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A services role associated with Accenture and Anthropic's enterprise AI deployment partnership, focused on turning AI capabilities into deployed customer workflows at scale.

OpenAI's partner program for helping enterprises deploy frontier AI through trained service and consulting partners.

Palantir's Artificial Intelligence Platform: a governed environment for building AI applications and workflows over enterprise data, tools, and ontology-backed permissions.

A go-to-market and delivery motion where hands-on services, implementation, or field engineering are part of the value proposition — not an afterthought or a professional services add-on.

A customer implementation delivered by a partner — often with vendor FDE guidance, playbook access, and escalation support for issues the partner can't resolve.

The work of helping an organization adopt a new workflow through communication, training, incentives, support, and leadership alignment. An FDE treats this as part of deployment, not the customer's problem.

The information needed to make a workflow step useful: user role, current task, relevant object, policy, history, and next action. FDEs pass exactly what's needed, not the whole ticket.

The degree to which output gives a user enough specific, trustworthy information to take the next step. An answer can be accurate but still lack actionability.

A mechanism for collecting user reactions, corrections, overrides, and requests and feeding them back into deployment or product improvements.

The acceptable cost range for running a deployment — model usage, infrastructure, support, and integration maintenance. Shapes model routing and caching decisions.

The total time a workflow can spend on model calls, retrieval, tool calls, and UI updates before users lose patience or trust. Forces caching and architectural choices.

An expected service level for a workflow — response time, turnaround time, uptime, or escalation time. Defines the reliability bar the deployment must meet.

Credentials scoped to live systems — managed, rotated, and audited more carefully than test credentials. The FDE waits for these before final validation.

A customer-controlled test environment used to validate integrations, permissions, and workflows before production. Usually has fake or masked data and less strict network rules than prod.

A small UX, data, or workflow friction that seems minor but harms adoption when repeated on every case.

A manual or improvised process customers use when the product doesn't fit their workflow. The best source of product gaps if the FDE takes time to document it.

The constant FDE tension between solving the immediate customer need fast, keeping scope narrow, and building something reusable for the next account.

Specific conditions the customer and FDE agree must be true before a feature, workflow, or deployment is considered done.

A less common but important scenario that can break trust, safety, or usefulness if the deployment mishandles it. FDEs find these by watching real users, not by reading requirements.

The straightforward case where the workflow works as expected, without edge cases, missing data, or integration failures. Demos run on the happy path; production does not.