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 

Indulge me for a second and let’s try a little thought experiment.

Imagine your colleagues outside of L&D — managers, frontline staff, sales reps, new hires etc — each had their own always-available, always-supportive AI performance coach. No set-up time, no searching for materials, just instant, intelligent support in the moment they need it.

What would you want that coach to do for each of those colleagues?

• Offer reminders on key tasks?

• Help them prepare for or reflect on a difficult conversation or meeting?

• Serve up step-by-step guidance during their first few weeks in a new role?

This isn’t the stuff of fantasy anymore. AI is becoming sophisticated enough to do all of that — and more. But let's not get carried away.

Your answers to the 'learner need' question matter more than any tech solution. Because performance support is about learner need first and tech delivery second — not the other way around.

That's why the PerformaGo AI tool that we are developing, starts with performer needs, not content or clever tech. It help's you to think through: What do learners struggle with most, post-training? What’s hard for them to remember? What gets skipped when things get busy? What barriers exist in the workplace that might be holding them back or discouraging them from applying their new skills?

Then we make it super-easy for you to design lightweight, AI-powered assistants that:

• Provide authentic and accurate help, advice and reminders

• Reference the specific skill sets and content that an individual learner needs to complete a specific task 

• Prompt the learner to plan, complete or reflect, based on real-world application and challenges

This is about providing real-time learning and support that lives in the workplace — not inside the LMS.

So, what would you want an AI coach to help your learners do? 

Interested in finding out more about how PerformaGo could help your learners achieve more successful workplace application? Register your interest at pacificblue.ai -  help us shape the tool and get early access.

Many of us in L&D still hesitate about AI. The reasons vary enormously. For example:

• “It’s not good enough yet.”
• “It’s going to steal my job.”
• “I’m not technical enough.”

Last week’s post explored the flaw in that last reason. We saw that most of us in L&D already hold a hidden set of technical skills — more than most of us give ourselves credit for.

This week, I want to dig into that idea a bit more, with a personal story and a reminder of another skill that many of us underestimate.

You Don’t Need to Know the Detail to Benefit

I’ve never been particularly strong in or had much of an interest in maths. Yet here I am, building a software tool. Even more surprising, as part of deepening my understanding of how AI works, I became quite fascinated by the elegant mathematics that sits behind GPTs.

Luckily, I don’t need to do the maths to be in awe of it — or to benefit from what it enables. I can use GPTs without knowing the detail of semantic maps, tokens, or probability models. I simply need to know how to make the best use of what that ‘behind the scenes’ math can produce.

And I think that highlights an interesting and important parallel for us in L&D. We don’t have to understand every last underlying detail of the subject matter we turn into courses for our learners.

Instead, we need to filter out irrelevant complexity and translate what remains into a learning experience that is relevant, authentic, and usable. This is the hidden L&D skill that I'd like to focus on here.

The Hidden Skill: Turning Complexity Into Clarity

Plenty of SMEs know their subject in depth. A few are even instinctively good instructional designers. But a key understanding that separates a good instructional designer from many an SME is perspective. We design for the learner, not for the content. We can take a step back in order to

• see it from the learner’s point of view
• strip away the content clutter
• sequence ideas clearly and logically, and
• build authentic practice out of all that.

This, I believe, is one of our greatest professional assets.

From Learning Design to Performance Support

Not everyone reading this will have designed performance support materials or content before. If you haven’t, the good news is that the skills you use to design an effective course are very similar to the ones you’ll need to design useful performance support in the workplace.

And here’s the even better news: performance support is the perfect way to combine those existing skills with the power of AI.

Just as we filter complexity into clarity for learners during the course design process, we can use AI to help us produce, usable workplace performance support scaffolding.

Put those two forces together — L&D’s eye for learner relevance and AI’s knack for simplification — and you have a winning formula for extending learning into the flow of work.

Harnessing AI without Being a Techie

You don’t need to be a techie to make AI work in L&D. What you need is a recognition of the skills you have already — and a willingness to apply them in new contexts.

