly and consistently is the hard bit. Without structured training and onboarding, adoption will be slow, patchy, or doomed.

Again, this is why you want a true partner.

What’s the approach to work with you to not only deploy this platform, but make it a success?

And no, you don’t care if they have a ‘help centre’ for 24/7 doom-scrolling.

Prompt playground: Try it yourself

Copy and paste this into your AI assistant of choice to experiment.

# Context

I'm a learning and development manager looking to evaluate the latest learning technology. I'm attending a number of conferences over the next few months where I'll be assessing suppliers AI-powered products.

I want to be prepared by understanding the best questions about their product's generative AI features and larger AI capabilities.

# Task

Your task is to suggest no more than 10 questions I should ask as part of a validation process on their products generative AI capabilities.

These should be clear and easy for me to understand as a beginner in the world of generative AI.

Final Thoughts

Ok, this is your foundation.

You can and should customise this to your context.

I like to think of this checklist as your BS detector, negotiation tool, and insurance policy all rolled into one. I think in a world full of AI promises, the smartest move you can make is asking better questions.

Next week, we’ll explore how to assess your line-up of potential learning platforms and make the right decision for your context.

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

P.S. If you’re reading this and thinking, “I want to make better AI tech decisions”. I can help you with that. Learn more about my no-fluff AI upskilling services for Business (or please recommend me to a friend 😉).

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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.

Generative AI is everything, everywhere, all at once.

It can be confusing.

Cutting through the chaos to get to what’s practical and meaningful is not easy. It can be simple but not always easy.

A good place to start with navigating the chaos is getting clear on where you are today.

→ That’s where the Generative AI readiness model comes in.

It’s designed to help you know where you stand today and what you can do to improve your readiness. You can use this both in and out of the L&D world.

This post is broken down into 3 stages:

1. Unpacking the Gen AI Readiness model and your place on it
2. How to move to the next level of readiness with key indicators and actions
3. A 6-step process to improve your Gen AI readiness (teams and individuals)

The AI readiness model: Where are you?

Ok. Time to get clear on where you are today.

I’m 99% certain as of 2024, you’d do well just to reach ‘Awareness’.

The forward thinking teams and individuals will be aiming for ‘Exploration’. Everyone is still figuring this out. Take your time at the ‘Awareness’ stage.

Phase 1: Awareness

You recognise the importance of Generative AI but have limited understanding or practical experience. Here, the focus is on building basic knowledge about what AI is, its potential benefits, and the various technologies involved.

Phase 2: Exploration

You actively engage with AI concepts through learning and experimentation.

You may start small-scale pilot projects or experiments to understand how AI can be applied to their processes or products.

Phase 3: Adoption

This signifies a commitment to integrating AI into organisational processes or products. At this level, there is a good understanding of AI, and it’s being actively used to enhance operations, decision-making, or customer experiences.

Phase 4: Scaling

You’re fully prepared to integrate and scale AI solutions.

This means having the technical capabilities, strategic alignment, and organisational culture to support AI at scale.

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How to reach the next AI readiness level

Your Generative AI maturity won’t be fixed.

The way this technology advances, I’ve no doubt we’ll be swinging back and forth through the levels.

Like with the above. I believe the sweet spot for the next 18 months is in ‘Exploration’. Each level is broken down into two sections of ‘Key Indicators’ and “Actions”.

→ Indicators = the behaviours that should be displayed

→ Actions = the activities you/and your team are engaged with

1. Awareness

Key Indicators:

1. Curiosity about AI
2. Basic awareness of AI technologies and their potential impact
3. Initial discussions about AI opportunities.

Key Actions:

Attending introductory workshops, webinars, and consuming educational content on applied Gen AI fundamentals for work.

2. Exploration

Key Indicators:

1. Engagement in AI education programs
2. Experimentation with AI tools and platforms in workflow
3. Assessment of AI applications relevant to the organisation’s context

Key Actions:

• Participating in active upskilling
• Experimenting with AI tools in controlled projects
• Beginning to integrate AI into select processes for testing and learning
• A commitment to build foundational knowledge and skills
• Exploring AI use cases, and understanding the resources required for meaningful Gen AI initiatives

3. Adoption

Key Indicators:

1. Development of a Gen AI learning strategy
2. Data readiness assessment
3. Established AI governance practices
4. Live initiation of pilot projects.

