Few ideas in Learning & Development have been as popular (or as persistent) as learning styles. Most of us are familiar with the basic idea: people have different styles of learning (‘visual’ or ‘auditory’ are couple of examples). Identify someone’s style, match the training to it, and ‘hey presto’ learning will be more effective.

You can understand the attraction. It’s an idea that seems deeply intuitive. That feels totally learner-centred. That is, in fact, irresistible to many learners and L&D professionals.

Which goes a long way to explaining why, for years, learning styles have been taught, assessed, and embedded into L&D practice. But there’s a big problem with this idea. Because when you look closely at the evidence, the idea simply doesn’t hold up.

  

Let’s be precise about what’s being challenged

The specific claim that research has examined and failed to support, is what psychologists call the meshing hypothesis. The idea that learners learn better when instruction is matched to their preferred learning style (e.g., visual learners taught visually, auditory learners taught verbally).

Now it’s important to note, that this is different from saying:

• people don’t have preferences (they do),
• or that variety is bad (it isn’t),
• or that accessibility doesn’t matter (it absolutely does).

But back to that central question: does matching instruction to a learner’s declared style improve learning outcomes?

Fans of the learning styles idea are going to be disappointed. Because after more than two decades of research, the answer is still: no compelling evidence.

 

What the research actually says

One of the most widely cited reviews was published by Harold Pashler and colleagues in 2008. They set out very clearly what evidence would be required to support learning styles — and then examined the available studies.

Their conclusion was blunt: there was no credible evidence showing that matching instruction to learning styles improves learning.

Other reviews have reached the same conclusion. A very major UK review by Coffield et al. examined dozens of learning style models and found serious issues with reliability, validity, and practical usefulness.

As a result, learning styles are now commonly described in the research literature as a “neuromyth” — a belief about the brain or learning that sounds scientific but isn’t supported by robust evidence.

So much so that for years there has even been an open “Learning Styles Challenge”, offering a cash prize to anyone who can demonstrate real-world benefits from designing instruction around learning styles. To date, the prize remains unclaimed.

  

Why the idea refuses to die

If the evidence is so weak, why does the idea persist? Partly because learning styles play into something everyone is a fan of: personalisation. They give learners language to describe themselves and give designers a sense that they are tailoring learning rather than mass-producing it.

They’re also easy to explain, easy to assess, and easy to sell. But ease and intuitiveness are not the same thing as effectiveness.

 

What actually works better

Ironically, many people who believe in learning styles are already doing things that do work.

Research consistently shows that learning is more effective when:

• the mode of instruction matches the nature of the content (for example, diagrams for spatial information or audio for pronunciation),
• learners get practice, not just content exposure,
• retrieval and feedback are built into practice
• complexity is managed carefully,
• learners are supported in applying learning in context.

In other words, the question isn’t, “What type of learner are you?” It’s, “What kind of learning does this task require?” That shift — from labelling learners to analysing tasks — is what helps L&D become more effective.

 

Preferences still matter, BUt...

None of this means learner preferences should be ignored. Preferences matter for all sorts of reasons like:

• motivation
• engagement,
• inclusion and accessibility.

But preference is not the same as learning effectiveness. Giving learners choice in how they engage with content can be valuable. Designing everything around a fixed learning style is not.

 

A more helpful way to think about it?

If we really want to personalise learning, there are better levers to pull than learning styles:

• design for the task, not the learning style label
• design for application and performance, not just exposure to or consumption of content
• design for context, not abstraction

Learning styles promised personalisation. The evidence suggests we can do better — by focusing less on categories and labels and more on what people actually need to do more effectively in the workplace.

If you want to make something sound serious and inaccessible, here’s a simple trick: talk about neuroscience.

I’m serious. Just roll the word around in your head for a minute. Neuroscience.

See what I mean? It feels scary. Impersonal. Something conducted in hermetically sealed labs where nobody smiles and every sentence (uttered by scientists in crisply-laundered white coats) must contain at least three syllables.

It feels like the kind of thing that can’t possibly relate to regular people or everyday jobs — let alone the practical world of Learning & Development.

