The rise of AI Engineers

A prediction stood out to us, one that speaks directly to how data and BI teams will be structured in the coming years.

Gartner expects that by 2027 there will be three times more open roles for AI engineers than for data scientists, driven by the rise of pretrained models and the need to integrate AI into real business processes. 

It points to a clear shift in where the real work of AI is moving.

Why this shift is happening

For a long time, companies focused on hiring data scientists to build custom models. That made sense when organisations relied on bespoke ML to solve specific problems. 

Today, pretrained models are strong enough that the main challenge is no longer building models but making them work reliably across products and workflows.

This is exactly what AI engineers are hired to do. Their work sits between data, software and operations. They ensure that models can be:

• Integrated into daily tools and applications
• Powered by clean and well structured data
• Monitored for accuracy, cost and security
• Scaled across teams without friction

In other words, the value shifts from experimentation to execution.

What this means for Data and BI teams

Gartner’s broader trend analysis shows that AI will reshape much more than just model development. Natural language will become a common way to access data, AI will automate parts of data integration and cloud spend will need tighter control due to AI heavy workloads.

This reshapes expectations for Data and BI teams. They will need to:

• Validate AI outputs inside analytics workflows
• Manage the cost of AI driven data pipelines
• Oversee governance of training data and model use
• Support product teams embedding AI in user facing tools

These responsibilities sit squarely in the realm of AI engineering rather than classic BI development.

A growing talent challenge

Gartner also warns that by 2030 half of enterprises will face lasting shortages in critical roles as AI adoption accelerates. AI engineering is likely to be one of them. Organisations waiting too long to adapt their skill mix risk falling behind as demand outpaces supply.

Forward looking teams can avoid this by upskilling BI and analytics engineers, encouraging close collaboration with software teams and putting lightweight governance around AI use cases.

A chance for BI teams to grow their influence

This shift is not a loss for BI. It is a growth opportunity. As AI becomes embedded in decision making, the teams who understand data and can operationalise AI will be central to digital strategy.

The future of BI belongs to teams that can ship AI, not just analyse it. Building AI engineering capability now will put organisations ahead of the curve.