The Adaptation Gap: Why AI Skills Alone Aren’t Enough
AI is changing people’s work faster than organisations are developing people’s capacity to change with it.
I wrote that sentence some time ago.
This week, some new research put rather a lot of numbers behind it.
McKinsey Global Institute estimates that around 11 million US workers in declining occupations may need to move into different occupations by 2035 as AI, automation and other structural changes reshape work.
But that’s not the number I find most interesting.
More than 70% of workers could require some level of reinvention, even if they don’t change occupation at all.
And look at what employers are already asking for.
Since 2022, demand for AI fluency has increased elevenfold.
Demand for adaptability has increased fivefold.
Demand for resilience, curiosity and willingness to learn has tripled.
McKinsey describes these as skills that prepare people for continuous change. (McKinsey & Company)
I think that last bit is the important one.
Because we’ve spent an enormous amount of time talking about the skills gap.
I’m increasingly interested in the adaptation gap.
What if the skill you learn next isn’t the last one you’ll need?
The traditional response to technological change goes something like this:
Work changes.
We identify the new skills people need.
We train them.
Problem solved.
Except what happens when the work changes again?
And again?
And again?
AI isn’t simply introducing another piece of workplace technology.
It is beginning to change tasks, roles, workflows, organisational structures and even where human value sits.
McKinsey estimates that only around one in seven workers who need to move into growing occupations will have a relatively direct pathway.
For many others, the route will involve significant retraining, new credentials, different work and potentially different pay. (McKinsey & Company)
So teaching people today’s AI tools is necessary.
But it isn’t enough.
We need to develop people who can keep moving.
That’s a very different human capability.
This is where Human Frontier Thinking™ comes in
Human Frontier Thinking™ is the discipline of continually expanding how humans think, learn and act at the edge of change, discovering capabilities and possibilities we did not previously know existed.
And that word continually matters.
We aren’t trying to prepare people for one future.
We can’t.
We’re trying to develop people capable of meeting futures we can’t yet see.
So what can we actually do now?
Get better at noticing change before it becomes unavoidable
The first Human Frontier Capacity is Edge Sensing.
Most organisations respond to change once its consequences become obvious.
The role has disappeared.
The competitor has moved.
The customer has changed.
The skill is obsolete.
By then, adaptation becomes urgent and frightening.
Instead, develop the habit of looking at the edge.
What’s beginning to change in your profession?
Which parts of your expertise are becoming abundant?
What are customers beginning to value differently?
What can AI suddenly do that it couldn’t do six months ago?
What are you doing today that probably won’t require you in three years?
Not to predict the future perfectly.
To give yourself more time to move.
Practise Courageous Curiosity®
Expertise is valuable.
But expertise can also become a trap when the world changes around it.
We become very good at saying:
I know how this works.
Courageous Curiosity® adds another question:
What if something has changed?
Follow developments outside your immediate profession.
Talk to people who see the world differently.
Experiment with unfamiliar technology.
Question the assumptions underneath the way your work is currently done.
Ask what you would do if you were starting your profession from scratch today.
Curiosity isn’t a nice-to-have personality trait in an uncertain world.
It creates options.
Don’t outsource all the thinking
AI can now give us answers faster than most of us can formulate the question.
Use that extraordinary capability.
But don’t confuse having access to intelligence with developing your own.
Sometimes think before you ask AI.
Form a view.
Make the judgement.
Create the first idea.
Then use AI to challenge you.
Ask what you’ve missed.
Ask for contrary evidence.
Ask it to attack your argument.
Ask what would make you wrong.
The objective isn’t to prove that the human is cleverer than the machine.
It’s to make sure the human continues developing too.
Develop judgement, not just knowledge
As information and competent output become abundant, judgement becomes more valuable.
What matters?
What is noise?
Which evidence should I trust?
What are the consequences?
When should I challenge the machine?
What decision am I prepared to own?
Those aren’t skills we develop by reading about judgement.
We develop them by exercising it.
Which means organisations need to be very careful about which decisions they remove from people in the pursuit of efficiency.
Human-in-the-loop isn’t enough.
We need human-judgement-in-the-loop where the consequences require it.
Redesign development when AI removes the old route
This may be particularly important for young people.
AI is very good at absorbing routine work.
Excellent.
But some routine work was also developmental work.
The first draft.
Basic research.
Initial analysis.
Reviewing dozens of cases until patterns become visible.
The answer isn’t to preserve inefficient work for nostalgia’s sake.
It’s to ask:
What capability was that work developing?
If the capability still matters, redesign the developmental experience.
Simulation.
Shadowing.
Stretch assignments.
Case work.
Deliberate practice.
Human-AI challenge exercises.
Real decisions with appropriate supervision.
Give people another way to get the reps.
Don’t protect the task. Protect the capability the task was developing.
Stop treating adaptability as something employees either have or don’t
This matters particularly to leaders.
Telling people to “be more adaptable” while constantly changing priorities, overloading them with work and giving them no time to learn is not developing adaptability.
It’s exhausting people.
Human capability develops in conditions.
Do people have permission to experiment?
Can they question how things are done?
Is learning part of work or something they’re expected to squeeze in afterwards?
Can they admit they don’t know?
Are managers developing judgement or simply checking output?
Do people have enough space to think?
The Brave New Leader® isn’t simply managing change.
They’re creating the conditions in which people become more capable of navigating it.
Become much more deliberate about Intelligence Allocation
One of the questions I think every organisation should now be asking is:
How do we allocate the work and the authority between human and artificial intelligence?
That’s what I mean by Intelligence Allocation.
Not simply:
Can AI do this?
But:
Should it?
Should a human do it?
Should they do it together?
Who needs to understand it?
Who makes the consequential judgement?
Who has authority to act?
Who remains accountable?
And what human capability will be strengthened or weakened by that allocation?
There are two tests.
Performance today.
What allocation produces the best outcome now?
And:
Capability tomorrow.
What allocation ensures that we continue developing the understanding, judgement, agency and expertise we’ll need next?
An organisation can make an apparently brilliant efficiency decision today and quietly create a human capability problem for five years’ time.
Measure something other than adoption
How many people have licences?
How many are active users?
How many hours have we saved?
How much output has increased?
Useful.
But they don’t tell us what is happening to the humans.
I’d start asking different questions too.
Are people making better decisions?
Is judgement improving?
Are they more confident dealing with unfamiliar problems?
Are they learning faster?
Can they challenge AI effectively?
Are people moving successfully into new roles?
Is AI-released capacity being used for learning, innovation, relationships and better thinking, or simply filled with more work?
And perhaps the simplest question of all:
Are our people becoming more capable as our organisation becomes more AI-enabled?
We need to stop preparing people for a future that will stay still
It won’t.
McKinsey’s modelling suggests the US economy may actually create more jobs than automation replaces over the next decade.
That’s important.
This isn’t inevitably a story about mass unemployment.
It’s a story about movement.
Jobs changing.
Tasks changing.
Expertise changing.
Career paths changing.
And people repeatedly having to learn their way into what comes next. (McKinsey & Company)
That’s why I don’t think our biggest challenge is teaching everyone the right AI skills.
Those skills will change too.
Our bigger challenge is developing humans capable of continuing to think, learn, judge and act when what they know is no longer quite enough.
That’s Human Frontier Thinking™.
AI will keep developing.
We need to become much more deliberate about developing the humans alongside it.
Because perhaps the real future-ready skill isn’t knowing what to do next.
It’s knowing how to find your way when you don’t.
Grow Bold. Think Big. Stay Human.
Carol Glover, The Maverick Maker®