(And just to be clear, I’m poking fun at the discipline of neuroscience here, not at the very real cognitive differences that many people live with.)

But then you open John Medina’s book Brain Rules, and suddenly your perception of neuroscience is completely flipped. His writing is energetic, engaging, and fun. Medina is that rare person that can bring even the driest-sounding of subjects or topics to life. And if you haven’t ever read his book, I’d strongly encourage you to.

One of the ideas that stayed with me long after reading Brain Rules was his discussion of music. Not in a “Playing Mozart to babies makes them genius-level smart” way, but in a grounded, evidence-based way.

And more recently, I happened across some research from MindLab, a UK-based cognitive science group, that reminded me just how powerful (and misunderstood) music can be when it comes to helping us concentrate.

So, if you've ever wondered why some playlists help get you into the flow while others knock you off track, this article explores the brain-friendly principles behind that difference.

So, let’s dive into understanding what music does — and doesn’t — do when you’re trying to get work done.

First: Music does not make you smarter

Medina is unambiguous about this.

• No, Mozart won’t raise your IQ.
• No, playing classical music to babies doesn’t give them a head start.
• No, music is not a cognitive shortcut.

So, if you’ve been blaming your lack of genius on a childhood without Chopin… sorry.

What music can do is change your mental state

But what Medina does explore is how music can:

• alter your mood and your energy and stress levels
• shift your focus by making tasks feel more enjoyable
• increase alertness if you’re drifting
• create a rhythm that helps with repetitive tasks

In other words, music doesn’t make you smarter but it certainly puts you in the right frame of mind to think more smartly.

From the point of view of cognitive load, this is important. After, all, anything that reduces stress or stabilises your frame of mind frees up more bandwidth for meaningful work.

The MindLab research adds a useful layer

The MindLab research reminded me of all this. It ran a series of task-based studies involving music. It’s not deep neuroscience — but it is practical psychology, and it very much aligns with Medina’s findings.

Here’s what they found:

1. Different tasks benefit from different types of music

• Classical is good for accuracy-based tasks and detail-heavy work
• Ambient/electronic music is better for repetitive tasks, data entry
• Dance music boosts speed, but sometimes reduces accuracy
• Pop is okay for conceptual work (if lyrics aren’t distracting)

2. Music reduces stress, which improves performance

Participants consistently showed lower cortisol when listening to calming music. If your stress levels are lower, you attention levels are generally higher. Better attention levels generally lead to better results or output.

3. Lyrics are the enemy of language-heavy tasks

If your task requires reading, writing, or generating language…music with lyrics will probably slow you down. (This is why your “productive Spotify playlist” works brilliantly—until it doesn’t.)

A simple, practical takeaway

Most people in L&D will have moments in their working day when they have important jobs that just need to get done. Defining some objectives, designing a course outline, working up an e-learning interaction, to name but a few. So, can music help?

Well, I think the real takeaway here isn’t about genre, although clearly that can be a factor. It’s more about fit. Making sure there’s a good fit between:

• task type
• emotional state
• personal musical preference

And really, that’s Medina in a nutshell: know how your brain works, and design for it. And sometimes, working out how your brain works can begin with something as simple as recognising why a particular playlist helps you slip into flow — and why another playlist derails your concentration completely.

A couple of final thoughts

Reading Medina’s work makes you realise that learning about neuroscience doesn’t have to be intimidating or abstract. Quite the opposite.

And in case you’re wondering? Yes, I wrote this while listening to music. Did it make me smarter? Sadly, not. But it did make the words you’ve just been reading flow more readily.

 

• Brain Rules: 12 Principles for Surviving and Thriving at Work, Home and School
• John J.Medina
• Pear Press 2008
• Brain rules.net

In last week’s post, I wrote about the jagged edge — the uneven frontier between what AI is exceptionally good at and where it is still surprisingly weak.

That insight came into focus for me while standing on the patio of the Stahl House in the Hollywood Hills, looking down at the sharp, uneven ridges, set against the flat, orderly grid of Los Angeles below.

That contrast — smooth here, jagged there — feels like the perfect metaphor for AI right now. Not evenly distributed. Not predictable. Not consistent.

