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The Diffusion of AI
Why adoption is not the
same as impact
​
AI is spreading quickly across organisations.
Pilots multiply, tools are rolled out, productivity claims circulate, boards ask for AI strategies, and leaders feel pressure to keep up. From the outside, this looks like progress.
In practice, the impact is uneven, fragile, and often disappointing. Not because AI lacks potential — but because diffusion is being mistaken for transformation.
How the confusion forms
The problem is not enthusiasm, it is abstraction.
Most AI conversations focus on what the technology can do rather than how organisations actually change. Adoption is treated as a technical exercise: deploy the tool, train users, measure outputs.
But diffusion is not a technical event. It is a social, organisational, and behavioural process. When that is ignored, AI is introduced into systems that are not ready to absorb it — sometimes with weak results, sometimes with negative ones.
Diffusion is not neutral
AI never enters a blank slate.
It diffuses into existing ways of deciding, trusting, managing risk, learning, and relating to authority. In some environments it sharpens clarity and coordination; in others it amplifies confusion, overconfidence, and narrative drift.
The difference is not the tool, it is the conditions it lands in.
Why early gains mislead
Early deployments often look promising.
Tasks are automated, responses are faster and costs appear to fall. These gains can be real — but they are mostly local and first-order.
What follows is more complex. Judgment quietly shifts from people to outputs. Confidence rises faster than understanding. Coordination relies more on plausibility than verification. Accountability becomes harder to locate.
By the time these effects are visible, sub-optimal use of AI is already embedded.
The judgement problem
AI is very good at producing coherent answers, that is also the risk.
When fluency is mistaken for correctness, people stop checking reality as carefully. Disagreement fades under volume and speed. Plausible narratives travel faster than conditions.
This is not a failure of intelligence, it is a failure of judgment under new circumstances.
AI changes how judgment is exercised, not just how work is done.
Why diffusion stalls
real value
Many organisations report the same pattern.
AI tools are available, usage is uneven, benefits plateau and tension grows between optimism and results.
This happens when AI is layered onto work without changing how decisions are questioned, how uncertainty is handled, how learning is shared, or how responsibility is retained.
Without those shifts, AI simply accelerates existing habits — good or bad.
The opportunity leaders often miss
The real opportunity in AI is not automation, it is augmented judgment. AI can surface patterns, test assumptions, widen options, and reduce cognitive load — but only if humans remain responsible for testing outputs against reality, holding context and consequences, noticing when coherence replaces truth, and deciding what should be done, not just what can be done.
That requires capability, not just tools.
What to look out for
Signals that AI diffusion is weakening rather than strengthening performance:
• Faster answers, slower agreement
• Higher confidence, fewer challenges
• Decisions justified by outputs rather than conditions
• Responsibility diffused across systems
• Learning framed as optimisation, not understanding
Individually, these seem manageable, together, they undermine impact.
A different way to think about diffusion
The diffusion of AI is not a race, it is a test of whether organisations can integrate powerful tools without losing contact with reality, judgment, and trust.
Those that can will see real gains, those that can’t will see accelerating activity — and fragile outcomes. AI will spread regardless.
The question is whether understanding and responsibility spread with it.
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