Why forward-deployed engineers should build capability, not dependency

Forward-deployed engineers (FDEs) from AI model vendors can help enterprises deploy production AI systems by combining hands-on customer collaboration with deep product knowledge, direct access to vendor engineering teams, and the ability to address issues in either the deployment or the underlying product. Unlike third-party consultants, FDEs may resolve model limitations, integration problems, and emerging capability gaps more directly, as illustrated by Cohere’s work rebuilding a customer’s meeting-briefing agent and improving its tool integrations for larger contexts. While vendor involvement can raise concerns about lock-in, the text distinguishes technology-related dependency on models and platforms from operational dependency caused by customer teams lacking the knowledge to maintain their systems. It argues that FDE engagements can reduce operational dependency when they emphasize co-building, documentation, training, reusable engineering practices, testing, and knowledge transfer throughout the project, enabling customers to eventually operate, troubleshoot, evaluate, and extend their AI deployments independently.

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