The takeaway from last week’s post was pretty clear: success with AI isn’t about mastering the technology. It’s about recognising where it shines and where it stumbles.

And that raises the next logical question.

 

What Does the Jagged Edge Look Like for L&D?

This is the part that matters. The practical, day-to-day reality of using AI in real L&D work.

Because if you work in L&D, you are very likely navigating that jagged edge — whether through generative content requests, experiments with course outlines, or colleagues asking, “Can we use this AI thing to help with…?”

Understanding where AI is strong (and where it is unreliable) is becoming a genuinely useful professional skill. So, here are some thoughts on what this looks like as we head towards the end of 2025

 

Where AI Really Helps L&D (The “Smooth” Side)

This is where the ground is smooth and firm and flat. Where AI consistently behaves well and adds value without fuss.

Summarising long, dense content

Policies, procedures, reports, transcripts — AI is excellent at turning them into:

• short summaries
• digestible bullet points
• suggested learning objectives
• concise recaps for learners

This isn’t just speculation. If you’ve used AI for this kind of task already you’ll know it’s pretty reliable.

 

Cleaning up messy SME input

We all love our SMEs; but we also know that a massive, unstructured ‘brain dump’ is often their default mode. However, if you give AI:

• rambling emails
• half-formed notes
• slides covered in dense bullet points

it will make a good first pass at organising that content more clearly and logically. The output won’t be perfect. It will still need some extra human brain power to get it over the finish line; but it will get you to the end of the process much more quickly.

 

Drafting outlines, ideas and examples

AI is consistently strong at:

• drafting module outlines
• drafting simple scenarios
• creating examples and non-examples
• rewriting content at different reading levels

It certainly doesn’t replace creativity and original thinking; but it can really help unblock those creative juices when you are struggling to come up with good ideas.

 

Re-framing content for clarity

“Explain this as if…”

“Give me examples from…”

“Rewrite this in plain English.”

This is an area of real strength. In the right circumstances, AI can be just as great at giving you the last 10% (refining and polishing) as it at getting you 90% of the way there.

 

Where AI Still Struggles (The Jagged Edge)

These are the uneven ridges — the places where footing is uncertain and relying on AI is definitely risky.

Anything requiring organisational context

AI doesn’t know:

• your policies
• your culture
• the shortcuts your learners actually use
• the unofficial steps that matter
• the messy real-world constraints

Its answers can sound right in theory, but can be very wrong in practical terms.

 

Emotional nuance, judgement or interpersonal dynamics

Handling conflict. Managing a difficult conversation. AI can mimic empathy but it often misses:

• tone
• boundaries
• what not to say

Use it in these kinds of contexts with extreme care.

 

Multi-step reasoning

AI still struggles with:

• multi-stage logic
• conditional pathways
• decisions that depend on context
• keeping its own reasoning consistent

It can excel at steps 1–3 then completely misfire on step 4.

 

Highly specialised expertise

In specialised domains like medicine, engineering, law and compliance, plausible-sounding nonsense is especially dangerous.

 

Explaining the “why” behind a rule

AI is good at restating a rule. Much weaker at explaining the reasoning behind it. Learners need the “why” for authentic understanding — and AI often can’t supply it.

 

Navigating the Terrain

As noted in last week’s post, the jagged edge isn’t a barrier — it’s a map. In an L&D context, it really helps us understand where:

• AI can accelerate design work, and
• humans must remain firmly in the loop.

The real work right now is not avoiding the jagged edge but learning to navigate along it with confidence.

A few weeks ago, during a trip to the US, I was lucky enough to find myself standing on the patio of the Stahl House in the Hollywood Hills — a glass-and-steel architectural icon perched high above Los Angeles.

The view from the patio is breath-taking; and as a lover of mid-century minimalist architecture, I found the house equally stunning.

From that patio vantage point, the other striking aspect was the contrast between the hills immediately below the patio and the distant cityscape. Way below, the city stretched out in a perfect grid: flat, predictable, orderly. But right in front of me, the landscape fell away in uneven ridges — sharp, fractured, unpredictable.

I was reminded of the sharp, uneven edges of those hills the other day when I came across the term jagged edge - to describe what the world of AI looks like at the moment.