The maths that underpins GPTs may remain out of reach for me, but the elegance of what it makes possible is not. The same is true for L&D. Our job isn’t to master the inner workings of AI, but to harness its power in ways that help people learn, perform, and succeed at work.

If you’d like to see the personal story that sparked this reflection, take a look at this week’s post from my PerformaGo diary: In Awe of the Math I’ll Never Do 

As a customer, it’s a generally a pleasant experience going to a restaurant.

We order the food we want. It arrives as specified and we get to enjoy it. And if anything is not quite as we’d like it, we can always have a word with the staff.

But what about if you are the chef and his team. Always busy. Always harassed. Always churning out the same predictable product. Frequently dealing with picky customers who don’t like this or that about what you produce.

In theory, the chef should be very much in control of his or her universe. In practice it may not be like that.

Does that chef’s kitchen sound like your L&D function? If not, I’m delighted to hear it. No need to read on.

If yes, then you are currently facing a very unattractive future. Because the orders are only going to increase in volume and the ‘customers’ are only going to get pickier and more demanding.

If your ‘customers’ are telling you what they want and how they want it, fundamentally they don’t value you. In their heads, you don't know anything much about the best approach to learning for their particular need. So they formulate their own plans and just present them to you for implementation.

There could be various reasons for finding yourself in a situation like this.

Perhaps you've inherited it from a predecessor. Perhaps this is all you've ever known and you just assume this is the way it's done. Perhaps you'd love to do things differently but can't see a way to turn things around.

Whatever the causes, this is a terrible situation to find yourself in as part of a learning and development function. And it doesn't have to be this way. The ship can be turned around.

If you are serious about turning your L&D department into the trusted and respected part of your organisation it deserves to be, take a look at our on-demand webinar on this topic: How to amplify learning: the journey from order-taker to trusted expert.

It’s easy to feel uncomfortable or distracted when yet another new technology comes along.

Fresh concepts, unfamiliar vocabulary, and endless hype can leave us feeling overwhelmed or even anxious. Neuroscientists call this an amygdala response — the part of our brain that processes threats quickly kicks in, making us want to shut down or avoid the source of discomfort.

That reaction is natural. But here’s the problem: it just reinforces a sense of inadequacy. A sense that we’re “behind” or “not technical enough” to keep up. And in the new world of AI unfolding before us that feeling can become overwhelming.

I went to a fairly tech-focused, hands-on event run by Amazon Web Services in Manchester last week. And trust me I had a couple of my own, amygdala-induced, 'shall I just cut and run, now?" moments. But I stuck it out. And I'm glad that I did because the more I persevered, the more I realised something really important

Most of us in L&D are far more tech-savvy than we give ourselves credit for.

Take a step back for a minute and think about all the different tech tools in your orbit and the different ways you might use them:

• Instructional tools → You’ve already learned to use Articulate Storyline, Rise, Captivate, or similar platforms. That’s logic, sequencing, and (if you really get into Storyline in particular) using variables.

• LMS administration → Uploading SCORM files, managing enrolments, troubleshooting access. That’s systems thinking and workflow design.

• Digital collaboration tools → Microsoft Teams, SharePoint, Slack, Miro. That’s user experience awareness and adoption strategy.

• Content authoring → Video editing, podcasting, slide design. That’s multimedia fluency and interface design.

These aren’t minor skills. They are the very foundations of working with any new software, regardless of its unfamiliar associated concepts or jargon.

Now granted, not everyone is going to be expert in every one of those areas or those tools. But joining the dots between what you know already and what's emerging is probably not as difficult as you think. The “new” world of AI doesn’t replace what you already know — it connects to it.

As well as my recent experiences last week, I’ve been reminded of this in my wider journey. If you've been reading any of my recent posts on this blog or over on my  new diary blog, you'll know that I'm on a journey to build a performance support app, specifically for L&D.

