The headlines about AI eliminating jobs keep coming, but the actual workforce data tells a more complicated story — and for anyone working in data, it’s one worth understanding closely.

Visier’s new “Hidden in the Headcount” report analyzed over 3.6 million live employee records across 155 enterprise organizations from 2022 to 2026. The top-line finding: overall hiring is down 24%. But inside that number, the picture looks very different depending on what kind of work you do.

Data and analytics roles have grown their share of total headcount by 49% over the same period. Product management is up 39%. Finance is down 4%. HR is down 3%. These aren’t rounding errors — they’re structural shifts in how large organizations are allocating their human capital as AI becomes part of how work gets done.

The AI Isn’t Replacing Data People — It’s Changing What Data People Do

The 49% headline growth in data and analytics is real, but it masks a dramatic reshaping happening inside the profession.

AI Engineers hired within data teams grew by 251%. Data Scientists, meanwhile, dropped 32%.

That gap tells you something important: companies aren’t reducing their investment in data capability. They’re redirecting it. The role of the traditional data scientist — writing models from scratch, building pipelines, running experiments — is being partially absorbed by AI tooling. What organizations need more of now is people who understand how to build, deploy, and govern AI systems in production.

This is not a comfortable shift for every practitioner. But it is a real one, and the data is clear: data fluency still matters enormously. The question is which flavour of data fluency your organisation is hiring for.

What This Actually Means for Business Leaders

If you run a business and you’re trying to figure out whether to invest in your data team or assume AI will handle it, this research gives you a useful benchmark.

Companies that have scaled AI in production aren’t running with fewer data people — they’re running with different ones. They need people who can interrogate AI outputs, spot when a model is drifting, design the right questions to ask, and make decisions when the system surface ambiguity that no algorithm can resolve cleanly.

The 24% drop in overall hiring also suggests something else: AI is doing real work that humans used to do, particularly in roles that are highly procedural or repetitive. That’s not a scandal — that’s productivity. The challenge for business leaders is figuring out which of their roles are in that category, and which require the kind of contextual judgment AI still cannot replicate reliably.

The “Irreplaceable” Skills Are Not What Most People Expect

Across the Visier data, the roles holding their share of headcount or growing tend to share a few traits: they require cross-functional judgment, they involve managing relationships and stakeholders, and they sit close to strategic decision-making.

Data and analytics qualifies on all three counts. A good data analyst or analytics engineer isn’t just running queries — they’re translating business context into questions, presenting findings to people who don’t speak SQL, and helping an organisation figure out what to do with what the data says.

That’s hard to automate. The tooling is getting better, but the judgment layer — knowing which question is worth asking, why an anomaly matters, what a trend means for a real business decision — still sits with people.

What This Means for Upskilling

The Visier data puts pressure on learning and development teams. If data analytics roles are growing 49% in share, and AI Engineering roles within that domain are growing 251%, the logical implication is that the people who thrive will be those who move toward the intersection of data fluency and AI systems knowledge.

That doesn’t mean everyone needs to become an ML engineer. It means:

• Business analysts need to understand what AI models are and aren’t doing when they interpret AI-assisted outputs
• Data professionals who’ve been working primarily with traditional BI tools need exposure to how AI pipelines actually work
• Leadership teams need enough data literacy to ask good questions of the AI systems their organisations are deploying

This is precisely why Enterprise DNA has been expanding its curriculum beyond Power BI and SQL into Python, AI integration, and applied data science. The market is moving, and the Visier data confirms the direction.

The Real Question Is Timing

The 32% drop in data scientist hiring isn’t a signal to abandon the profession — it’s a signal to evolve it. The skills that defined the role five years ago are being automated or commoditised. The skills that matter now sit at the intersection of AI capability and business context.

Companies that understand this early will have a significant advantage. They’ll build teams that can actually use AI effectively — not just deploy it and hope for the best.

Those that don’t will find themselves with falling data team headcount and no explanation for why their AI investments aren’t producing results.

What This Means for Business

The Visier research suggests that AI is redistributing value within data and analytics, not eliminating it. That’s meaningfully different from how most AI workforce coverage frames the question.

The 49% headcount share growth for data and analytics isn’t an accident — it reflects what happens when organisations try to actually use AI at scale. They need more people who understand data, not fewer. But those people need a different and broader set of skills than the ones that dominated the field in 2021.

If your organisation is still thinking about AI adoption as a headcount reduction exercise, this data should give you pause. The companies scaling AI most effectively are investing in data capability alongside it — because that’s what it takes to use it well.

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Enterprise DNA is a global platform for data and AI skills training, with courses in Power BI, Python, SQL, AI integration, and applied analytics. If your team needs to upskill for the AI era, explore our learning platform or speak to us about a business plan that fits your team’s needs.