So, let’s explore the meaning of that term and what it might mean for L&D.

Often, we talk about AI as though it’s spreading evenly across everything; but it isn’t. It’s advancing in fits and starts — powerful in one moment, incredibly clumsy in the next. Researchers have named this unevenness the jagged edge of AI.

Understanding the Jagged Edge

You may well have experienced this jagged edge first hand, when AI is strong and accurate in some areas but weak and unreliable in others. 

It can draft a pretty good course outline in seconds. It can generate accurate code that works. Yet still produce nonsense when asked to evaluate a moral dilemma.

This unevenness exists because AI doesn’t understand; it predicts. It excels where data is abundant and patterns are clear — structured writing, number crunching, summarising, and retrieval. It falls short where context, empathy or tacit knowledge contribute significantly to the outcome.

 

Evidence from the Research

This metaphor for uneven progress is backed by evidence. In 2023, researchers from Harvard Business School and Boston Consulting Group ran a field experiment exploring how AI affected knowledge workers. They called their study Navigating the Jagged Technological Frontier.

They found that workers using AI completed 12% more tasks and worked 25% faster when those tasks fell within AI’s frontier — areas where AI already performed well.

But when tasks sat just beyond that frontier, the results flipped: performance declined.
In other words, AI’s progress isn’t smooth or universal. It’s jagged. Full of peaks and troughs that shift depending on the nature of the work.

The Human–AI Division of Labour

At first glance, that jaggedness can seem like a flaw. But if you think of it more like a map or a bunch of helpful road signs — it usefully highlights where humans and machines each add the most value.

• AI excels at automating structure: generating first drafts, summarising information, categorising, and identifying patterns.
• We excel at interpreting complexity: spotting anomalies, understanding tone, making ethical calls, and connecting dots in ambiguous situations.

When we understand where the jagged edge runs, we can design smarter systems and workflows that let each party play to its strengths.

What This Means for L&D

For L&D professionals, that insight is particularly useful. It tells us the real opportunity isn’t just using AI generically. It’s about recognising where AI’s edge lies in our own jobs — what can be safely automated, and what still needs human input and decision-making.

For example:

• A learning designer can use AI to structure a skills framework (below the edge), but must still apply contextual awareness to fit it to their own organisation (beyond the edge).
• A subject matter expert might use AI to generate examples or case studies (below the edge), but they’ll need to refine and validate accuracy, richness and complexity of those examples (beyond the edge).

It’s going to take us all a while to get used to this ‘new frontier’; and we’ll need to be ready for the jagged edge to shift as AI technology evolves. But recognising and navigating the jagged edge confidently, within our own job roles, could massively transform our productivity and effectiveness.

Reference:
Dell’Acqua, F., Kalliamvakou, E., Kirov, S., Ransbotham, S., & Rock, D. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper 24-013.

 

I’ve written a lot over the last few months about the significance of shifting from a training-focused paradigm to a performance-support focused one. It’s a big shift. Understandably, plenty of people feel like this is me saying, ‘do away with your training events.’

Why training still matters — but needs to evolve

Definitely not the case. But it does raise a big question that people often ask:

"What would training look like if it were designed with performance support in mind from the start?"

Let’s dig into answering that, to see how you might refine what you do during a training event, when designing for a more performance-support focused paradigm.

It’s easy to see performance support as a nice ‘add-on’. Something to highlight to the learners towards the end of the event, as a rather perfunctory ‘transition to the workplace’ segment. I confess, I’ve run my fair share of such segments over the years.

Unfortunately, this approach will pretty much guarantee that your performance support solution gets little use or has low perceived value.

 

From event thinking to performance thinking

So, if you are providing performance support tools or materials, then they need to be introduced and used as an integral part of the training – not as an afterthought.

The question remains: 'How to do that?'

In our impact and instructional design programme, we introduce attendees to a very simple, very flexible but nevertheless very robust design flow that works very well during in-person and live online events.

It’s a three part design flow. It starts with presenting to learners, moves to providing structured practice, and finished with much less structured extended practice. The idea being that as you go through this sequence, you are helping the learners’ capability and self-direction to increase

From a learning design point of view, this is about providing multiple points of practice that is highly interactivity. Learners gradually become more autonomous and increasingly confident in their ability to apply what they are learning.

