Category: Artificial intelligence

One of the most common questions I’m asked by teams is: Where can AI REALLY help me at work?

That’s a question I like to hear.

Many people make the mistake of jumping to tools first instead of defining a problem.

I totally understand when a transformational technology like generative AI emerges, everyone wants to try it. And that’s great! One of the best ways to discover how these tools can be useful is through experimentation.

But, what we can do is apply a decision-making framework to structure that experimentation and ensure you’re using AI effectively.

The goal? Not bloody finding more use cases to create content – stop it!

Instead, I’ll focus on saving time, improving quality, and focus on more human things that matter. I like to class this as the boring and basic but HUGELY effective applications of AI in your work.

Not everything needs to be a huge multi-workflow project that takes more time to build and maintain than the ROI it provides.

Start with tasks

Everything starts with tasks.

We use our skills to complete tasks. With AI tools, we can identify specific areas where they can provide meaningful support.

In relation to tasks, we should ask:

• How long do they take to complete?
• What is the output, and does it justify the effort required?
• What does ‘x’ task enable you to do?
• Is it repeatable and low margin for error?

This is by no means a complete list.

If we assess these and add the lens of AI assistance, we can find potential opportunities to work smarter.

Identifying opportunities

I’m certainly not the first to propose this.

LinkedIn CEO, Ryan Roslansky proposed a similar model back in early 2023.

This idea originates from Ryan’s Redefining Work article, where he explores how AI will accelerate workforce learning and amplify the importance of skills.

Ryan suggests moving away from viewing jobs as titles, and instead, seeing them as a collection of tasks. These tasks will inevitably evolve alongside AI and other technological advancements. He recommends breaking your job down into its primary daily tasks.

You can bucket those tasks in this format:

1. Tasks AI can fully take on for you, like summarising meeting notes or email chains.
2. Tasks AI can help improve your work and efficiency, like help writing code or content.
3. Tasks that require your unique skills – your people skills – like creativity and collaboration.

This sets the stage for how I currently recommend working with AI.

You could say my framework is the process before Ryan’s.

Without understanding your tasks, it’s hard to say if/how AI can support.

Let’s unpack three real-world examples from my own work where I’ve identified tasks that fall into this category, how I use AI to support me and why.

1/ AI in Meetings

I spend a significant part of my week talking to different teams and individuals about how AI and technology can solve their problems, especially in learning and development (L&D).

Many teams still think of AI primarily in terms of content creation, but there’s so much more it can do.

Most of my meetings range from 15 to 45 minutes. Like many of us, I scribble quick notes in a doc or on a notepad, but they’re often fragmented and hard to revisit later.

But I really want to focus on the conversation rather than taking detailed notes.

It’s super distracting for me when I’m like “I should write down what they just said” yet, that means breaking off mid-convo, too unnatural for me. By the time there is a break, I’ve usually forgotten what I was supposed to write down.

To solve this, I use an AI meeting assistant to join calls, record transcripts, and summarise key insights (if participants are cool with that of course – always ask!). This might seem basic, but as I always say, boring and basic is sexy because it’s efficient.

We all go to meetings, we all need notes and we all forget important insights and useful ideas.

It’s a shared pain.

Instead of spending hours sifting through notes, I can quickly review key takeaways and action points compield by my AI friend. Nothing gets lost too because I can see the original transcript if I sense AI is off base with anything. Like that one time it thought I recommended unicorns as a solution – another story for another time.

This allows me to focus on human conversations without worrying about missing important details.

It genuinely improves my life.

A few tools to explore:

• Read AI
• SANA (as part of their AI platform)
• Granola (a new one I’ve been testing)

If your organisation already has approved AI tools, use those. Microsoft Teams and Google Workspace both have built-in meeting assistant functionalities. Never use anything outside your companies infrastructure.

TL;DR:

Not only does this save time, but it enables better human connection and helps keep track of discussions from weeks or months ago without relying on memory or notebook doodles (why do I always draw skulls? Question for a therapist 🤔).

You won’t find a billboard that reads “My AI meeting assistant saves me time, enables greater social connection and improves my work” but maybe it should.

2/ AI for Insights & Summaries

For most of us, reports are part of our jobs.

You might read short articles, while others (like me) deal with 250+ page research papers.

But let’s be honest – who has time to read all that in depth?

What we actually need is a way to synthesis key insights quickly while maintaining depth where it matters. Kinda like being your own Harvard Business Review.

