Enterprise AI pilot readiness check

Why now

Enterprise interest in AI pilots is high, but many launches still fail to convert into durable expansions.

• The common failure is governance and operating-model drift, not model capability alone.
• A readiness check creates a pre-commitment contract: what must be true before launch, what triggers hold, and what forces rollback.

What breaks without this

Pilots launch with ambiguous success criteria, so every stakeholder interprets outcomes differently.

• Unowned policy decisions create hidden legal and trust risk in production-like workflows.
• Teams continue shipping despite early warning signals because stop conditions were never declared.

Decision framework

Readiness axis 1: measurable business value tied to buyer outcomes.

• Readiness axis 2: governance controls (risk bands, escalation lanes, rollback path).
• Readiness axis 3: operating cadence (weekly scorecard with clear owners and actions).

Recommended path

Start with one narrow workflow where error cost is bounded and telemetry is reliable.

• Require citation-faithfulness, policy checks, and abstain behavior before broader autonomy.
• Scale by passing gates across multiple weeks, then expand scope in controlled increments.

Implementation sequence

Weeks 1-2: baseline manual process and define launch/hold/rollback thresholds.

• Weeks 3-6: run pilot with mandatory rationale payloads and incident-ready escalation lanes.
• Weeks 7+: evaluate expansion only if quality, safety, and economic metrics remain stable.

Tradeoffs and counterarguments

Hard gating can feel slower at first, but it prevents expensive post-launch reversals.

• Some teams prefer speed over controls; that trade usually increases incident and trust-repair cost.
• The practical compromise is constrained launch with explicit hold/rollback authority.