Key Actions:

• Implementing AI solutions across various business areas
• Investing in more advanced training for teams
• Establishing best practices for AI governance and ethical use

4. Scaling

Key Indicators:

1. Proven Gen AI pilot projects
2. Established Gen AI best practices
3. Scalable GenAI infrastructure
4. High-performing Gen AI upskilling programmes

Key Actions:

• You innovate with AI to improve employee performance
• You freely share knowledge across the company
• You focus on continuous improvement and the development of impactful Gen AI capabilities.

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The 6- step process to improve your AI readiness

Applying Gen AI tools intelligently is more than tech skills. It’s a blend of human and tech know-how.

If you’re not happy with where you are.

Let’s break down 6 simple steps to change that:

1. Check yourself

First things first, let’s talk about a little self-reflection.

No, not the kind you’d post on Instagram, but rather a skills check.

Think wider than tech – how’s the team with change, solving problems, getting creative etc? Get clear on the culture.

→ What is the level of GEN AI knowledge today?

You can’t figure out where to go if you don’t know your starting point. They say learning is about recognising your limitations today. So, it’s time to get some clarity.

⚒️ Tools + Resources:

• Find your place (and your teams) on the AI readiness model
• GEN AI explained for humans
• Everything you need to know about Generative AI

2. Spot the opportunities

Where can AI really enhance work for you?

No gimmicks.

Identify the tasks that could benefit. Always think problem-first, never tool-first.

Your north star should be meaningful and impactful enhancements. This needs to align to your context. Where a huge corporation might find little use with AI support in business writing. A one person team could be transformed by it.

Context matters.

⚒️ Tools + Resources:

• AI Copilots powering the future of performance
• 3 ways to work smarter with AI in L&D
• How Gen AI tools are enhancing employee performance: 10 Insightful charts
• The data is clear: Conversational AI tools are improving skills at speed

3. Explore the field

Curious about how other folks are doing things?

It’s natural.

If you’ve got a good network in your industry, reach out to others. I guarantee they’re thinking the same thing. You could create a small community to build your knowledge.

We might be talking about AI. But the present and future is always human powered.

⚒️ Tools + Resources

• How BHP used ChatGPT to improve its leadership framework
• Why McKinsey created an LLM to give employees and customers access to thousands of reports
• 5 early adopters of Generative AI at work

4. Find the gaps

Reflecting back on our skills check, let’s identify what’s missing.

Is your team up to speed on AI basics? You can’t master these tools without understanding the basics.

Plus, nobody wants to be that person who pretends to know what they’re doing but doesn’t, right?

⚒️ Tools + Resources:

• Delve into the AI Readiness model again – where is your team today?
• How to craft an intelligent strategy for Gen AI collaboration
• The 5-step ‘make me AI confident’ learning strategy

5. Pick your battles

Put your focus where it counts.

Gen AI tools are not a saviour. Some gaps matter more than others, so choose wisely where to invest your energy.

⚒️ Tools + Resources:

• The Gen AI tools vs task assessment

6. Map it out

A goal without a plan means nothing.

Plan your move.

A clear upskilling strategy will take your team from AI newbies to pros quicker than you think.

⚒️ Tools + Resources

• How to create a impactful Gen AI upskilling programme thats meaningful

The AI Readiness Assessment For L&D Teams: Benchmark your maturity against fellow L&D professionals

I’ve created an easier way for you to discover your current level of maturity with AI in L&D, compare this against fellow industry professionals and learn how to keep growing.

You can get your personalised AI Readiness Report at zero-cost.

I built this for every L&D pro and team to get clarity on where our industry really is with AI maturity. Social media makes you think everyone is in front of you, but the reality is very different.

It takes less than 5 minutes to complete, and you don’t have to share any personal or company data.

→ Get yours now

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Final Thoughts

There you have it, friend.

Your personal strategy to becoming ready for Generative AI at work.

1. Get clear on where you are today
2. Scale at your own pace with intelligent moves
3. Don’t stand still

If you’re reading this and thinking “I want to become AI confident”.

I can help you with that. Learn more about my no-fluff AI upskilling services for Business (or please recommend me to a friend 😉).

I hope this helps.

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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.