A lot reports and research are a lot of fluff, an underrated skill is to distill, humanise and share the best bits for action with your audience. No one needs you to regurgitate what the report said.

Always ask, how do I serve my team/audience with this info?

One tool I really like for analysis is Notebook LM, I have a separate ‘How to’ video of NotebookLM, which you can check out if you’re interested.

It allows me to upload multiple documents (up to 300 🤯), analyse them together, and extract meaningful summaries.

Other AI tools help provide quick summaries, surface key insights, and even answer questions about reports and research too. NotebookLM is just my preference.

To be clear this isn’t about replacing critical thinking.

Instead, AI helps get that first layer of understanding:

• What is this report about?
• Why is it important?
• How can it help me?

This approach allows me to decide which sections deserve deeper human analysis rather than blindly committing hours to reading everything.

Something I find incredibly useful as these tasks eat up about 10 hours + of my week.

TL;DR:

AI summaries prevent wasted time on unnecessary deep dives, ensuring you focus only on the most impactful parts, and become better-informed vs overwhelmed with useless insights.

3/ AI in Data Analysis

We all deal with data in some form.

You don’t have to be an analyst to benefit from AI-powered support.

Whether it’s customer trends, sales numbers, content engagement, or HR reports, AI can help surface patterns that might otherwise take hours to uncover.

You can fire up literally any tools like ChatGPT, Claude, and Copilot to ask:

• Explain this data to me?
• How can I use it?
• What are the key insights and what are the actions I can explore?

Beyond data crunching, AI can even help you think more broadly by highlighting blind spots.

Again, helping you think critically in the process by working with AI to uncover:

• What you’ve missed
• What else could I consider (aka devils advocate)?

TL;DR:

Mostly, we look at AI to provide answers, but it can unearth better questions too. As always, the tools are only as good as the human using them.

Final Thoughts

If you’re sitting there thinking, I’d love to start using AI, but I don’t know where to begin, start with your tasks.

• ⏰ What tasks take up the most time?
• 🥊 Which ones require effort disproportionate to the value they create?
• 🤖 What processes could be improved with AI-assisted support?

Never forget, AI isn’t about automating everything.

It’s about enhancing your work so you can focus on higher-value, human-centred work.

→ 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.

AI has been promising a lot these last few years.

Previously, we looked at how Google is attempting to evolve the education system with its LearnLM model, specifically trained on learning science principles.

While I’m keen to see how that develops, I know a lot of us are in workplace L&D teams.

In my opinion, it’s less about learning at work and more about performance.

I’m sure there’ll be a few eye rolls at that sentence, but most organisations are looking to L&D to build capability and performance, not help people learn.

So, how can new tech support this?

Josh Bersin coined the phrase “learning in the flow of work” back in 2018, yet we never really had the tech to realise that vision. In 2025, it’s a different story.

Today, we’re exploring how AI is reshaping performance support at work with multimodal tools that can see and speak.

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Since generative AI tools crashed into our lives, everyone from tech bros to social media gurus has declared that it’s going to reshape education and learning.

We’ve seen AI models built specifically for education, changing how students receive support in a previous edition covering when AI is trained on learning science a few weeks back.

But what about the workplace?

Performance is bigger than learning at work

Workplace learning is a multi-billion-dollar industry, and everyone wants a slice of that pie.

Unlike traditional education, a lot of workplace learning is focused on performance. It’s getting people the right support exactly when they need it.

I’m not talking about learning deep concepts or broad skills for life. That’s a different game entirely. Performance support is about immediate problem-solving.

It’s resources, not courses.

The reality is we face a series of small – mid-level performance blockers daily

Stuff like:

• How do I use VLOOKUP in Excel?
• How can I convert this PowerPoint into Google Slides?

Yes, basic things, I’m aware.

These micro-challenges fill our workdays, but the answers are often buried deep in Google searches or trapped in some long-forgotten company SharePoint.

Now this might not be what you class as ‘learning’ but they stop people from performing, and if we agree that workplace learning is actually enhancing performance, then these are big problems.

Multimodal AI Tools are reshaping our experience

For those unfamiliar with the term “Multimodal” it just means multiple inputs and outputs.

So you can have:

• A text input and output
• A text input and visual output
• An audio input and text output

We’re no longer limited to just asking AI how to do X.

It can now see, hear, and respond in real time.