Ideally, by the time you reach the extended practice phase of a session, the trainer will be able to fully step back, observe the learners and only provide feedback (where needed) during a group de-brief after the activity.

The good news is that from a performance support perspective, this is a very effective design flow, too. The difference comes in how you think about and design your practice activities.

 

What a performance-support-focused training session looks like

So, from a learning design perspective, a good flow of practice activities will help the learners to process and embed what they have learnt; the only ‘external’ assistance comes from the trainer’s input during the structured practice stage and any feedback or de-brief after the extended practice stage.

From a performance support perspective, the structured and extended practice activity stages of the flow are the perfect moments to enhance that trainer input and support by introducing and phasing in use of the performance support tools or materials.

This familiarises learners with the tools and how to use them; and positions them as the natural post-training ‘go-to’ which will help to get the job done.

 

Bringing performance support into the classroom

For example, imagine a training event that is focused on helping learners to plan and prepare annual performance reviews with their team members.

The course design analysis phase highlighted the benefit of providing a simple performance support tool to guide learners through the planning and preparation.

Having learners use the planning tool as part of any practice activity makes the practice much more authentic; and, ensures the learners see the tool as part and parcel of how to apply what they are learning.

 

Training as the gateway, not the goal

All this highlights clearly that selecting and designing performance support tools or materials must be an integral part of your existing impact and instructional design process. Not an afterthought. The training is vital but only the first stage of a journey to the really important goal: improved workplace outcomes.

Achieving this level of performance support integration requires L&D to get close to the business. To have a good understanding of what desired workplace performance looks like. To be clear about how knowledge and skills covered during a training event are applied in the learners’ workflow, once they are back on the job.

If that sounds a bit challenging, it shouldn't. In reality, performance-support-focused training isn’t a different discipline — it’s simply the next logical step in doing what great L&D has always aimed to do: help people do their jobs more effectively.

Context, they say, is everything. Very true in many areas of life, especially true of instructional design. And just as significant when it comes to performance support. Without carefully considering context (and its close relative integration) you are likely to come unstuck.

I was reminded of this just recently when I re-discovered a wonderful example from Allison Rossett. Her example was written in the 90s, meaning the original tech references are out of date now. Below, you’ll find my (minor) re-working of the example to include current tech references.

But before recounting the example, I want to add a bit of context of my own.

Why context matters 

Ever since the arrival of cognitive learning theories, we’ve recognised the importance of context in learning. Viewed through a purely instructional design lens, understanding how learners actually apply their learning in the workplace, will avoid teaching knowledge and skills in the abstract. A clear understanding of the specific ways in which knowledge and skills are being applied, helps you craft a tailored learning experience that closely reflects the workplace reality of the learners.

When thinking about supporting learners after a learning event, understanding that aspect of context is equally important. After all, the content of the support materials will need to be equally well contextualised and tailored.

But you will also want to focus on actual physical surroundings. Literally, where are they when they need that support. At a desk? At the top of an electricity pylon? In a noisy, crowded space?

That’s a much narrower and much more specific aspect of context. But it’s vital because it helps determine what a usable performance support tool needs to look like. 

A job-aid printed on a piece of paper won’t be very effective at the top of a pylon. Audio support won’t be much help in a noisy, crowded workspace. Granted, these are obvious, simplified examples. But you get the idea.

Consider integration for EVEN greater effectiveness

Within that aspect of context, you also need to consider integration. What do I mean by that? Essentially, it’s about how embedded the support is within the task. Making sure that learners can get to the support they need as quickly and easily as possible, while still taking into account the type of performance support tool that will make most sense in that context.

In some cases, you only need to think about these questions for a minute or two. The answer will stare you in the face. Other times, it’s a bit more nuanced and you might need to work through a few options and possibilities before you reach the best result. Other times, you may end up having to compromise a little bit.

And this is why I like Rossett’s example, so much. It takes an example that everyone understands and readily walks us through many of the considerations connected to context and integration. So, here’s my slightly updated version of her example…

Context and integration example

Imagine that you have to drive to an event on the other side of the town where you live. Your brother works on that side of town and knows it well; but you rarely have cause to go there and only a very limited experience of driving around there.

