The Forward-Deployed Engineer Model: A Founder's Buyer's Guide<br>Skip to contentESSAYAUG 202618 MIN READ<br>What Founders Should Steal From the Forward-Deployed Engineer Model (and What to Leave)<br>Every serious buyer of AI now gets the same thing: a senior engineer embedded in the business, judged on production outcomes. The real thing starts at $1 million a year. Five of its mechanics translate to founder scale, and this is the buyer's guide to them.<br>Damir Mujic<br>FOUNDER & PRINCIPAL CONSULTANT
Nabeel Qureshi joined Palantir in the summer of 2015. He later moved to Toulouse and spent a year working four days a week inside the factory where Airbus builds the A350. His team's software tracked work orders, missing parts and non-conformities across the production line. He calls it "Asana, but for building planes," and writes that it "ended up helping to drive the A350 manufacturing surge and successfully 4x'ing the pace of manufacturing while keeping Airbus's high standards of quality."
The same essay holds the opposite memory. On other accounts, "you'd have a company buying an 8-12 week pilot, and we'd spend all 8-12 weeks just getting data access, and the final week scrambling to have something to demo."
Same firm, same software, same hiring bar. What varied was where the engineer sat and how much license he had to push back.
A decade later, the biggest names in AI have picked a side. In May 2026 OpenAI launched a Deployment Company with $4 billion from a nineteen-firm syndicate led by TPG. "Our customers tell us they need help going from pilot to production," said COO Brad Lightcap. "Deployment Company will put our engineers inside their teams, with the resources to ship." At the end of June, AWS announced a $1 billion Forward Deployed Engineering organization: pods of five or six engineers, forty-five-day engagement cycles. Two days later Microsoft unveiled Frontier Company, a $2.5 billion unit of some six thousand engineers and industry experts embedded inside customers. And in mid-July Anthropic, Blackstone and Hellman & Friedman, with Goldman Sachs among the investors, capitalized Ode with $1.5 billion to do the same work against live enterprise data.
Every serious seller of AI has converged on the same delivery model: a senior engineer inside the customer's business, judged on production outcomes rather than demos.
Here is the part that concerns you, the founder of a company doing $3 million to $15 million a year. None of this is being built for you. OpenAI charges at least $10 million per client for this kind of work, per The Information. The smallest deal size Palantir even discloses is $1 million, and its average customer is worth about $4.7 million a year to them ($4.475 billion in revenue across 954 customers, per the FY2025 10-K; the division is ours).
You cannot buy the real thing. You can understand why it wins, and buy the five mechanics that survive translation to your scale. That is what this essay is for. (Disclosure, early and plainly: Mercury, the firm publishing this, sells senior advisory and engineering of the kind the last third argues for. Read accordingly.)
The numbers everyone quotes, corrected
You have probably seen the claim that 95% of AI pilots fail. It is a misquote, and since this essay will lean on the underlying study, the correction matters.
The source is MIT's NANDA initiative, whose July 2025 report drew on 52 organization interviews, 153 leader surveys and 300+ public initiatives. What it found: despite $30 to 40 billion of enterprise investment in generative AI, 95% of organizations were getting zero return, meaning no measurable P&L impact roughly six months after their pilots. The pilot-level numbers are separate, and worse in a more specific way: of custom, task-specific enterprise tools, about 5% reached production. Generic LLM tools reached production around 40% of the time. The report is preliminary, self-described as "directionally accurate," and drew fire for its methodology (Futuriom: "The 95% figure is presented in one sentence, but the authors offer no detail on where they came up with that number"). Treat it as one loud data point, corroborated by quieter ones.
The quieter numbers point the same way. S&P Global's 451 Research surveyed 1,006 IT and business professionals and found the share of companies abandoning the majority of their AI initiatives jumped from 17% to 42% in a year; on average, 46% of projects were scrapped between proof of concept and broad adoption. Gartner forecasts that over 40% of agentic AI projects will be canceled by the end of 2027; a forecast, and a directional one.
The headlines skip why. RAND interviewed 65 practitioners and found 84% of them citing leadership-driven root causes, misunderstanding or miscommunicating what problem needed solving, as the primary reason AI projects fail. Data quality came second. BCG's survey of 1,000 executives attributes 70% of implementation difficulty to people and process, 20% to...