Voice and vision capabilities mean we can talk to AI tools, show them what we’re working on, and get on-demand help. Instead of scrolling through pages of search results, we can (actually) solve problems in the flow of work.

How you can try this at zero cost

I’m not going to assume everyone has a paid license to top of the range AI tools.

Instead, I’ll show you how this type of performance support can work with a zero-cost tool courtesy of those folks at Google.

In Google AI Studio, you can test multimodal features for free. You can talk to the AI, show it your screen, and work together to solve problems in real-time.

With this, you can:

1. Talk with Gemini Live.
2. Show it what you’re working on.
3. Share your screen for real-time support.

It’s simple.

Head over to Google AI Studio.

Select your AI model (Gemini 2.0), choose your output format (audio, text, etc.), and start collaborating.

And…of course, here’s a step-by-step video on how to do that ↓

Note: AI isn’t perfect. You, the human, still need to apply critical thinking and validate results.

AI as a Support Tool, Not a Replacement

This is where on-demand performance support is heading.

We’re not talking about replacing human expertise, but rather evolving traditional job aids, FAQs, and knowledge bases into dynamic, AI-powered conversational support systems.

We can:

• Help employees understand new concepts.
• Troubleshoot technical issues in real time.
• Set up hardware and software.

Not everything needs a full-blown training course.

Sometimes, we just need an answer now.

Plus, solutions like this can only help us focus more on the human stuff that matters. I mean, do you really want to keep buying and running Excel courses for teams in 2025? I’ll leave you to ponder that.

Final Thoughts

This is still in its infancy.

ChatGPT, Google Gemini, and other AI tools are already capable of vision and real-time interaction. More will follow.

The key question is: How will organisations (and you) use this?

Perhaps. it’s time, once again to rethink how we provide support with today’s technology. It’s not a case of either/or. We have an opportunity to shape how this plays out.

This is just the beginning.

→ 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.

It’s been hard not to talk about AI this year.

If we’re being honest, it’s been hard not to for the last few years. One day, we were fighting off a super virus, and now we’re gushing over generative AI tools.

Crazy how fast things change.

The past 12 months have given me plenty of time to work with various teams and companies on AI skills programmes. It’s taught me a very important lesson: despite the current pace of AI tool adoption, there is a lack of investment in the mindsets, behaviours, and meaningful skills needed to leverage them effectively.

It’s generic to say that AI, particularly generative AI (which are not the same, FYI), has opened up a transformational shift in how we work, learn, and interact with the world.

Yes, I’m playing Captain Obvious, but stay with me…

With any major technological shift, achieving a successful ROI doesn’t happen overnight.

The journey from what I class as a curious “hobbyist” to a confident “adopter” is a gradual one, and I cannot overstate how much patience you need to develop here.

Social media doom-scrolling makes it easy to feel pressured to learn everything about AI instantly.

Everyone and their dog is an AI expert today, and apparently, they can make you master AI in 7 days. Be wary of these people. They will stunt your chances of long-term success.

Building a deep understanding of such a transformative technology requires time and effort.

And to be quite frank, no one has mastered it yet. They probably never will, as it’s always evolving.

You already know my views on this.

Meaningful AI adoption is about more than just knowing how the tools work. It’s about cultivating a mindset and building behaviours that allow us to integrate AI meaningfully and responsibly into what we do.

→ And that takes time and effort.

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The 3 Stages of AI Literacy: Hobbyists, Experimenters, and Adopters

There are so many bloody maturity models out there right now.

While mine is not as fancy as a consulting firm, I believe it’s simple to use.

My work these last few years has shown most people are navigating through three broad stages of AI skills maturity: hobbyists, experimenters, and adopters.

Let’s unpack these ↓

Hobbyists

Hobbyists are those who dabble in AI, experimenting with tools like ChatGPT in their personal time but haven’t yet applied it systematically in their work.

They’re curious, but they haven’t reached a level of skill where AI significantly impacts their productivity. Mostly they create cat pictures and get AI to write crap social media posts stuffed full of emojis.

Experimenters

Experimenters have begun incorporating AI into their daily tasks, testing out its capabilities, and exploring use cases in real-world contexts. They’re still in the learning phase, figuring out what works, what doesn’t, and how AI fits into their broader workflow.

I like this level the most. To experiment, fail and learn is a beautiful thing. The majority of people who play here will do very well.

