AI on tap, with plumbers included.

05.26.2026

Author - Enrique Dans

The promise of frontier AI has always sounded like a utility: abundant intelligence, available on demand, as easy to access as electricity, water, or cloud computing. And yet, the most sophisticated AI companies in the world are increasingly doing something very different: they are sending people.

Forward Deployed Engineers inside organizations will work with business leaders, operators, and frontline teams to identify where AI can make the biggest impact, redesign workflows, and turn those gains into durable systems. But if intelligence were already a true utility, this would not be necessary. You would not need to send your own engineers to every customer to make the faucet work.

Forward Deployed Engineers are often solving the real problem: taking frontier models out of the demo environment and making them function inside messy, regulated, fragmented organizations. They deal with permissions, legacy systems, compliance, data quality, workflows, operational constraints, and all the things that make companies different from benchmarks. 

Every major technology industry goes through an artisanal phase before it becomes industrial: Yes. But now the signal is different: the frontier AI industry is discovering that models alone do not cross the enterprise gap.

When the vendor itself has to supply the scarce human expertise required to make the product work, the category is still immature. 

Also once Forward Deployed Engineering becomes a source of revenue, prestige, customer lock-in and strategic proximity, it becomes harder for the vendor to eliminate it. The very people solving the product’s incompleteness can become part of the business model that depends on that incompleteness.

If a frontier AI company builds the layer that makes deployments repeatable, modular, and partner-scalable, it may undermine the bespoke, high-touch model that currently brings it close to the largest customers. That is why the real platform may not come from inside the companies training the models. It may come from another layer.

The next stage of enterprise AI will not be defined by who has the most impressive model or the largest deployment team. It will be defined by who builds the layer that makes those deployment teams less necessary.

And when the real platform layer appears, the industry will change very quickly. Because utilities do not scale by sending engineers to every sink. They scale when the plumbing is already there.