How might you go about finding your way there?

Look at the city map you keep in your home office. 
Not very integrated and only tailored in the sense that it’s a map of the place you need to drive in.

Go to Google Maps and have it map out the route. Print out the results to have with you in the car.
There is significant tailoring here. You get the exact route that you need. The support is integrated. But dangerously so. Glancing at the print out of the directions while driving is not recommended!

Ask your brother for directions
Tailored. And depending on your memory and the accuracy of his directions, this could work quite well. But not hugely integrated.

Pull out a city map that you keep in your glove box. Look at this before your journey and keep consulting it as you drive.
As with the map in the home office, not very tailored. As with the printed directions, integrated but dangerously so.

Use your sat nav
Lots of tailoring (and adapting). Deeply integrated into the task.

Of course, in this example most people will see immediately the benefits of the sat nav over all other options. This is one of those ‘staring you in the face’ decisions. 

However, just to be a little bit contrarian, I continue to be a sat nav refusenik. I could share multiple examples where I have sat as a passenger and watched people make the dumbest of navigation decisions because they have switched off their navigation brain and rely slavishly on a sat nav master. Another post for another day, I think!

But in conclusion, whatever decisions you make regarding context and integration, the important point to remember is that the more embedded, intuitive and tailored you can make that support solution, the more your learners are likely to see value in it and engage with over and over.

 

You can read a more personal view of my journey to create an AI-driven performance support tool in my weekly PerformaGo Diary.

 

If you are an end user of AI it all feels pretty simple. Ask your question or make your request. Get an answer or response. Keep going until you are done.

But what about if you need to start engaging with AI at a slightly more strategic/technical level. “It’s all a bit too complicated for me,” is not an untypical (or unreasonable) response. After all, once you start talking about APIs, prompts, Python or LLMs you can understand why someone’s eyes might glaze over and their brain might shut down.

To the uninitiated, it does start to sound like a bunch of tech gobbledygook dreamt up by a fairly unfriendly bunch of Martians!

(By the way if you are interested in dipping your toe into the more technical aspects of AI, my goal is to unpack the jargon and concepts in an L&D-friendly manner in my PerformaGo Diary and the associated Making Learning Stick blog)

But the good news in all this is that you don’t need to become a part-time programmer to get more than end-user value from AI. Because a lot of what’s going on behind the curtain with AI is as much about design logic as it is about programming. And design logic is something that most people in L&D are already very familiar and comfortable with.

 

It’s About Design, Not Code

So, what do I mean by design logic and how is it different from something like coding? Well, coding is about telling a system how to execute something, step-by-step. The detailed ‘how to’ instructions that run under the hood. Whereas design logic is about the why and the what.

So, viewed from a strategic or purpose-driven perspective, what makes AI really useful is the ability of those deploying it in a work-based environment to clearly define its intent (the why) and the outcomes you want from it (the what).

Which, of course, is not that different from thinking purposefully about a piece of work-based training.

 

What This Means for L&D

So, if we move beyond the anxiety many people feel about coding and tech speak, what we actually discover is that AI design and learning design share the same DNA.

Both are about:

• Creating flows and structures so things make sense.
• Filtering out unnecessary complexity.
• Anticipating what people will need, when they’ll need it, and how they’ll best engage with it.

In reality, you are already thinking in systems, flows, and interactions — exactly the same kind of mental models that you need to use when thinking about deploying AI.

So, working more strategically and purposefully with AI isn’t about shifting from instructional designer to techie expert. It’s about applying your existing learning design skills into a slightly different arena.

You don’t need to learn a single line of Python code to work effectively with AI. But thinking like a learning designer will definitely help you get better results from it.

 

AI Doesn’t Replace Design Thinking — It Rewards It

So, AI isn’t something L&D needs to fear or surrender to. It’s something we can shape using our existing design skills and thinking. Because when you strip away the code, AI’s power depends on the same things that make great learning work: clear purpose, sound structure, and empathy for the learner

That’s why the future of L&D doesn’t belong to the coders — it belongs to the designers.  People who can take what they already know about how people learn and apply it to how intelligent systems perform.