Moving into software development felt like jumping in at the deep end. But as I began exploring no-code tools and AI, I kept noticing familiar patterns: workflows that echoed the decision-making aspects of Storyline variables and interfaces that weren’t so different from the tools I’ve used for years.

We’re already carrying around a whole bunch of tech-related skills around with us. It's almost like discovering a hidden skillset that we never realised we had.

If you’d like to find out about how I worked through the excitement (and the fear) of stepping into a more tech-focused space, I share some of the behind-the-scenes story in this week’s Diary post.

One of the things that we cover in the e-learning modules in our impact and instructional design training is the difference between user interface design (UID) and learner interface design. I first came across this distinction courtesy of all-round e-learning genius Michael Allen.

Without getting into the weeds, it’s the difference between what you might call the basic high-level global interface settings you provide to your learners – navigation being the most obvious and probably the best example.

As with all things UID, the aim is to make this as simple, consistent and intuitive as possible. However, Allen makes the point that within that global UID it’s likely you’ll have an entirely separate set of design principles that relate specifically to the task-focused practice activities that you are building into your e-learning.

The interesting point here, is that your learner interface design (LID) might not be consistent with your user interface design. In fact, in extreme cases, it might effectively ‘go against’ your UID.

In other words, for the purposes of making your practice activity authentic and a reflection of the real-world situation it is aiming to replicate, if that real-world activity is messy and complicated to complete, that may need to be reflected in the design of the activity. Meaning that from a learner perspective, it might not be simple, consistent and intuitive.

Diving into that in a bit more detail is probably a good topic for another day. Today I really do want to focus on simple, consistent and intuitive UID. Because endless unnecessary clicks can be a quiet killer of learner engagement.

Every extra click — the extra “Next” button, the redundant confirmation screen, the maze-like menu — acts as micro-barrier. It slows learners down, interrupts flow, and chips away at motivation. It may not seem like much, but multiplied across an entire course or module, the hidden cost is high: lost learners, missed outcomes, and frustrated managers.

E-Learning is a particularly problematic delivery medium in this respect; but the hidden cost of too many clicks or actions is not restricted to e-learning.

Performance support provided in a digital/electronic format is no different. If we want support tools to be genuinely used in the flow of work, they need to feel like they fit perfectly in that flow. That means eliminating friction at every step. Every second counts when someone is trying to solve a problem or apply a skill on the job.

The principle is simple: the easier the journey to the required solution, the more likely the learner is to stick with it — and the more likely performance will actually improve.

If you’ve been following my posts recently, you’ll know that I’m very focused on supporting performance in the flow of work just at the moment. So well-contextualised performance support tools built around well-defined and contextualised moments of need are very much top of mind at the moment.

That’s why, as I continue building performance support-focused tools like PerformaGo, one of my guiding principles is simplicity. Because every unnecessary click isn’t just wasted effort — it’s a lost opportunity for impact.

If you are interested in reading a little behind the scenes story of my obsession with UID, you can read the latest entry from my PerformaGo diary here.

I’ve long been interested in technology and its potential to make life easier and more efficient. However, I’m definitely not a pointy-headed tech nerd. Tech jargon user-unfriendly pieces of software drive me insane.

One of my particular pet peeves is the ability of the tech world to make something simple and every day sound much more complicated than it actually is.

One of my favourite toe-curlers is ‘boot up’ or ‘reboot’. For the ordinary folk in the room, ‘start’, ‘switch on’ or ‘restart’ seems like a perfectly reasonable and well-understood alternative.

And it’s amazing how, just when you thought you had heard all the weird and wonderful terms that the tech world can come up with, you discover a new one.

I honestly don’t know if this has come into use recently with the arrival of AI or if it has been around for a while and I’d just never come across it before; but a newly discovered tech term for me is ‘ingest’ or ‘ingestion’.

Now the standard usage, of course, would be in a biological or medical context.

But in the best traditions of tech jargon, this slightly obscure verb/noun combination, which is definitely not used much in everyday speech, has become the standard way to describe how a file or doc has been uploaded and chunked by a GPT’s knowledge base.

