More and more AI companies are taking the Forward Deployed Engineer route as they look to get organizations to use their products.
Google Cloud CEO Thomas Kurian announced this week that the company is building a new AI-focused organization within its go-to-market team, with a significant push to hire Forward Deployed Engineers (FDEs). The move is designed to place Google engineers directly inside customer organizations to accelerate adoption of its enterprise AI products and agent platform. Google Cloud’s chief revenue officer Matt Renner put it plainly: “We will not just send lots of sales staff, but will approach customers with more technical resources.”
The timing is deliberate. This announcement comes days after OpenAI launched The Deployment Company — a $4 billion venture backed by TPG, Goldman Sachs, and McKinsey — and acquired Tomoro, an Edinburgh-based AI consulting firm, to immediately staff it with approximately 150 battle-tested FDEs. Anthropic, meanwhile, announced its own joint venture backed by Blackstone, Hellman & Friedman, and Goldman Sachs to embed its Applied AI engineers at enterprise clients. Three of the biggest names in AI, all moving in the same direction within the same week.
The Palantir Playbook
None of this is new. The FDE model was pioneered by Palantir, which embedded engineers — called “Deltas” internally — inside defense and intelligence agencies for years. Until 2016, Palantir actually had more FDEs than software engineers. The logic was simple: complex software inside complex institutions cannot be deployed remotely. You have to send in people who think like startup CTOs, with a mandate to make things work rather than find reasons they won’t.
The model proved its worth financially. Palantir’s stock dropped to $6 in 2022 before returning over 640% in five years — a run driven not by superior model benchmarks, but by deployment depth that competitors were unwilling to match because it looked too expensive and too operationally intense.
Now the entire AI industry is copying it.
Why Now
The shift reflects a hard truth that’s become impossible to ignore: model quality is no longer the bottleneck to enterprise AI adoption. Integration, change management, security review, and the slow work of redesigning business processes around AI are the real constraints. Enterprises aren’t failing to adopt AI because they can’t access powerful models — they’re failing because they can’t operationalize them.
OpenAI’s own share of the enterprise API market reportedly fell from around 50% in 2023 to roughly 25% by mid-2025, with Anthropic and Google making significant inroads. Building the best model is no longer sufficient. What matters now is who can actually operationalize AI inside the world’s most complex organizations. The FDE is the answer the industry has landed on.
Google’s Scale Play
Google Cloud’s FDE push is not just about headcount — it is woven into a broader $750 million commitment to its 120,000-member partner ecosystem. A chunk of that funding will go toward placing Google FDEs inside major systems integrators like Accenture, Capgemini, Cognizant, Deloitte, HCLTech, PwC, and TCS — embedding Google’s engineers at the firms that carry the most weight in enterprise IT decisions.
At Google Cloud Next 2026, Kurian declared “the era of the pilot is over. The era of the agent is here.” He outlined plans to massively expand Google’s field organization, core technology engineering, and forward deployed engineering across industries. The job listings already span roles across the US, India, Brazil, Australia, Mexico, Singapore, South Korea, and Canada — with seniority levels ranging from FDE II up to FDE IV, and specializations covering everything from telecommunications to generative media.
The minimum bar is high: most roles require hands-on experience with RAG architectures, vector databases, foundation model fine-tuning, and production-grade AI deployment on cloud platforms. These are not sales engineers. These are builders who are expected to own delivery of high-stakes projects at the customer site.
What This Means
The FDE model is sticky in a way traditional SaaS is not. When a team of embedded engineers spends months building a custom AI system deeply integrated into a company’s internal data, workflows, and compliance architecture, the switching cost becomes enormous. This is the strategic bet: deployment expertise is the scarce resource, not model capability.
For enterprises, the implication is significant. With Google, OpenAI, and Anthropic all racing to embed engineers inside organizations, the competition for AI adoption is shifting from the model layer to the services layer. The companies that win the agentic era may not be the ones with the best models — they may be the ones with the best engineers on the ground.