Enterprise Legal AI Deployment: A Rollout Checklist

Enterprise legal AI deployment should move beyond a pilot only when legal, IT, and security can show that the workflow meets agreed requirements for access, data handling, output quality, and approval. That means testing access, reviewing representative outputs, documenting exceptions, and assigning clear owners before the team expands use.

Use a deployment acceptance record to keep each test, owner, supporting evidence, and decision in one place. A successful login or persuasive demonstration does not show that the workflow is ready for wider use. This checklist focuses on the gate from an approved pilot to controlled expansion, not the broader change-management work that follows.

GC AI is the enterprise legal AI platform for in-house teams, used by 2,100+ legal teams across 45+ countries as of October 2026. Our Agent Connectors connect legal work with context from email, files, and other business applications, making a negotiation workflow a useful pilot: retrieve approved context, prepare findings, have counsel review them, and control the resulting action.

Although the acceptance-record framework can be adapted to other legal AI tools, the product-specific checks and worked example below use GC AI.

If your team is still selecting a platform, start with our guide to enterprise legal AI. The checklist below begins after that selection.

Define Your Enterprise Legal AI Deployment Plan, Owners, and Acceptance Record

Start with one legal workflow you can test repeatedly from the same kinds of inputs to a reviewable output.

A pilot framed only as “use AI for commercial work” is too broad to show what passed, what failed, or whether the workflow is ready to expand.

For example, the team might test whether GC AI can prepare an issue summary and draft response from an approved agreement, negotiation thread, and written positions. Use the same approved inputs and review standard each time so the test is repeatable. For help choosing the task, use the AI for legal operations guide.

Legal operations should record the participating users, permitted data, source systems, required reviewer, and excluded matters. You can also include representative variations: incomplete instructions, conflicting document versions, and a request that exceeds the user's authority.

Before the pilot begins, assign each approval decision to a named owner so failures, exceptions, and escalation paths are clear. The supervising lawyer owns the output standard. IT owns identity and application setup. Security owns data-handling questions. The general counsel or designated sponsor approves expansion after those owners report their findings.

Create a Deployment Acceptance Record

Use the control table below to plan the pilot, and record the environment and configuration alongside each result.

Control and Owner	Evidence to Collect	Test to Record
Scope: legal operations	Approved task, inputs, exclusions, and reviewer	Run an included task and an excluded request; check the escalation route.
Data handling: security	Applicable terms, approved data categories, and unresolved questions	Trace a sample input through its source, processing, storage, and output.
Identity: IT	Identity-provider configuration and test identities	Confirm permitted sign-in and the expected rejection of an unauthorized identity.
Permissions: organization admin	Roles, access tiers, group membership, and sharing settings	Compare access to a shared resource and a restricted resource.
Connectors: admin and source-system owner	Enabled connections and permitted actions	Test approved retrieval, blocked actions, and configured approval behavior.
Output: supervising lawyer	Reference answer, generated output, and reviewed version	Record missed issues, unsupported statements, corrections, and completion time.
Exit: IT and security	Removal procedure, dependencies, and support contacts	Test access revocation and document what remains to resolve.

For each individual test, use the record below to capture the result, evidence, exception, approval, and recheck. Keep each result separate so that a passed login check cannot obscure an unresolved permissions failure.

Record Field	What to Capture
Control and scope	Workflow, application, account type, data, and configuration tested
Owner	Person running the test and resolving failures
Evidence	Links to configuration records, source versions, and permitted screenshots
Test and result	Test date; expected and observed behavior; pass, fail, or unresolved
Exception and decision	Remaining issue; affected users; temporary restriction; expiry; decision
Approval	Approver; approval date; permitted next stage
Recheck	Trigger; due date; responsible person

Keep sensitive test material in its approved location. The acceptance record can reference that material without becoming another repository for confidential agreements.

Turn Legal AI Security Review Into Deployment Evidence

A security review becomes useful for deployment when the team turns vendor documentation and unresolved questions into specific acceptance tests. Bring the applicable vendor agreement, security review, and unresolved procurement questions into the pilot. Confirm which data categories the team approved and whether the proposed workflow introduces a new source system, recipient, or storage location.

Our security page documents SOC 2 Type II, encryption at rest and in transit, and our commitment that LLM providers do not train on customer data. We maintain zero-data-retention agreements with LLM providers wherever feasible. Application data is stored, so provider retention and application retention require separate review.

Security should trace a sample workflow: where the agreement begins, what the platform receives, where the reviewed output goes, and which terms govern retention and deletion. Identify any unanswered question before permitting the affected data or connection.

An exception needs an owner and a limit. For example, the team might approve testing with a restricted sample while a question about another data category remains open. Record who accepted that condition, which work it permits, and what must happen before the exception expires.

Vendor documentation supplies evidence about the service. Your completed record establishes what your organization reviewed and approved for this deployment.

Test SSO and Permissions With Different Users

Before expanding access, test both who can sign in and what each user can see or do after signing in. Single sign-on establishes how a person authenticates. Content and product permissions determine what that person can do after signing in.