Granted, it is a term that most people will more or less understand; and, ‘yes’ there is some logic behind its use. In a biological/medical context, of course, it describes the full consumption/digestion of something into something else; and this is clearly the intent behind the tech usage.

But really? Couldn’t we just say, the files have been uploaded and chunked?

Of course, the annoying thing about all of this jargon is that once it’s established, if you are working in a tech context in which it is used, at the very least you have to make the effort to understand it; and (shudder) eventually you’ll almost certainly find yourself using it.

In short, sometimes you have no choice but to learn to speak the tech ‘lingo.’

If you have read some of my recent posts, you’ll know that I’m in the early stages of building an AI-powered app for L&D called PerformaGo. I’m also keeping a diary-like blog which is documenting the journey ‘behind the scenes.’

I’ve divided the diary into 4 broad sections, one of which is called, ‘Learning to speak API’. It’s all about understanding, unpacking and explaining the tech ‘lingo’ and concepts so they make sense to me and to ordinary readers like you.

So, why not follow along if you are interested. And if you’ve got any particular tech-speak pet-peeves, I’d love to hear from you.

This isn’t the first time that I’ve made reference to what I call Storyline’s ‘secret weapon’. A Storyline ‘advanced’ feature that gets buried away in the background because it’s not very ‘sexy’ nor particularly easy to market. Yet learning to use it can transform the e-learning courses you build.

In case you are wondering? I’m talking about Storyline variables.

However, the purpose of today’s post is not to write extensively about Storyline variables. (If you are interested, I have written about Storyline variables here).

What interests me today is the fact that lots of software/technologies often have some kind of ‘secret weapon’. In other words, a hidden something that a majority of users don’t know about, which is incredibly powerful or useful but somehow never gets the attention it deserves.

Typically, the hidden something is conceptually abstract or technically a bit complex; so, it takes some care and effort to explain it clearly and easily. And it often takes some care and effort by software users to really reap the benefits of it.

Which usually leaves it friendless. Tucked away in an obscure corner of the interface by UX and UI designers. Shunned by the marketers (just not ‘sexy’ enough). Glossed over by the technical writers and technology evangelists.

And you can understand why. In the push to get a piece of software or a technology widely adopted as quickly as possible, the most popular and easiest to use features are, inevitably, going to get the most attention.

And in lots of ways, that is a good, user-centred approach. After all, if the software or technology in question isn’t obviously solving some kind of problem or making it easier or possible to do something that was previously difficult or impossible to do, what’s the point?

But this also misses an important point. Hiding that more difficult-to-explain feature probably results in inferior user output. Returning to the example I opened with, this is absolutely true in the case of Storyline variables. It’s not that you need to use them in every single course that you create. But never using them at all will absolutely reduce the creative potential and the effectiveness of your instructional design.

So, why am I obsessing about all this, at the moment? Well, you may already know that I’m working on a new software tool for L&D called PerformaGo. And the AI technology that this software is designed to help L&D folk like yourself unleash, definitely has its own buried treasures. (Something I’ve been writing about here). In essence, it’s about how you provide specific knowledge to your custom GPT so that you get reliable, accurate output when it’s being used by a learner.

Which means that this particular ‘buried treasure’ is going to play a very significant role in making the PerformaGo tool a success. The question of how that specific knowledge is packaged up and accessed can’t be glossed over, in the hope that some users will find it and work out how to use it. It needs to be front and centre.

And that will be a challenge. Because in some respects this element is a bit abstract. It will definitely require some careful thought around how it is presented and explained to users within the software interface itself and in any related ‘help’ content or software onboarding.

But once you consider the benefits for L&D folk of getting this ‘buried treasure’ front and centre, the challenge of achieving that goal seems small by comparison.

If any of this interests you, or you like the idea of becoming an early adopter or pilot user of PerformaGo or you would just like to find out some more about the tool, why not register your interest.

And you can follow a more ‘behind-the-scenes’ take on the journey to build the PerformaGo software tool in my online diary.