Why forward deployed engineering is growing

The role existed quietly for about twenty years. Then, across roughly eighteen months, it became one of the most-discussed jobs in the industry. Three things caused that, and the numbers behind each are worth handling carefully, because this is a topic where figures get repeated without their sources.

Cause one: pilots that do not reach production

In July 2025, MIT's Project NANDA published "The GenAI Divide: State of AI in Business 2025". Its headline finding was that 95 percent of generative AI pilots delivered no measurable profit-and-loss impact, and that only 5 percent of integrated systems created significant value.

That number is now quoted everywhere, usually naked. Here is the caveat it deserves: the study drew on 52 executive interviews, surveys of 153 leaders, and analysis of 300 public deployments. That is a real piece of research and it is not a census. Treat 95 percent as a strong signal about a real problem rather than a precise measurement of an industry.

What makes it credible is that it matches what anyone selling AI into a large company describes. The model works. The pilot impresses people. Then it dies in the distance between the pilot and the actual workflow, and the reasons are almost never about the model. They are about data nobody had audited, a success metric nobody defined before the work started, and users who were not involved in designing the workflow they were handed.

None of those three are solvable from outside the building. That is the whole argument for the role. Forward deployed engineering for enterprise AI covers the last mile in detail.

Cause two: investors started arguing for it out loud

On 4 June 2025, a16z partner Joe Schmidt published "Trading Margin for Moat", which called the forward deployed engineer the hottest job in startups and argued that AI companies should build implementation-heavy service teams instead of chasing product-led growth.

The evidence he uses is the previous generation of enterprise software. ServiceNow's gross margin at IPO was 63.2 percent and Workday's was 54.1 percent, both well below the 80 percent that software investors treat as normal. Both later climbed, to 79 percent and 75 percent in 2024. The services-heavy start was the on-ramp rather than the destination.

The piece also noted that OpenAI had 22 open forward deployed and solutions engineering roles out of 311 total positions at the time of writing, which is a useful marker of how seriously a frontier lab was treating the motion in mid-2025.

When a firm of that size publishes a thesis, founders act on it. A good part of the growth in postings is companies following advice they read.

Cause three: the largest cloud made it a product

In June 2026, AWS announced a Forward Deployed Engineering organization backed by a $1 billion investment, embedding thousands of engineers with customers to build production AI systems.

Two details in that announcement matter more than the dollar figure. AWS said the work would be structured around shared goals and business results rather than billable hours. And it named customer self-sufficiency after the engagement as a design goal. Francessca Vasquez, VP of Frontier AI Engineering and Services, framed the demand as customers having moved past exploring what AI can do and wanting it to be core to how they operate.

A month later, AWS extended the model to consulting partners, describing a three-phase pattern: AWS engineers embedded with partner teams, then support shifting toward helping partners scale their own practice, then partner teams working independently. It also named the reusable assets that make the model repeatable, including domain ontologies, evaluation frameworks and capability registries.

When the incumbent cloud provider builds an organization around embedded engineers and prices it on outcomes, the model has stopped being one company's eccentricity.

What the hiring data shows

The clearest measurement comes from the analysis of 1,000 postings published in November 2025 using Revealera data. Postings carrying the title grew 1,165 percent year over year, comparing January to October 2025 against the same period in 2024, and October 2025 was the highest volume on record.

Two things in that dataset qualify the growth. First, the title covers three different jobs, so part of the increase is relabelling rather than new work. We take that apart in the three jobs hiding behind the title. Second, 58 percent of postings came from companies with 11 to 200 employees, which tells you the adopters are growth-stage companies selling into large enterprises rather than only the well-known names.

The industries named are consistent with the argument: financial services in 24 percent of postings that specified one, government and defence 18 percent, healthcare 17 percent, insurance 17 percent, energy and utilities 13 percent. These are the sectors where the environment, rather than the model, is the hard part.

The cause underneath all three

Strip out the announcements and one mechanism is left. Every large company has its own data, its own workflows, its own compliance constraints and its own definition of good enough. Selling an AI product into one of them is also selling an integration project. Somebody has to do that project.

For twenty years that somebody was a systems integrator working from a specification. What changed is that the specification cannot be written in advance any more, because with AI systems the customer does not know what good looks like until they see the system behave on their own data.

What could stop it

Worth saying, since most coverage of this treats the trend as permanent.

Two things would slow it. The first is the model's own economics: an embedded engineer is expensive and lands on gross margin where investors see it, and a16z's Marc Andrusko argued in January 2026 that most companies copying this end up with an expensive services business rather than a platform. The Palantirization problem covers that case.

The second is automation of the work itself. Palantir now ships a product called AI FDE, an agent that operates its platform through conversation. The part of the job that is platform operation is being automated by the same wave that created the demand. Is the role being automated works through what that leaves.

Our read: the typing shrinks and the accountability does not move. Deciding which problem is worth solving inside an organization that cannot describe it stays a job for a person, and so does being answerable when the deployment does not run.