What “Forward Deployed” actually means, and why your team can already do it

A new term is spreading through corporate AI strategy decks with remarkable speed. Forward Deployed Engineer, or FDE, has become the phrase technology vendors, consultancies and LinkedIn thought leaders reach for when they want to signal serious, embedded, outcomes-focused AI delivery. Like most terms that travel this fast, it is in danger of meaning everything and nothing at once.

As a starting point, it is worth slowing down long enough to understand what it actually describes and where it came from. Most importantly,  the organisations best positioned to benefit are probably not the ones scrambling to hire for a job title that did not exist three years ago.

What ‘Forward Deployed’ actually means

The name originated at Palantir Technologies, which borrowed “forward deployed” from military vocabulary: the idea of personnel stationed close to the front lines rather than operating from a distance. Palantir used it to describe engineers embedded directly inside client organisations, solving real problems on-site rather than building products in isolation and hoping they would fit.

The term has since been adopted across AI models and platform providers as shorthand for a particular kind of delivery model: one where the technologist and the business problems occupy the same room.

Strip away the branding, and three distinct skill groupings that sit underneath it:

1. Applied engineering: Building production-grade solutions close to the problem
2. Consultative judgement: The ability to sit inside a client’s context, navigate its constraints, and identify what is actually needed, which often differs from what was first requested.
3. Product instinct: Recognising which patterns can be generalised and reused, so each successive problem is solved faster.

These three skills in combination are what make the model work. Any one of them alone produces something familiar but incomplete: engineers who build the wrong thing, consultants who recommend but cannot build, or product thinkers disconnected from the messy reality of customers.

This model is not, in fact, new

Organisations that have been doing serious technology consulting have been delivering exactly this way for years; embedding multidisciplinary teams inside client organisations, building alongside them, and leaving capability behind. The difference today is that AI has raised the stakes, accelerated the pace, and given the pattern a catchy name.

We know this because we have lived it. Long before FDE became a buzzword, we have been embedding multidisciplinary pods inside client organisations who are building and iterating solutions, and leaving new capability behind. The model has a new name, but the skills and the way of working are not new.

Using forward deployed AI teams in practice

Consider a large financial services organisation facing a problem familiar to most enterprises at scale: significant manual effort across analysis, testing and defect triage, fragmented tooling, and no clear path to scale what individual engineers were already doing informally with AI. The question was not whether AI could help. It was how to embed it without compromising quality or the controls that regulated environments demand.

Rather than delivering a recommendation or a prototype, we embedded a forward deployed pod directly across targeted engineering squads. Over eight weeks, working alongside the client’s own engineers using a baseline, enable, adopt, measure model, the pod introduced agentic workflows across the product development lifecycle and built a tooling-agnostic playbook designed to travel across the organisation rather than sit with a single team.

The results were tangible:

But the metric that mattered most was not in that list. The squads moved from ad hoc AI usage to AI woven into daily practice. Not just through engineering, but through the knowledge and frameworks to really embed it. The capability stayed in the organisation after the engagement ended through enduring artifacts such as an Agentic Toolkit. That is the repeatable model now being rolled out more broadly across the business.

How executives can build forward deployed capability

For leaders sponsoring AI investment, the instinct is often to ask: do we need to hire Forward Deployed Engineers? The more useful question is: do we already have the component skills, and are we deploying them together?

The strongest technology, data and product professionals in most large organisations have already grown into the capability this model requires. They can build. They understand the business context. They have developed the judgment to know what will actually be used versus what will be impressive in a demonstration. What they often lack is not skill, but permission, the structural conditions to work in close proximity to the problem, in small empowered teams, with a mandate to build and measure rather than recommend and hand off.

We have found that this is what the pod model addresses. Rather than a single embedded engineer, a FDE pod brings the three skill clusters together in a team sized for the work, paired with the change capability needed to make adoption stick. It is the difference between a fast pilot and a repeatable pattern.

We have demonstrated this time and time again in engagements that are truly moving the needle.

Driving sustainable AI adoption

The organisations that will extract sustainable value from AI are not necessarily the ones moving fastest. They are the ones that treat adoption as the point of the work, not the final 10%. Technology that is not used does not create value, regardless of how well it is built.

The forward deployed model or whatever you choose to call it,  is the right structural response to that challenge. But in large organisations, the pod is the unit that makes it work: a team that can build, advise, and embed simultaneously, and that leaves the skills it brings behind rather than taking them away when the engagement ends.

For executives trying to work out where to start: look at your existing teams before you look at the hiring market. The capability is more likely already there than the current noise around job titles would suggest.