Most AI pilots fail due to poor change management and a lack of business context. A successful model involves embedding vendor engineers within the client's team to handle agent onboarding, systems integration, and process customization, ensuring the AI works within the company's unique environment.

To accelerate AI adoption in large, slow-moving enterprises, startups are reviving the 'forward deployed engineer' model. By embedding their own engineers within customer organizations to build and implement solutions, they overcome internal inertia and talent gaps, dramatically shortening sales and deployment cycles for complex AI products.

Unlike professional services that trade time for money, Anthropic's Forward-Deployed Engineers (FDEs) partner on outcomes. Their core goal is to solve novel problems, feed learnings back into product/research, and create scalable templates for future customers, not to build a billable services arm.

Unlike traditional SaaS, AI agents require weeks of hands-on training. The most critical factor for success is the quality of the vendor's forward deployed engineer (FDE) who helps implement, not the product's brand recognition or feature superiority.

Complex agentic products require hands-on help to deploy successfully. Gating Forward Deployed Engineers (FDEs) to only large customers leads to failed 'zombie deployments.' AI companies should view FDEs as an investment in customer success and word-of-mouth, even if it means initially spending a dollar to make a dollar.

The high-margin, pure Software-as-a-Service model is becoming obsolete in the AI era. Complex AI implementation requires hands-on integration, giving rise to consultative models like the "forward deployed engineer," where provider experts are embedded with clients to ensure success.

Inside AWS, engineering teams are transitioning from writing code to managing fleets of agents. Matt Garman notes this has shrunk team sizes needed per initiative from ten engineers down to three or four. Because these smaller teams deliver features significantly faster, organizations face the operational challenge of continually redeploying engineers across projects while sustaining ongoing maintenance.

As enterprises deploy agents for critical tasks like RFP generation or invoice processing, they will require dedicated evaluation frameworks and teams. This will create a massive new market for agent observability and eval tools, moving them beyond AI-native companies to the broader enterprise.

To overcome high AI pilot failure rates, companies like Pace use "forward deployed engineers" (FDEs). These founder-type individuals work onsite, deeply understand customer problems, and do whatever it takes—from prompt tuning to data cleaning—to ensure successful production deployment.