Anthropic, OpenAI, and other frontier AI companies have begun hiring Forward Deployed Engineers at scale. The models are ready. The gap is deployment. These companies are now building the teams whose job is to make AI work inside real organizations.
An FDE embeds directly at a client site and stays until AI is running in production workflows. A PoC completes successfully, then nothing changes. A six-month AI transformation project ends, and no one uses the output. The bottleneck is almost always on-site. And on-site, one perspective is never enough.
Why Most AI Projects Stall at PoC
According to an Anthropic report, 90% of AI agents outside coding tasks fail in production. Even top-tier LLMs score below 47% on enterprise IT benchmarks. The gap is not model capability.
Enhans has identified three recurring failure patterns from the field.
First, workflows are not designed to accept AI output. PoC environments use clean, curated data. Real operations run on unstructured data spread across multiple systems.
Second, AI outputs do not connect to how decisions actually get made. A technically correct answer lands with no clear path to action if the people using it do not know how to interpret and apply it.
Third, problems that never appeared during PoC surface in production: legacy system integration, security policies, data access permissions.
The Answers Are On-Site
These problems do not get solved by improving the model or rereading the requirements document.
Real operations contain things that are not written down. The informal workflows people actually use instead of official processes. The operational context compressed into a single data field. The gap between two connected systems that someone bridges manually every day. The unconscious judgment calls that experienced staff make without realizing it.
None of this shows up in requirements documents or video calls. It only becomes visible through direct conversation with the people doing the work and direct observation of how work actually happens.
That is why FDEs go on-site. The job is to make implicit knowledge explicit and connect it into a structure where AI can actually operate.
One Problem. Three Readings.
Problems in enterprise environments rarely have a single cause. A workflow may be poorly designed. Data may be scattered and unstructured. The system architecture may not be capable of accepting AI at all. A single diagnostic lens produces a single type of answer.
The same situation reads differently depending on which expertise is applied. From a workflow perspective, the problem calls for process redesign before anything else. From a data perspective, ontology structuring comes first. From a systems perspective, the integration architecture needs to be rebuilt. In most real engagements, all three are true at the same time.
Enhans FDE draws on PM, Data, and Engineer expertise to diagnose each client's AI deployment challenge. Defining the problem correctly is where every engagement begins.
How Enhans FDE Works
PM FDE defines the work context. Before any technical decision is made, the right place for AI in the workflow is identified at the level of work design.
Data FDE refines and structures enterprise data into a digital twin ontology that mirrors how the business actually operates, in a form AI can process.
Engineer FDE connects that structure to the client's internal systems, enabling AI agents to understand ontology-based work context and execute operations using the right tools.
When the right combination of perspectives is applied to the same problem, things that no single lens would have identified get resolved. If the work design is wrong, Data catches it. If the data structure shifts, PM reviews the work context again.
An AI System That Runs in Production
Business processes built from organizational data and tacit knowledge are refined, connected, and structured to match actual operations. AI operates within actual workflows. The system becomes more capable as projects stack up. Built on Enhans' AgentOS, this structure does not require starting from scratch when the next engagement begins.
The three-lens approach exists because the field demands it. The model is ready. What breaks down is the mapping: no one defined the full problem before building the solution.
Enhans FDE surfaces tacit knowledge, structures it into an ontology, and builds the system alongside the client until it runs in production. Three perspectives. One system. Actually running.
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