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Anything that keeps users or sponsors from using the deployment in normal work: trust, data quality, UX friction, unclear ownership, policy, incentives, or a missing integration. Usually not accuracy.

Getting users, operators, and sponsors to rely on AI in real work (not just approving a pilot and forgetting it).

Reducing repetitive human steps while preserving the judgment, controls, and accountability that still need a person.

The staged release of a new or changed workflow to users, teams, regions, or business units.

A workflow that coordinates more than one agent or specialized model role, usually with explicit routing, tool access, and handoff rules between them.

A workflow where an AI agent plans and performs multi-step work using tools, context, and controls (rather than just returning a chat response).

A workflow where humans and AI agents share the work, with clear ownership, review points, escalation paths, and audit trails for when each side holds the ball.

The day-to-day workflow followed by frontline operators, analysts, reviewers, or support staff who will actually use the deployed system.

The actual sequence of work users follow to make decisions, handle exceptions, and complete tasks — often different from what's documented.

A workflow where failures affect revenue, service levels, compliance, safety, or executive attention. The FDE treats it like production from day one.

A customer deployment strong enough to be used as evidence for other customers, prospects, or internal investment decisions.

A customer who works closely with the product or field team to shape a capability before it is broadly available — accepting rough edges in exchange for influence over the roadmap.

The person accountable for how a workflow runs in production: decisions, exceptions, user adoption, and success metrics. An FDE should not launch without one.

A customer-facing technical partner who has earned enough trust to challenge the customer's assumptions, explain tradeoffs honestly, and guide the deployment toward production rather than toward a good demo.

A map of the customer leaders who can fund, unblock, mandate, or kill the deployment — and what each one actually cares about.

Designing the solution with the customer's users and technical owners while the workflow is still being built — so the product fits the real operation, not an imagined one.

A specific operational friction or failure painful enough that the customer will change workflow, assign owners, and support engineering work to fix it.

A focused demonstration of model behavior on customer-relevant examples to test value, expose gaps, and decide what has to change before deployment. Not a sales deck — a working diagnostic.

A Palantir-style intensive workshop where FDEs and customer teams identify use cases, build working AI workflows on AIP, and define the path to production — often over a few days onsite.

A short engagement designed to prove value on realistic customer data and workflows — not just show that the product can demo well. Ends with a launch plan, not a prettier prototype.