How to run an enterprise AI assistant governance pilot

Expected outcome

The pilot ends with a one-page decision record and a portable evidence package. The decision states the approved workflow, user population, data boundary, allowed actions, required review, measured outcomes, unresolved limitations, operating owners, incident owner, budget, and reevaluation date.

The evidence package contains the frozen configuration, user and role map, data-flow inventory, test cases, run and approval records, failure-drill results, cost model, deletion receipt, export sample, and issue register. A reviewer should be able to distinguish documented vendor capability from a control that was actually configured and verified.

Use the enterprise AI assistant buyer’s guide to create the shortlist. This tutorial begins after a candidate is selected and turns procurement claims into local evidence.

Prerequisites

Select one workflow with clear inputs and a checkable outcome, such as drafting an internal knowledge answer with citations, summarizing a permitted meeting record, or preparing a non-binding support response for human review. Avoid a first pilot that can send payments, modify production, make employment decisions, or disclose high-impact advice.

Name these roles:

Role	Decision right
Business owner	Defines the job and accepts outcome quality
Product owner	Owns workflow, backlog, and user experience
Security owner	Approves identity, tools, secrets, and incident controls
Data owner	Approves fields, processors, region, retention, and deletion
Legal or compliance reviewer	Interprets applicable obligations and contracts
Operations owner	Runs deployment, monitoring, backup, and recovery
Release authority	Makes the final bounded approval decision

Freeze product, model, retrieval, tool, prompt, policy, and deployment versions. Record whether the assistant is managed, self-hosted, or hybrid. “Self-hosted” does not mean every model, search, telemetry, or support dependency stays inside the customer boundary.

Step 1: write a bounded pilot charter

The charter should fit on one page:

pilot:
  workflow: ""
  business_owner: ""
  start_date: ""
  end_date: ""
  users: []
  allowed_data: []
  prohibited_data: []
  allowed_actions: []
  prohibited_actions: []
  required_human_review: ""
  systems_connected: []
  budget_limit: ""
  success_metrics: []
  hard_gates: []
  incident_owner: ""
  release_authority: ""

Avoid goals such as “evaluate AI productivity.” Define a job and a baseline: time to produce a verified result, reviewer correction, citation support, refusal behavior, user accessibility, reliability, and cost per accepted outcome.

The charter expires automatically. Continuing after the end date requires an explicit decision; a forgotten pilot must not become ungoverned production.

Step 2: map identity, data, and tools

Draw the data flow at field level. Include prompts, uploaded files, retrieved passages, embeddings, chat history, feedback, traces, cached results, support access, exports, and backups. For each, record processor, controller, region, encryption, access role, retention, deletion, and whether it may be used for service improvement.

Test identity propagation. Two users with different document permissions should ask the same question. The restricted user must not receive denied content in the answer, model context, citation list, logs, or cache. A shared service credential can widen access even when the interface has SSO.

Inventory tools separately from models. For every integration, document scopes, credential owner, read/write capability, target resources, approval rule, rate limit, idempotency, logging, revocation, and recovery. Start read-only where possible.

Compare self-hosted candidates in the companion LibreChat vs Open WebUI review, but verify the purchased or deployed version: fast-moving projects change authentication, extension, and enterprise boundaries.

Step 3: turn policy into testable controls

For each policy statement, define owner, configured value, test, evidence, and failure response:

Policy intention	Configured control	Test evidence
Only approved users access the assistant	SSO, role mapping, disabled public signup	Allowed and denied login records
Users retrieve only permitted documents	Identity-aware retrieval filters	Two-user denied-source test
Sensitive prompts are not retained indefinitely	Field inventory and retention schedule	Expiry and deletion receipt
Consequential actions require review	Bound approval event	Changed-argument rejection test
Unsafe content does not authorize tools	Deterministic policy layer	Stored and retrieved injection cases
Incidents can be investigated	Correlated, minimized audit events	Trace reconstruction exercise

Do not accept a settings screenshot as the only evidence. Test the behavior through the normal user path and the API or background path. Configuration drift can leave one path unprotected.

The AI governance tooling guide helps assign enforcement points and owners.

Step 4: build the evaluation and review set

Create 30 to 50 tasks from the real workflow. Include routine, edge, unanswerable, permission-denied, adversarial, and recovery cases. Preserve expected evidence and required reviewer disposition.

