A talent obsession with numbers to match
New research from executive search firm Christian & Timbers, built on interviews with more than 250 C-suite executives across 180 companies and over 300 forward deployed engineers between January and June 2026, quantifies something CIOs have felt anecdotally for a year: the hardest part of enterprise AI is no longer the model, it is the person who can make the model work inside a specific business. The firm projects demand for forward deployed engineers, sometimes called FDEs, to surge 2,100 percent by the end of 2026. That is not a typo or a rounding exercise. It is a category of hiring that essentially did not exist as a distinct job title two years ago now becoming one of the tightest talent markets in enterprise technology.
The adoption curve inside that number is just as sharp. At the start of 2026, only 5 to 10 percent of companies surveyed said they planned to hire forward deployed engineers. By the second quarter, 70 percent did. Jeff Christian, founder of Christian & Timbers, described the shift bluntly: this is all happening at a speed he has never seen, with enterprises hiring in the middle of summer, typically the slowest hiring season of the year. That timing detail matters. Companies do not accelerate hiring against seasonal norms unless the cost of waiting has become obviously higher than the cost of moving fast.
The supply side is the real constraint
The research puts a number on the scarcity that should reset expectations for any CIO planning an AI hiring strategy: roughly 17,000 forward deployed engineers currently work in the U.S. market, but only about 2,000 of them have the specific expertise to deliver AI implementations with meaningful return on investment, defined in the research as impact in the multiple tens of millions of dollars. That is not a shortage of junior talent that time and training will fix quickly. It is a shortage of people who have already done the specific, unglamorous work of embedding with a business unit, understanding its workflows, and shipping an AI deployment that survives contact with real operations.
Large consulting firms are responding by scaling existing pods rather than trying to hire from a thin external market alone. The research found firms reporting a need to grow forward deployed engineering teams by a factor of ten, moving from small teams of a handful of people to teams of 20 to 100. That kind of internal scaling, promoting from adjacent roles and building training pipelines, is itself an admission that the external labor market cannot supply this skill set fast enough through hiring alone.
What forward deployed engineers actually do differently
The role gets conflated with generic AI implementation consulting, but the distinction is specific and worth CIOs understanding before they write a job description. Chris Taylor, CEO of Ode, an AI implementation firm working with Anthropic, drew a sharp line on what most FDEs in the market today are actually capable of: many can roll out a coding assistant like Claude Code effectively, but very few can build flagship AI product features from scratch. That gap between deployment competence and product-building competence is exactly where the 17,000 versus 2,000 figures diverge, and it is the gap a CIO needs to test for in interviews rather than assuming a forward deployed engineer title guarantees the harder skill.
The origin of the term traces back to Palantir, which built its consulting model around engineers embedded directly inside client organizations rather than working from a vendor's own offices. OpenAI and Anthropic have since built out similar functions, with OpenAI's deployment-focused teams and Anthropic-aligned firms like Ode following the same pattern. The through-line across all of these is that the engineer's job centers on translating a general-purpose AI system into something that works reliably against one company's specific data, workflows, and failure modes, rather than writing new model code.
Why this changes the AI buy versus build calculus
For a CIO evaluating whether to build an in-house AI implementation capability or rely on vendors and consultants, this research reframes the decision. The scarce resource is not compute or model access, both of which are now commodity purchases at enterprise scale. It is the roughly 2,000 people nationwide who can reliably close the gap between a promising pilot and a production system that survives real operational load. That scarcity means the build option is genuinely harder to execute than it was even a year ago, when a strong internal engineering team could plausibly stand up its own AI deployment function.
It also means vendor and consulting relationships built around forward deployed engineering talent carry a premium that will likely persist rather than compress, at least until training pipelines catch up with demand. Christian was candid that the current moment may not last: he raised the possibility that within two years, much of what forward deployed engineers do today could become automated. CIOs should treat that as a real scenario worth planning for, not dismiss it, because it changes whether this is a multiyear talent strategy or a bridge investment to be made deliberately short.
What this means for the CIO roadmap
The immediate action is to stop treating forward deployed engineering as a subcategory of general AI hiring and start treating it as its own scarce, specifically defined skill to source, test for, and retain. Given that 70 percent of companies are now planning to hire for this role, competition for the qualified 2,000 will only intensify, and a CIO who has not already identified whether existing engineering staff have the embedded, workflow-first skill set this role demands is starting from behind peers who moved in the first half of the year.
For organizations that cannot compete on compensation for that narrow pool, the more realistic path is internal development: identifying engineers who already understand the company's specific operational workflows and pairing them with vendor-provided forward deployed engineers during initial deployments, with an explicit goal of transferring the skill in-house. That approach trades speed now for capability later, which is a reasonable trade if Christian's two-year automation window turns out to be accurate and the premium on this talent proves temporary rather than permanent.