Adopters

Adopters have fully embraced AI, using it effectively and strategically in their context to enhance work.

They’ve developed a level of comfort and expertise that allows them to apply AI in ways that generate meaningful, long-term value. A caution here: I’ve found some who’ve gone too far down the rabbit hole have become blinded to AI’s limits. Try to avoid that.

Be balanced, in all things.

Aim for ‘Good enough’

Moving from one stage to the next is a slow process. Often frustratingly slow in a world where we expect immediate results.

That’s totally fine. It’s a necessary progression.

Without taking the time to fully understand the nuances of AI and how it can be harnessed, you risk missing out on the true potential of the technology.

A thread that weaves through each of these stages is experimentation and exploration. You will bounce between each stage as new advancements emerge. Right now, that’s like every other week.

It is entirely possible to be an adopter at the start of the month and find yourself back to a hobbyist without keeping up a practice of experimentation and exploration.

Always get clear on the ‘what, why and how’.

Classic advice for a reason.

Be intentional with AI skill building

This will sound counterintuitive, and yes CEO of x company, I know you want the ‘AI Effect’ today.

But with AI literacy, being more intentional can reap rewards for years – perhaps even decades.

I’ve seen this in some of my work with clients.

Senior executives have crazy expectations for workers to become ‘AI Experts’. They don’t even know what that means – I don’t even know what that means!

If we’re talking about tools like ChatGPT, becoming an expert on that with its almost daily updates is like chasing after your 5-year-old when they see an ice cream truck fly by.

Solid fundamentals will help, no doubt.

But fundamentals don’t = fully capable expert.

AI is not static.

Learning the fundamentals and taking time to put them into practice is key. Yes, I know that’s hard in a world where you need more than 1 week to show ‘ROI’.

By encouraging a more deliberate approach, you can craft the mindsets, new behaviours, and technical, and human skills to navigate AI transformations at large.

I know I’m preaching to the choir here.

(Note: Being more deliberate with crafting AI skills does not mean building bloated 3-month + learning experiences. No one wants or needs this!).

In sum: You need a bit of patience, time and structure but lots of experimentation. Again, counter-intuitive, I’m aware, but with a technology so transformational, we have to find ways for these elements to co-exist.

80% of AI projects fail because of this

Another report I’m reading, in what I must say, is an era for ungodly amounts of reporting on one topic, focuses on the root causes of failure for AI projects.

If I’m being fair, the findings of these failures apply to L&D projects too.

Anyway, one of the biggest factors for failure was being given the time for a project to succeed. You see executives are drinking the koolaid.

They think that what needs at least a year to succeed can be done in a week.

The writing is on the wall for most projects before they start.

You have no doubt suffered this exact problem with countless L&D projects.

Think of all the projects that have died because:

• Expectations were unchecked
• A problem was not defined to solve
• The resources you need to succeed weren’t provided
• You were given 1 week when you need 1 year

One word to define this – misalignment.

AI literacy is about building a long-term capability, not a short-term fix.

This creates a workforce that is not just technically competent, but equipped with the critical thinking, creativity, and adaptability needed to succeed in an AI-driven future.

Final thoughts

As a good BCG article once told me, “Treat Gen AI upskilling as a marathon, not a sprint”.

Yes, you need to move fast to help people unlock the potential of new technology. But, you also have to be smart. People won’t just get it after some 30-minute online course.

They will need more hand-holding than you think, and you need to inject a dose of realism into the ‘time to become proficient’ with your AI tools of choice. Marathons are a mixture of both fast and slower-paced elements.

Again, think constant experimentation and exploration. This is not a static game.

The investment in Gen AI fundamentals at most companies is criminally low.

Don’t fall into the trap of tools before educating on the basics. I’ve seen this back-fire too many times.

As the wise Uncle Ben said, “With great power, comes great responsibility” – and too many are forgetting the final part of that famous quote.

As I said in a recent newsletter:

With all-time high levels of use across millions of Gen AI tools and all-time low levels of AI literacy, we could be heading for a skills car crash of our own design.

Too many forget that AI is only as good as the human using it.

It’s, perhaps, the greatest ‘mistake’ made in all this AI excitement.

