August 27, 2026
AI’s Real Business Knowledge Gap
Last week, I spoke to a leader who proudly told me their business had "an AI strategy".
Promising start, I thought.
Then came the reveal. They had bought a few tools, run a couple of demos, and asked their teams to "start using AI where helpful".
Which, translated from Executive to English, means: we have purchased a gym membership and are surprised we do not look like athletes yet.
That is the problem.
Businesses have spent the last two years treating AI adoption like a software rollout, when in reality it is a people capability challenge wearing a technical costume.
And according to TechRadar's reporting on this exact issue, the biggest knowledge gap exposed by AI is not technical at all. It is whether businesses have equipped their people to work with it properly. Source: TechRadar, 'AI has exposed the biggest knowledge gap in business. Hint: it isn't technical'.
I think that is dead right.
Because most companies do not have an AI problem. They have a leadership clarity, skills translation, and organisational confidence problem.
AI is not failing. Rollouts are.
Let us call it as it is.
AI is not underwhelming because the tools are weak. The tools are getting absurdly capable, absurdly fast. What is weak, in many businesses, is the bridge between ambition and execution.
Leaders say things like:
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We need to embed AI across the business
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We want teams to become more productive
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We need to stay competitive
Lovely. Sounds great on a slide.
But when you scratch the surface, basic questions go unanswered:
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Which teams should use AI, and for what?
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What does good usage look like?
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Where does human judgement still matter most?
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What risks are acceptable, and which are not?
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Who owns capability-building?
If those questions are foggy, people default to one of two behaviours.
They either use AI badly, or avoid it completely.
Neither is a strategy.
The real gap is confidence, not code
Here is the myth that needs binning.
You do not need everyone in your business to become a machine learning engineer. You need them to understand where AI helps, where it lies, where it saves time, and where it absolutely should not be left unsupervised like a toddler with a marker pen.
The biggest gap is not that people cannot build models.
It is that many employees, and plenty of leaders too, do not yet know how to:
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ask better questions of AI tools
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judge output quality
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spot hallucinations or weak reasoning
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apply AI in role-specific workflows
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use it without breaking trust, governance, or common sense
That is a knowledge gap, yes. But more specifically, it is a commercial capability gap.
Because if your people cannot confidently translate AI into everyday work, your fancy investment becomes office theatre.
Expensive office theatre, at that.
Why leaders keep missing it
I have seen this film before, just with different branding.
Cloud. Data transformation. Cyber maturity. Digital transformation. Every wave arrives with a burst of executive excitement, a procurement flurry, and then a quiet realisation that buying the thing was the easy bit.
The hard bit is behavioural change.
Leaders miss this for a few reasons.
They mistake access for adoption
Giving people an AI tool is not the same as changing how work gets done.
That is like handing someone a piano and expecting jazz by Friday.
They overestimate how comfortable teams feel
A lot of employees are nodding in meetings while privately wondering whether AI is useful, risky, overhyped, or coming for their job.
That uncertainty kills experimentation.
They underinvest in middle management
This one matters a lot.
Senior leaders set ambition. Frontline teams do the work. But middle managers are the conversion layer. If they do not understand how to guide AI usage in practical terms, the whole thing stalls in PowerPoint purgatory.
They hire for yesterday's org chart
Many businesses are still hiring as if AI is a side project rather than a core operating capability.
That means too few people who can connect strategy, operations, data, risk, and team enablement.
In other words, they are hunting unicorn specialists while missing the translators, builders, and pragmatic leaders who make change stick.
What good looks like
The firms getting this right are not necessarily the noisiest.
They are usually doing four things well.
They start with workflows, not hype
They ask: where is time wasted, where are decisions slow, where is quality inconsistent, and where can AI genuinely improve outcomes?
That is a much better question than: how do we use AI everywhere?
They train by role
A generic AI lunch-and-learn is fine as an opener. It is not enough.
Engineers, analysts, cyber teams, operations leads, people teams, and executives all need different guidance, different use cases, and different red lines.
Role-based enablement beats broad evangelism every time.
They normalise experimentation with guardrails
People need permission to test, question, and iterate. But they also need clear boundaries.
That includes:
Freedom without guardrails is chaos. Guardrails without freedom is bureaucracy in a shiny new jacket.
They hire people who can connect dots
This is where recruitment becomes strategic, not administrative.
The winners are not just hiring technical AI talent. They are hiring people who can translate capability into business outcomes.
That might mean:
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data leaders who can operationalise insight
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infrastructure and cloud professionals who can scale tooling safely
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cyber experts who understand AI governance risk
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product and BI talent who can embed AI into real decision-making
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people leaders who can drive adoption without causing cultural whiplash
In short, they hire adults.
A simple framework for closing the gap
If I were advising a leadership team tomorrow morning, I would keep it brutally simple.
The AIR Model: Alignment, Integration, Readiness.
Alignment
Get clear on where AI matters commercially.
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What business problems are you solving?
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What outcomes matter most?
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What should humans still own decisively?
Integration
Embed AI into actual workflows.
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Which processes improve with AI?
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What does adoption look like by team?
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Where are the governance checkpoints?
Readiness
Equip people to use it well.
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Who needs training, and of what kind?
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Which managers need support first?
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Do you have the right talent to lead this properly?
If one of those three is weak, your AI plans will wobble.
If all three are weak, you do not have a transformation. You have a budget line.
Questions worth asking internally
If you are serious about making AI work, ask your leadership team these questions:
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Can each function explain how AI improves its work in practical terms?
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Do managers know how to coach teams on safe, effective AI use?
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Are we measuring productivity theatre or actual business impact?
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Have we hired enough translators between technical capability and operational reality?
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Would our employees say they feel confident using AI, or merely pressured to?
The answers will tell you more than any vendor demo ever could.
This is now a talent issue
Here is my slightly cheeky but sincere view.
The companies that win with AI will not necessarily be the ones with the flashiest tools. They will be the ones with the clearest thinking, the best-enabled teams, and the smartest hiring.
Because every major technology shift eventually becomes a people issue.
And people issues are never solved by pretending they are purely technical.
If your AI strategy depends on employees magically becoming fluent, confident, and commercially sharp without investment, support, or better hiring, that is not optimism. That is wishful thinking dressed as leadership.
AI has exposed a knowledge gap, yes. But it has also exposed a leadership gap.
The good news is that both are fixable.
With clearer priorities. Better capability-building. Smarter hiring. Less hype.
And perhaps fewer meetings where someone says "we just need to leverage AI" as if that means anything on its own.
The businesses that sort this now will pull away fast.
The ones that do not will keep wondering why all that investment still feels strangely theoretical.
And nobody wants to explain that to the board with a straight face.
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