The trouble with AI is that we're using it to do the wrong things faster

The first great achievement of generative AI in marketing was to make the blank page disappear.

Need an article? Ask a model.

Need an email sequence? Generate one.

Need twenty advertising variations? A few instructions will do.

Tasks that once took hours can now take minutes.

This is genuinely useful. But it raises an awkward question.

What if producing marketing material was never the industry's most important problem?

The abundance problem

For much of marketing's history, content was expensive to produce and distribute.

Writing required time. Design required specialist skills. Publishing required infrastructure. Reaching an audience required access to channels and budgets.

These constraints imposed a degree of discipline, even if not always deliberately.

AI is removing many of them.

A business can now produce more articles than its customers could reasonably read, more email variations than anyone could meaningfully test and more campaign ideas than its marketing team could evaluate.

The cost of production is falling.

The cost of attention is not.

Indeed, as more organisations exploit cheap production, gaining meaningful attention may become harder.

This is an uncomfortable possibility for an industry that has often treated volume as a sign of progress.

If everyone can produce more content, producing more content is unlikely to remain much of a competitive advantage.

The difference between content and communication

A technically competent article is not necessarily an effective piece of marketing.

It may contain accurate information, follow search-engine conventions and sound perfectly professional.

But if it doesn't address a meaningful customer problem, influence a decision or contribute something distinctive, its commercial value may be negligible.

The same applies to outbound email.

An AI system might produce a beautifully personalised message that refers to a prospect's company, role and recent announcement.

Yet if the underlying proposition is irrelevant, the personalisation merely makes the interruption more elaborate.

The failure is not linguistic. It is strategic.

The model has successfully completed the task it was given.

The task was simply the wrong one.

Where intelligence actually belongs

The more important application of AI begins before the first word is written.

Which organisations might have a genuine requirement for this service?

What circumstances would create that requirement?

Which people are involved in the buying decision?

What would they need to understand before considering a change?

What information has the company already published?

Where are the gaps?

And, perhaps most importantly, what should the company do next?

These questions require research, interpretation and judgement.

They involve connecting evidence from different sources, recognising uncertainty and understanding commercial context.

This is a more demanding application of AI than generating another article.

It is also potentially much more valuable.

Consider two approaches to a campaign.

One creates a hundred messages and uses AI to personalise them.

The other researches a hundred organisations, identifies which have plausible reasons to buy, distinguishes the relevant buying situations and develops communications accordingly.

Both use AI.

But one is principally a production system. The other is beginning to behave like a commercial intelligence system.

The distinction matters.

Why humans don't disappear

AI systems can process information at remarkable speed, but speed is not the same as judgement.

A buying signal may be ambiguous. A source may be unreliable. An attractive opportunity may not fit the organisation's strategic priorities. A technically accurate claim may still be inappropriate for a particular audience.

Experienced marketers understand that much of their work involves deciding what not to do.

They consider reputation, timing, competitive context, brand positioning and the subtle difference between being relevant and appearing opportunistic.

These are not reasons to reject AI.

They are reasons to give it a more intelligent role.

The objective should be to use AI to expand the evidence available to human decision-makers and to execute appropriately within agreed boundaries.

The result is not human marketing with a faster typing assistant.

It is a different division of labour.

From abundance to discernment

The irony is that AI may make restraint one of marketing's most valuable capabilities.

When content becomes cheap, selecting what deserves to be said becomes more important.

When outreach becomes easy, knowing when not to contact someone becomes more valuable.

When every activity can be automated, deciding which activities are worth undertaking becomes the real source of advantage.

The future of marketing may therefore involve considerably more artificial intelligence and considerably less marketing noise.

That would be progress worth pursuing.

Not because we have learnt to do everything faster.

But because we have finally learnt to distinguish what is worth doing at all.