Because design thinking isn’t just part of L&D’s DNA. It’s also our bridge to the future.

 

Curious how this post took shape?
This post was inspired by a story I shared in my Diary blog — “From Rodeo Drive to AI: The Three Languages Every Custom GPT Needs.”

Spoiler alert. It is a bit more technical in content. But it’s the tale of a California trip that unexpectedly helped me understand how several bits of tech work together to make AI more effective.

You can read that story here

Ask anyone outside of L&D what comes to mind when they think of “learning,” and the answer is almost always the same: classrooms, courses, qualifications.

That’s not surprising. From the time we’re four or five years old, we spend hours of our lives in an education system where the ultimate prize is being good at school. Success is measured in grades, certificates, and diplomas. Society reinforces the message: the more successful you are at completing formal learning, the more valuable you are.

So, it’s no wonder that when business leaders run into a performance problem, their first instinct is to ask L&D for training. Courses feel like the answer because courses are what we’ve all been conditioned to believe in.


The Blind Spot This Creates
The problem is, formal learning isn’t always the lever that moves performance. In fact, often it’s not even close.

Sometimes what’s needed isn’t weeks of training. Sometimes it’s something as simple as a single page of A4 paper — a job aid, a checklist, a quick reference — that gives people exactly what they need, when they need it.

But here’s the counterintuitive part: because these solutions look so simple, they often get dismissed. People inevitably think, “How could something that small make a real difference?”

This bias towards formal learning is a big blind spot in how we approach workplace learning. It’s why L&D so often gets trapped in the order-taking cycle — asked to churn out courses because they’re seen as the “serious” answer, even when simpler, leaner performance support would have more impact.


Why Simplicity Works
The irony is that simplicity is exactly what makes performance support powerful:

•    A well-designed checklist can significantly reduces errors.
•    A short job aid can save hours of wasted time.
•    A contextual nudge can shift behaviour more effectively than any course module.

None of these examples look like the formal learning solutions we’ve been trained to overvalue. But they work; and often, they work better.


Shifting Our Mindset in L&D
If we  want to increase learning impact, we need to challenge the cultural over-perception of formal learning. That doesn’t mean courses don’t matter. It means we stop treating them as the default solution.

It means educating our stakeholders that effectiveness isn’t about how long or formal a learning solution is, but about how well it supports performance. Sometimes that’s a course. Sometimes it’s workplace support. And sometimes it’s a combination of both.


A Closing Thought...
The world has taught us to equate learning with classrooms, exams, and certificates. But in the workplace, those aren’t always what move the needle.

Performance improves when people have the right support at the right time. And sometimes, that support looks a lot less like school — and a lot more like a single sheet of paper.

We’ve all been there. “We need a course on…” or “Can you put together a workshop about…” And before you know it, you’re scoping out slides, activities, and maybe even booking rooms.

It’s the classic “training order-taking” scenario. The problem is, fulfilling training orders doesn’t always lead to better performance. Sometimes it just leads to more training.

The Limits of the Order-Taker Role

When we accept training requests at face value, we miss the bigger question: what’s the real problem we’re trying to solve?

Is performance lagging because people don’t know how to do something? Or is it because the process is broken, the tools are clunky, or expectations are unclear? In many cases, training isn’t the answer at all.

That’s the trap of being an order-taker. It turns us into a busy L&D function, but it doesn’t necessarily make us impactful.

The Shift: From Training to Performance

To increase impact, L&D needs to move from being an order-taker to becoming a performance enabler.

Sometimes, that means asking harder questions up front:

• What’s really getting in the way of performance?
• Is this a skills gap, a motivation gap, or an environmental issue?
• If training is part of the answer, what else needs to be in place for it to work?

It also means widening our toolkit. A course might sometimes be the right solution — but often it’s just one piece of a broader picture.

What Performance Enablement Looks Like

Performance enablement is about making it easier for people to succeed in their work. That might look like:

• A job aid that reduces reliance on memory.
• A checklist that ensures key steps aren’t missed.
• A guided conversation that helps managers coach effectively.
• A performance support tool that provides answers in the flow of work.

