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Today’s Thoughts ☠️

Hey there 👋,

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.

This is one of the reasons I created the AI For L&D Crash Course.

I wanted something specific for L&D pros to make better decisions with the new era of generative AI tools. That means looking beyond just ‘how’ to use these tools.

So today, we’re exploring the When (and when not) To Use AI side of this conversation.

Get your tea or beverage of choice ready, 🍵.

We've got lots to discuss!

👀 In today’s chat:

• Not falling into the ‘AI is my hammer’ habit
  

• Assessing tasks, not jobs in the age of AI

• Smart frameworks for smart work

(P.S. What do you think workplace L&D teams need to do to drive meaningful value for their business in 2025? Answer this one question survey to share your thoughts, and get exclusive access to the findings this December).

THE BIG THOUGHT
When to Use AI (and When to Avoid It)

I’m noticing a growing trend with one question that’s beginning to concern me.

It reveals that people’s behavioural patterns and decision-making are changing – but not for the better.

That question is: “Which AI tool should I use for x?”

While this might sound harmless, it carries underlying issues. Generative AI is just one tool among many. The current state of ‘wow, look at that’ is blinding people to other (and often better) options that might better suit the task.

We’ve fallen into an odd Swiss army knife dilemma.

Instead of defining a problem and identifying the best tool, we’re throwing AI at various problems to see what sticks. It’s the classic ‘everything looks like a nail when you have a hammer’ conundrum.

Treating AI as a one-stop solution is risky.

Don’t get me wrong. It’s good and useful, but it’s not always the right tool for the job.

We need to go beyond asking why, what, and how, and start asking ourselves when.

To do this, let’s explore some data and real industry use cases to understand when is AI helpful and when could it be harmful to work.

📌 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

• It’s not a hammer

Assess tasks not jobs

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 2x2 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:

Think wisely about when to wield that power.

👀 ICYMI (In case you missed it!)

• The critical component of AI training that most companies overlook

• How To Be a Strategic L&D Leader (And Why It Has Nothing To Do With Learning Design)

Till next time, you stay classy, learning friend!

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