Here’s five things I suggest you do:

1. Teach AI Fundamentals: What is AI and Gen AI, and what is not? How LLMs work, etc
2. Behaviours + mindset: How to think critically and validate outputs. Understand AI hallucinations. Know when and when not to rely on AI tools
3. Practical use cases: Not cat pics, real work impact. You could combine this with ‘tools’ for experimentation.
4. Picking the right tools: Not every AI tool is created equal, so know the opps and limitations of yours
5. Upgrade human skills: You won’t go far without a strong sense (and clarity) of thinking and analytical judgment.

The key to all of this is time, patience and intention to build the right skills.

Sometimes that will be fast, others it will be slow.

[Bonus: Think about introducing some really simple and easy to follow guidelines for AI use at work. Don’t overcomplicate it with jargon! – think best practices, or as much of a best practice as you can give on this rollercoaster]

In sum: Don’t make the mistake of rushing the process of crafting meaningful AI skills and behaviours.

Oh, and if you’d like help with any of this, shoot me a message.

📖 Read more

→ The Hidden Impact of AI on Your Skills

→ How To Get More People Adopting AI at Work To Build Modern Skills

→ Designing Meaningful AI Skills Programmes

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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.

There’s a time and place for everything.

• Dumb hairstyles = school and college
• Overpriced tight-fitting clothes to impress the opposite sex = your 20’s
• Not being judged for eating an entire chocolate log = Christmas
• Using generative AI tools = ?

While I hope you agree with the rest, the last one is debatable.

Depending on your relationship with AI, your view on ‘when’ to use its delightful powers can be vastly skewed.

The ‘AI cultists’, as I like to call them, will proclaim we should use AI for everything, while ‘dooms dayers’ will warn you not to touch it as you’ll lose your humanity.

Of course, the truth of the matter is not so clear-cut.

There’s an interconnected web of assessments and decisions to be made. The good thing is this is all human-powered. The world has been so focused on ‘how’ to use new tools, that we’ve paid little attention to why and when.

Let’s change that.

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📌 Key insights

• AI is a tool, not a saviour
• Boring and basic is where AI shines best with tasks
• Balance your understanding and application for maximum benefits
• AI is not a hammer

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Assess tasks not jobs for AI

I appreciate LinkedIn CEO Ryan Roslansky’s concept of assessing ‘tasks, not jobs’ in the context of generative AI at work.

This idea originates from Ryan’s Redefining Work article, where he explores how AI will accelerate workforce learning and amplify the importance of skills.

Ryan suggests moving away from viewing jobs as titles, and instead, seeing them as a collection of tasks. These tasks will inevitably evolve alongside AI and other technological advancements. He recommends breaking your job down into its primary daily tasks.

You can bucket those tasks in this format:

1. Tasks AI can fully take on for you, like summarising meeting notes or email chains.
2. Tasks AI can help improve your work and efficiency, like help writing code or content.
3. Tasks that require your unique skills – your people skills – like creativity and collaboration.

This sets the stage for how I currently recommend working with AI.

Where AI helps best

You might see glamorous examples of generative AI tools on social media.

In reality, the majority of benefits come from tackling boring and basic tasks. I’m talking about writing better emails, summarising reports, and brainstorming ideas.

It’s smart to delegate simple, mundane, yet time-consuming tasks to AI.

This creates space for more human-centred work.

I don’t understand why some people seem determined to have AI handle the human elements. What a boring life that would be! I want AI to handle the laundry via a workflow so I can focus on building cool stuff – not the other way around.

A bunch of smart folks have done lots of research on this.

The above visuals come from Gallup and Asana, but I want to talk a little bit about a joint research project from Boston Consulting Group and Harvard.

These two powerhouses wanted to cut through the hype to see if AI tools like ChatGPT can improve productivity and performance. They worked with 758 BCG consultants (about 7% of their individual contributor-level staff) and split them into three groups:

• One without AI access
• One with GPT-4
• Another with GPT-4 plus some training on prompt engineering

These consultants tackled 18 real-world consulting tasks to see how AI would affect their work.

The results? Pretty impressive, I’ve got to say.

The consultants using AI managed to complete 12.2% more tasks and knocked them out 25.1% faster. But here’s what really caught my attention – the quality of their work shot up by more than 40%!

It’s one thing to do something at speed, but another to do it at such high quality too.

That’s the trap I see happening in every industry right now. Too many prioritise speed over quality. You can have both if you craft the right skills to collaborate with AI.

There was a catch though (when is there not!).

When consultants tried to use AI for tasks it wasn’t built for, their performance dropped by 19%.