Measure:

• Outcome accepted without change, with minor edit, after major rewrite, or rejected.
• Material factual and citation errors.
• Reviewer active minutes and escalation time.
• Correct refusal and clarification.
• Permission or policy violations.
• Latency, retries, availability, and abandonment.
• Model, infrastructure, storage, support, and human cost.
• Accessibility and usability for the named user population.

Keep survey sentiment separate from verified results. Users may like speed while reviewers absorb hidden correction work. Report both.

Step 5: run failure and abuse drills

At minimum, test:

1. Revoke a user’s document access and confirm retrieval and cached answers change.
2. Insert an instruction into a permitted document that asks the assistant to ignore policy or call a tool.
3. Interrupt a write after the external action succeeds but before the assistant records completion.
4. Cause a model, search, embedding, or tool timeout.
5. Remove a required document and test unanswerable behavior.
6. Attempt export and deletion using the documented operator path.
7. Rotate a provider or tool credential and confirm old access fails.

Desired outcomes include safe refusal, bounded retry, reconciliation, escalation, and explicit incomplete status. A fallback that silently uses a less governed model or broader credential is a failure.

Link material failures to the AI incident response playbook and add sanitized regression cases before retesting.

Step 6: calculate full operating cost

Calculate cost per verified accepted task, not cost per chat message. Include licenses, model use, embeddings, indexes, storage, observability, egress, hosting, support, integration maintenance, security review, reviewer minutes, failure remediation, backup, and expected exit work.

Model normal, peak, and failure-heavy volumes. A cheaper model that doubles correction time or retry volume may cost more. A self-hosted interface avoids some license costs but creates patching, availability, capacity, and incident ownership.

Record which costs are estimates and which came from the pilot. Recheck public pricing before procurement approval.

Step 7: test export, deletion, and exit

Before the pilot ends, export conversations, configuration, prompts, tool definitions, policies, evaluation cases, feedback, and audit records in documented formats. Load a sample into a fallback workflow and reproduce one answer or review decision.

Delete a synthetic user’s data and trace the request through primary storage, search indexes, vector stores, caches, telemetry, backups, and provider systems covered by contract. Record residual backup retention rather than promising immediate erasure where it is not supported.

An exit test reveals hidden lock-in earlier than contract negotiation. If essential evidence cannot leave the vendor UI, the organization does not fully own its decision history.

Copyable decision record

decision:
  workflow: ""
  pilot_dates: ""
  versions: {}
  evidence_package: ""
  measured_outcomes: {}
  hard_gates:
    identity_and_permissions: pass | fail
    data_and_retention: pass | fail
    action_controls: pass | fail | not_applicable
    incident_and_recovery: pass | fail
    export_and_exit: pass | fail
  accepted_limitations: []
  approved_users: []
  approved_data: []
  approved_actions: []
  required_review: ""
  product_owner: ""
  operations_owner: ""
  incident_owner: ""
  budget: ""
  reevaluate_on: []
  outcome: approve | extend | reject

Require signatures or recorded approvals from the business owner, data owner, security owner, and release authority. Legal or compliance approval depends on the use and jurisdiction; record the actual decision rather than assuming it.

Failure modes

Demo-to-production drift: the pilot quietly adds users, data, or tools. Enforce the charter in access and configuration, not only documentation.

Feature equals control: documentation lists retention or approval, but the chosen plan or path is not configured. Test the implemented behavior.

Shared-credential authorization: retrieval or tools run with broader service access than the user. Propagate identity and test denied content.

Survey-only success: users report time savings while reviewer corrections rise. Measure accepted outcomes and active human work.

Self-hosting optimism: model calls, telemetry, support, and updates still cross external boundaries. Map the complete data and operating flow.

No exit rehearsal: the team discovers unexportable evidence during renewal or incident response. Migrate a sample during the pilot.

Acceptance criteria

Approve only if every hard gate passes, the named workflow improves a verified outcome at an acceptable full cost, and operators complete denial, recovery, deletion, and export tests. Accepted limitations need an owner, monitoring signal, and review date.

The approval must remain bounded to the users, data, actions, model, tools, and review process tested. New connectors, broader datasets, autonomous writes, model changes, retention changes, or a different hosting boundary trigger reevaluation.

Publish the decision record internally with links to evidence. Do not copy sensitive prompts or credentials into the record; use protected identifiers and artifacts.

Next step

If the pilot passes, create a controlled production rollout with canary users, support ownership, monitoring, scheduled evidence review, and rollback criteria. Use the AI procurement checklist for contract and renewal controls, and preserve the pilot dataset as the first production regression gate.