If you speak to most Heads of AI, ML, or Data Science right now, you’ll hear a familiar frustration: “We can’t find the talent.”
But here’s the uncomfortable truth: it’s not just a talent shortage anymore. It’s a talent allocation problem.
And the latest insights from Deloitte back that up.
On paper, organisations feel ready for AI.
But when it comes to execution?
In other words, organisations have the slide decks, but not the people to deliver them. From a hiring perspective, this shows up as:
Overly ambitious job specs
Misaligned expectations (one hire to “do it all”)
Slow, reactive hiring processes
One of the most overlooked risks in AI transformation is pipeline erosion.
The roles that are able to be automated include:
These are exactly the roles that traditionally feed:
Junior analysts
Data analysts
Analytics engineers
So now we have a paradox:
That’s not a shortage, that’s a broken talent funnel.
There’s also a major misconception playing out. Within three years 82% of companies expect 10% of jobs to be fully automated. However, we’re now finding that AI isn’t removing jobs at the scale organisations expect, it’s simply reshaping them. That distinction matters because if your hiring strategy is still based on replacement, you’re already behind.
Leaders are starting to realise:
“We thought we were going to automate jobs. The truth is, you’re not. You’re going to give existing workers force multipliers where they can be more effective.”
However, 84% of organisations still haven’t redesigned job roles to accommodate AI support. This is a shocking stat because AI fundamentally changes decision-making, accountability and skill requirements.
Take the example of a Loan Officer who has always used judgment and experience to approve loans who must now work with an AI system that provides recommendations.
What’s happening is that most organisations are still trying to bolt AI onto legacy roles.
From a recruitment standpoint, this creates chaos:
Right now, most companies are doing the same thing:
That’s not wrong, but it’s not enough. Because teaching people about AI ≠ designing organisations that actually use it effectively.
Meanwhile:
And that’s where the real gap is emerging.
The most forward-thinking organisations are already moving beyond traditional job titles.
We’re seeing growth in roles like:
Yes and no! There is a shortage of:
But there’s also a massive misallocation problem:
Roles that don’t reflect how AI actually works
Hiring strategies built for a pre-AI world
From what I’m seeing across the market, the strongest Heads of AI / Data are:
Not just adding AI into roles but rebuilding them from scratch.
Creating new entry routes as traditional ones disappear.
The best talent now sits at the intersection of:
The companies that win in 2026 won’t be the ones with the biggest AI teams, they’ll be the ones who answer a much harder question:
Because right now, that’s where the real gap is.
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