I don’t see this as a negative. It’s very helpful to know where the limitations are. You cannot have a balanced approach without this. Another particularly interesting outcome was how the consultants ended up using AI.

Some folks took a hybrid approach, blending AI with their expertise, while others went all-in and relied heavily on AI.

Both styles seemed to work, but context was key.

While those marked as novice employees found the biggest performance gains, this dropped with those classed as experienced workers. Those in the latter category still saw a modest boost of 15% in most tasks.

TBH, I’d take that on most days.

You can’t buy time

Time is a fickle thing.

It’s our most precious and non-renewable resource.

If you’ve been to any of my keynotes in the past year, you will have heard me touch upon this. Perhaps it’s the broadened awareness of my mortality.

It’s probably got something to do with being very close to 40 years old, which my 23-year-old self didn’t expect to happen.

My impending mid-life crisis aside, time is something you should care about deeply.

You can always make more money, but you can’t buy more time.

The biggest promise and opportunity with AI tools is being able to reclaim that precious resource.

I’m not fussed about making 6-figures or building teams with AI only. I’m much more invested in getting time back to spend with those close to me and doing more of the human stuff I love at work.

We’re starting to see what people are doing with some of these time gains.

In Oliver Wyman’s AI for business research, they estimate Gen AI could save 300 billion work hours globally each year. I think that would be a wonderful outcome (as long as it doesn’t involve me doing more washing!).

Where AI Is Not Your Friend

I know this might break some hearts, but…AI is not your saviour.

Life is a mix of opportunities and pitfalls.

Research from BCG and Harvard offers an important lesson: generative AI works exceptionally well when used for tasks it can handle. However, beyond that, it’s the wild west.

As always, context is key in decision-making, and tools are constantly improving. This is where I like to appeal to everyone’s common sense. Yet, as I’m often reminded, common sense, it seems, isn’t so common these days.

It’s impossible for me to cover every task across every industry you might encounter.

Instead, here’s a general framework to help you determine when to use generative AI. The summary is simple: AI works well with tasks with pre-defined guidelines and less severe consequences of a f**k up. It should not be relied upon in what I class as ‘mission critical’ matters, aka the human stuff.

Over-reliance on AI is already a significant threat to education, work, and life.

We explored this in a recent edition on the “Hidden Impact of AI on Your Skills”.

In schools, new research has shown generative AI harms students’ learning because they over-rely on these tools, quickly losing key human skills. More alarmingly, we’ve seen the rise of AI companions as therapists and friends among 18–24-year-olds (especially men), replacing vital human connections.

This is why I always emphasise helping people develop the mindset and behaviours to use AI intelligently. Note: I define ‘using AI intelligently’ as understanding the why, what, how, and when of AI applications versus tasks.

Adoption can easily slip into addiction.

How to identify tasks AI can help with

This is the thing we all need help with.

Where can and can’t AI help me?

There’s no clear-cut answer to this. I’d love to give you some fancy 2×2 framework but I don’t believe that will serve you well. Each scenario is context-specific, and generative AI tech is evolving so fast.

I tend to think about my tasks in a macro and micro view.

Your tasks can easily be broken down into sub-tasks (micro). We’ve talked about continuing to invest in your thinking in this era of AI. This is something that requires deep thought and reverse engineering your ideal outcomes.

As an example, I use a little table like this:

It’s not fancy, but it does the job.

We have two macro tasks:

• Presenting insights and actions on the L&D functions performance to senior leaders
• Launching a new internal course

For our first task, my outcome is to deliver a presentation to senior leadership on L&D performance.

So, I break down (in my mind) the micro tasks to reach that, as you see above. I then assign each of those to a column. Note: The first column can be automation without AI.

I don’t use this for every task, only those that I believe, with my current experience of Gen AI, could be an opportunity to work smarter.

What’s key is the AI components are always low to mid-level, and the mission-critical parts are always done by me (the human).

Final thoughts

Knowing how to use AI tools is useful.

But understanding why and when to call upon their power is an advantage.

As we’ve covered, there is no one right way to assess this. The simplest part (imo) is to get clear on what are the uniquely human tasks in your work. Mark these as ‘mission critical’ – so you have zero or very minimal AI assistance.

Your low and mid-level should become clearer with this.

I say this sooo often, but it’s a damn good quote and continues to be relevant in this space:

With great power comes great responsibility

– Uncle Ben (Spiderman’s uncle)

Think wisely about when to wield that power.

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