When Expertise Stops Practising

The communications industry may be outsourcing its judgement.

Not intentionally. Not maliciously. And certainly not without reason.

Artificial intelligence has arrived with remarkable speed and remarkable utility. Few professions have adopted it as enthusiastically as communications. New workflows, new efficiencies, new possibilities. Tasks that once required hours can now be completed in minutes. Drafts appear instantly. Analyses arrive on demand. Content production scales almost without friction.

The transformation is real.

Yet the most significant consequence of AI in communications may have little to do with productivity. It may have more to do with what happens when a profession begins to automate the very processes through which expertise is developed.

For all the attention devoted to what AI can produce, there has been far less discussion about what professionals stop doing when they rely on it.

And that question matters.

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The Value Was Never the Text

Communications has always produced texts.

Reports, speeches, campaigns, articles, presentations, strategies.

But texts have never been the profession's primary value.

Its value lies elsewhere.

The best communicators are rarely those who write the most. They are those who notice what others overlook. They identify tensions before they become conflicts. They formulate hypotheses when others are still collecting observations. They recognise shifts in public sentiment, organisational culture, or political mood before those shifts become visible to everyone else.

In other words, the profession has always depended on judgement.

The text is often merely the visible output of a much less visible process: interpretation.

This distinction has become increasingly important in the age of generative AI because language models are exceptionally good at producing language. What they do not produce is judgement.

They generate plausible responses. They synthesise patterns. They recreate structures that statistically resemble expertise.

Often impressively so.

But plausibility and judgement are not the same thing.

One emerges from probability. The other emerges from experience, context, responsibility and reflection.

The difference is easy to overlook because the outputs frequently sound similar.

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The Friction That Creates Expertise

Most professions contain forms of productive friction.

Lawyers learn through difficult cases. Architects learn through revisions. Journalists learn through reporting. Leaders learn through decisions that carry consequences.

Communications has traditionally worked in much the same way.

People become better by wrestling with ambiguity. By writing weak first drafts. By discovering that their first argument was not their strongest argument. By receiving criticism. By misreading situations and gradually learning why.

The process is often inefficient.

It is also where professional judgement comes from.

Many of the qualities organisations value most–discernment, timing, intuition, perspective–are not acquired through information alone. They emerge through repeated encounters with uncertainty.

This is precisely where AI introduces a new tension.

The technology removes friction.

That is part of its appeal.

Yet when friction disappears entirely, something else may disappear with it.

The struggle to formulate a thought is often part of forming the thought itself.

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The Apprenticeship Problem

The implications are particularly significant for younger professionals.

Most fields rely on apprenticeship structures, whether formal or informal. New entrants perform simpler tasks, receive feedback, develop instincts, and gradually acquire responsibility.

The process is not always elegant, but it creates a professional ecosystem capable of renewing itself.

In communications, this has traditionally meant drafting documents, conducting research, preparing analyses and producing work that is often revised heavily by more experienced colleagues.

The objective was never merely the output.

The objective was the development of judgement.

If AI increasingly performs these tasks, an uncomfortable question emerges: how will future professionals acquire the capabilities that previous generations developed through practice?

A junior employee can now produce competent-looking work remarkably quickly.

What remains less clear is whether competence of appearance translates into competence of understanding.

The distinction may not become visible immediately.

It often takes years before judgement reveals itself–or its absence.

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From Originality to Consensus

There is another consequence.

Large language models are trained on existing patterns. They learn from enormous quantities of text, absorbing conventions, structures and assumptions.

This gives them extraordinary generative power.

It also creates a tendency towards consensus.

The outputs are often balanced, reasonable and persuasive. They sound familiar because they are built from familiar elements.

The result is a subtle form of convergence.

Across organisations, industries and institutions, communication increasingly begins to resemble itself. Similar formulations. Similar strategic language. Similar narratives about transformation, authenticity, purpose and engagement.

None of it is necessarily wrong.

But originality rarely emerges from consensus.

A profession dedicated to helping organisations understand difference, context and meaning may gradually find itself reproducing the same patterns at ever greater speed.

The irony is difficult to miss.

The more powerful our tools for generating communication become, the greater the importance of the one capability they cannot generate themselves: independent judgement.

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The Question Beneath the Technology

The discussion about AI often focuses on capabilities.

What can the technology do?

How much faster can it work?

How many tasks can it automate?

These are important questions.

But for professions built on interpretation rather than production, another question may be more important.

What capacities do we risk neglecting when the technology works exactly as intended?

The issue is not whether AI belongs in communications. It clearly does.

The issue is whether efficiency becomes the dominant lens through which the profession understands its own value.

Because if communications becomes primarily a matter of producing text, AI will inevitably become central.

If communications remains a matter of judgement, perspective and interpretation, AI becomes something else: a powerful tool in the hands of people who have already learned how to think.

That distinction may shape not only the future of the profession.

It may determine whether the profession continues to produce the very expertise on which its future depends.