These may not look as impressive as a full-blown course, but their impact can be huge. They get used in the moment of need. They reduce errors. They increase confidence. And they show the business that L&D is directly connected to performance outcomes.

Why This Matters Now

People don’t always have time to attend courses, however well designed. They need solutions that fit into their flow of work, not outside it.

That’s why the order-taker mindset feels increasingly out of step. The future belongs to L&D teams who can enable performance: diagnosing real needs, designing for usability, and delivering support that works in the moment.

How This Shapes PerformaGo

This shift is also shaping the design of the AI-powered tool I’m currently working on, called PerformaGo. From the ground up, it’s being built around the principle of performance enablement.

Instead of just asking, “What training can we deliver?”, it encourages us to consider, “What support will make performance easier?”

The goal isn’t to replace courses. It’s to give those of us in L&D a way to extend learning into performance — so the business gets the impact it needs, and learners get the support they want.

A Closing Thought

The order-taker model has kept many in L&D busy for many decades. But if we want to stay relevant, we can’t just take orders. We need to enable performance, too.

That’s the shift PerformaGo is designed to support. If you would like to stay connected and receive regular updates about what we are doing, you can register your interest.

If you prefer a more personal, behind-the-scenes take on all this, check out The PerformaGo Diary.

Looking into the L&D world from the outside, it’s tempting to think that more is better. More slides, more content, more detail. After all, if learners have all the information, they’ll surely be more successful, won’t they?

But anyone who’s worked in L&D knows that is rarely true. It's information or content overwhelm that can discourage learners from implement learning successfully; too much information rather than not enough of it.

Typically, they don’t need more information, but they could surely benefit from more relevant and better-structured information. And that’s where a simple but immensely powerful design principle comes in. The principle of chunking with relevance.

 

My Early Introduction: Information Mapping

I was first introduced to the principle of chunking with relevance through a methodology called Information Mapping. It’s a structured way of presenting information that relies heavily on breaking content down into smaller, clearly defined units.

With good reason. There’s plenty of evidence to suggest that we all (not just learners) process and understand information much more easily when it’s organised into meaningful, focused “chunks.”

Even outside of the framework of a formal methodology like Information Mapping, chunking with relevance is something all of us in L&D should be aiming to apply.

Fundamentally, chunking with relevance helps shift us from a mindset of, ‘what do I need to tell them’, to a more learner-focused approach that considers two things: ‘what do they need to know’ and ‘what will be manageable for them to process’.

 

 Why Chunking Works: The Cognitive View

The roots of chunking with relevance lie in cognitive psychology. We don’t have unlimited working memory. So, when information is presented in large, unstructured blocks, our brains struggle to process it.

Chunking with relevance reduces that cognitive load by grouping information into smaller, meaningful units. This makes it easier for learners to:

• Understand new material as it’s introduced.
• See the relationships between pieces of content.
• Retain and recall knowledge more successfully.

Chunking with relevance doesn’t guarantee perfect recall, but it does make initial comprehension much, much easier.

 

A Principle Shared with AI

Interestingly, chunking isn’t just good for us. It’s also vital for AI systems like GPTs.

For example, when you create a knowledge base for a custom GPT, dumping in a 100-page PDF won’t produce great results.

The GPT will work better when the information is broken down into smaller chunks. Each chunk gives the AI clearer context, helping it generate more accurate and relevant responses.

In other words: both humans and machines process information better when it’s structured thoughtfully. It’s a reminder that chunking is a universal design principle, not just a quirk of instructional theory.

 

 Why This Matters for L&D Now

Learners often face information overload at every turn — in their jobs, in their inboxes, and even in the learning materials we create. If we want our learning interventions to be effective, we can’t add to that overload.

Applying chunking with relevance is one of the simplest ways to reduce the cognitive burden. It helps learners see the signal in the noise. It makes content usable rather than intimidating. And crucially, it demonstrates the value that L&D brings: not by producing more information, but by structuring it in a way that is meaningful and relevant.

And if you’re curious to find out a bit more about the importance of chunking for AI, take a look at this week’s diary post