But some analysts will be replaced by analysts who know how to work with AI.

Let’s say that again. Slowly.

Eric Weber has a great line: data jobs aren’t dying, they’re splitting.

In other words, analysts are not disappearing. But the job is clearly splitting into two different tracks.

The first one is the analyst who uses AI to do the good old data work faster:

  • write SQL
  • build dashboards
  • clean data
  • stitch pipelines together
  • generate “insights” like: “Revenue grew in April because there were more purchases.” Thank you, Cap!

This work still matters. But this part of the job will keep getting cheaper. AI can do execution faster, calmer, and without the dramatic “I’m not in the right headspace today”.

The second one is the analyst who understands what data work is actually worth doing:

  • what should we measure?
  • why are we measuring it?
  • what business question are we trying to answer?
  • where is this metric lying to us?
  • how reliable is this conclusion?
  • what decision will the team make after seeing this?

That’s the part becoming more valuable.

And this is exactly what I try to do as a fractional Head of Marketing Analytics.

Not just “build another report”.

But help the team understand:

  • which questions actually matter for growth
  • which metrics affect decisions, and which ones just look nice
  • where analytics helps make money, and where it only creates a feeling of control
  • what should be automated with AI, and what still needs human judgment
  • which hypotheses are worth testing next

Because when building a report becomes easy, the real skill is no longer “I can build a report”.

The real skill is knowing which report should exist in the first place.

AI will make data products almost free to produce. Sounds great.

But there’s a catch.

Instead of a graveyard of unused dashboards, we may get a graveyard of AI agents, automated reports, and data apps nobody needs either.

They’ll just appear faster. With nicer icons.

The problem was never only that teams were slow at building analytics.

The problem was that nobody really knew why they were building it.

So the question is not “will AI replace me?” The better question is:

am I just speeding up the old work, or am I helping the business understand what work is worth doing?

The first one is becoming a commodity. The second one is the new zone of value for analysts.

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