The bottleneck in enterprise AI has moved from experimentation to production. Deloitte’s 2026 State of AI in the Enterprise report found only 25% of organizations have moved 40% or more of their AI pilots into production. That gap is where forward-deployed engineers (FDEs) from model vendors can help—but the real question is whether they leave behind capability or dependency.
Why vendor FDEs differ from third-party help
Vendor FDEs sit inside your technical environment while staying connected to the team building the underlying models. That gives them deeper product knowledge and direct access to research and engineering. When a Cohere customer’s agent failed because its instructions referenced nonexistent tools, the FDE rebuilt the agent and modified the Slack integration to handle larger contexts. Third-party providers often lack that visibility into root causes and must build workarounds or escalate back to the vendor.
This is not just faster escalation. It’s a wider set of options for making a deployment work without engineering around the product.
The lock-in worry is real, but not inevitable
Some dependency comes from the technology choice itself—using a vendor’s models or APIs creates architectural lock-in regardless of who deploys them. The FDE-specific risk is operational dependency: if critical system knowledge stays with the vendor’s engineers, your team can’t diagnose problems or modify the system safely. You own the deployment but not the capability to operate it.
Capability-building as the default
Cohere’s approach embeds knowledge transfer from the start. FDEs work alongside customer engineering teams through architecture, integration, deployment, testing, and troubleshooting. They run enablement sessions for major features and train internal champions. For technical topics like Model Context Protocol, FDEs help developers establish stronger implementation patterns.
Beyond system knowledge, FDEs help teams build reusable practices around deployment, load testing, and connector development. They also help customers create test suites that verify core functionality as model versions and configurations change. The goal is not just to transfer knowledge about the current deployment, but to give customers the ongoing ability to operate, evaluate, and extend the AI system independently.
What this means for your build-vs-buy decision
If you’re evaluating FDE support, ask what kind of dependency the engagement will leave behind. The best FDE engagements use deep product expertise to reduce your reliance on that expertise over time. Close vendor involvement should create greater operational independence, not less.
This aligns with the broader shift toward right-sizing AI for enterprise needs—the goal is building internal capability, not outsourcing your thinking. For teams choosing structured data extraction tools, the same principle applies: pick tools that strengthen your team’s ability to operate independently.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.