Upskilling is Currently Built for an Imaginary Employee

Upskilling has become one of the defining business priorities of our time. With AI reshaping roles faster than hiring cycles can keep pace, and the World Economic Forum projecting that nearly half of all workers' core skills will be disrupted by 2027, organizations are right to treat workforce development as urgent. Indeed, US companies alone now spend close to $100bn annually on employee training. The intent is evidently there: the impact, though, is not.

The Signal You Should Not Ignore

The data on employee satisfaction with upskilling tells an interesting story. Research consistently shows that the relevance of training to the individual learner (“is this actually helping me?”) is the number one driver of whether that training works. When managers don't understand their people's development needs, training satisfaction drops dramatically — in one study, to just 11%. When programs feel generic, disconnected from how people actually think and work, employees go through the motions – and investment in learning ends up not paying off.

Executives don't need to conclude that upskilling is necessarily failing wholesale. But, they should be alert to the real possibility that for a meaningful portion of their workforce, a substantial share of that investment may be missing the mark — and that the reasons why are hiding in plain sight.

The Overlooked Variable: How People Are Wired

Most upskilling programs are built around a base assumption of sameness: i.e. that the people going through them learn in roughly the same way. Same format, same pace, same delivery, same assessment: it should all work for most people, right? Design once, deploy at scale, job done… Unfortunately, this is where the problem starts.

As we say every day at Uptimize: everybody is wired differently, and people are cognitively diverse. They process information differently, focus differently, retain and retrieve knowledge through different mechanisms, and engage with material in ways that reflect fundamentally different wiring. Within this human universe of different brains, perhaps 20% of people are in some way neurodivergent - ADHDers, dyslexic people, autistic people and others with brain wiring that differs significantly from societal expectations of “normal”. But cognitive variation extends well beyond formal diagnosis. Even within what we might call the neurotypical population, the range of how people learn, absorb, and apply new skills is wide – and still poorly understood and under-considered.

A standardized upskilling program, if built for an imaginary average learner, will fit reasonably well for some. For a significant minority, though, it will create unnecessary friction, mask capability, and quietly disengage (and fail to develop further) some of the most original thinkers on the team.

Why Is This Still Happening?

Part of the answer is simply awareness. Cognitive diversity as a concept - the recognition that different neurological profiles bring different strengths and different learning needs - remains underrepresented in most L&D conversations, beyond a basic nod to the tenets of UDL (“Universal Design for Learning”). Many learning designers have never been trained to think in terms of cognitive difference to the degree that is needed to really ensure learning actually works across the board. Many HR leaders are only beginning to connect neurodiversity to workforce development, having previously focused on hiring and retention. This is a knowledge gap: it is closable.

But there is a deeper issue here too, beyond a lack of focus on cognitive difference per se. The current wave of upskilling (and particularly AI upskilling) is being underpinned and driven by a technocratic logic that prizes efficiency, standardization, and measurable outcomes. Skills frameworks are mapped, curricula are deployed, completion rates are tracked – in an approach that can feel pleasingly but deceptively neutral, objective, even meritocratic: the same opportunity extended to everyone.

Such an approach, though, isn't meritocratic if the delivery assumes everyone processes and demonstrates learning the same way. A program that measures competence through formats that disadvantage certain cognitive profiles isn't a level playing field: in practice, it works more like a filter. And in an era when the quality of human judgment, creativity, and problem-solving is exactly what AI cannot replicate, that is a costly error.

What's at Stake

The stakes here are both commercial, and human. Regarding the former, employees who find themselves consistently unable to engage with training the way it's designed often internalize the problem as personal failure rather than a design flaw. Many won't say anything: indeed, research suggests the majority of neurodivergent employees don't disclose, in part because they've learned that workplaces aren't built with them in mind.  As a result, talent goes underutilized, and potential goes unrealized. This causes costs to employers that are very real, though they may fail to effectively connect them back to learning design: disengagement, attrition, and the persistent underperformance of people who, in a different environment, would thrive.

Meanwhile, organizations that build cognitively-inclusive approaches to learning don't just serve their neurodivergent employees better - they build better training for everyone. The principles that make learning accessible to neurodivergent team members, such as varied formats, clear structure, flexible pacing, or multiple ways to demonstrate mastery, are the same principles that make learning more effective across the board. Thus, considering this kind of thing helps everybody.

The fix doesn't require rebuilding every program from scratch. It starts with a shift in assumption: from "we offer this to everyone" to "we've designed this for how people actually learn." It means auditing existing programs for cognitive accessibility with the same rigor applied to other forms of inclusion. It means giving managers the language and frameworks to understand how their people learn differently, not just what they need to learn. It also means recognizing that in a skills economy, the quality of how organizations develop human capability matters just as much as the content of what they're trying to teach.

Companies that invest $100bn in upskilling and never ask the question "does this work for how our people are actually wired?" are leaving a significant part of that investment on the table. The ones that do ask the question - and act on the answer - will find that unlocking cognitive diversity in learning is an exceptionally high return move.

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