To configure sign-in, use our SSO setup guide, which covers SAML and OpenID Connect through WorkOS. Setup requires the relevant administrative access, domain verification, and identity-provider configuration. The connection test checks communication between systems; it does not establish every account-lifecycle or access behavior.

Use test accounts approved by IT to confirm that an authorized user can sign in, an unauthorized user is rejected, and the recovery process works when sign-in fails. Record the exact condition tested.

Then compare users with different permissions. Our organization permissions guide explains how organization role, access tier, product roles, and resource sharing combine. Direct and group permissions can accumulate, so inspect every grant that applies. Custom-role availability can vary by organization.

Meaningful negative tests include:

• A user without a resource share cannot open the restricted resource.
• A user with view access cannot perform an edit reserved for another permission level.
• Removing one grant produces the expected result after accounting for remaining group or direct access.
• A member cannot perform an administrative action reserved for admins.

Record unexpected access as a failed test with the relevant configuration. Recheck after correction.

If the pilot includes GC AI for Word, our Microsoft Word add-in for reviewing and revising documents, assign the Microsoft 365 admin a separate setup check. Use the admin requirements to verify deployment and access in the environments your lawyers use.

Enable Connectors Around One Complete Workflow

To test connected workflows safely, start with only the systems and actions the approved pilot requires. Our Agent Connectors operate at organization, member, and action levels. Connectors are enabled at the organization level by default, but each member must authorize an account. Microsoft 365 connectors can require one-time tenant admin consent. Source-system permissions still apply.

For most connectors, admins can choose Disabled, Read-only, or Full access. Start approved pilot connectors at Read-only. Confirm retrieval and access boundaries before enabling the write actions the workflow needs.

Write and delete actions default to Needs approval. Within the organization's limits, members can set an action to Always allow, Needs approval, or Block. For the pilot, confirm which setting applies to each action and test it with controlled accounts before sending messages or changing records.

Review existing connectors individually. Turning off Enable new integrations by default makes connectors added later start Disabled; it does not change existing connector policies.

Our Document Storage feature syncs and indexes files as persistent sources for GC AI’s Contract Intelligence, our product for analyzing agreements. Contract Intelligence lets an agent read, extract, and answer questions across connected contracts with citations; it can reconcile amendments, statements of work, and renewals into a contract family and return structured terms in a sortable view. Document Storage and Agent Connectors have separate access policies, although both can use the same member account connection. Before changing either policy, record which synced sources depend on that connection.

Run an Illustrative Negotiation Test

An end-to-end negotiation workflow lets the team test retrieval, lawyer review, and controlled action together rather than validating each capability in isolation. Use an agreement approved for testing, a corresponding email thread, and the legal team's written negotiating positions. Include a version label or date for each source. Use controlled accounts and a designated test recipient.

The worked example below uses GC AI's Chat 2.0 multi-agent architecture within the core chat experience. Specialized research and analysis processes can run in parallel, reconcile findings, and return a structured result grounded in the approved sources. The lawyer checks the cited material, resolves ambiguity, and approves any external action. Agent Connectors handle account-authorized retrieval and actions; they are a separate control layer.

1. Have the participating lawyer connect the approved accounts. Ask GC AI to find the specified agreement and email thread. The lawyer confirms the correct counterparty, document version, and correspondence before continuing.

2. Give GC AI the approved positions, then request an issue summary with an instruction such as:

   Example prompt

   Compare the counterparty's latest requests with this agreement and our approved negotiating positions. For each open issue, identify the relevant clause or message, summarize the requested change, and state where it falls outside our positions. Flag missing information and conflicting versions. Do not infer approval from silence.

3. The lawyer checks the cited material against the sources, identifies omissions, and decides which positions to accept, reject, or escalate. Record corrections separately from ordinary stylistic preferences.

4. Ask GC AI to prepare an email using the lawyer's decisions. Review the proposed commitments, recipients, attachments, and tone. An accurate issue summary does not establish that the resulting response is ready to send.

5. If sending is in scope, use the designated test recipient and verify the configured approval behavior. Otherwise, keep the output as a draft. Save the reviewed response, source references, and approved instructions in the team's permitted location for the next test.

Repeat the workflow with a missing attachment or conflicting instruction to see whether the reviewer receives enough information to intervene.

Measure Reviewed Quality, Rework, and Time

Before evaluating the pilot's results, select completed matters or approved samples that reflect the proposed rollout. For each test, have the supervising lawyer prepare a reference answer and record the issues the output must identify, including exceptions that could change the advice or the recipient's next action. Specify which errors would prevent the output from being used.

Track four measures together:

• Quality: Material issues identified, unsupported assertions, source errors, and departures from approved positions.
• Rework: Corrections needed before the deliverable is usable, separated into legal, factual, and stylistic changes.
• Time: Preparation, AI interaction, verification, corrections, and finalization.
• Repeat use: Whether participants return to the approved workflow and complete useful work after initial training.

Record how many tasks were reviewed and how many passed, failed, or remained unresolved. “Most outputs passed” is hard to interpret without the task count, review standard, and failure criteria. Record material misses separately so that a serious error cannot disappear inside an average.

Compare similar work with and without AI under the same review standard. Include the time spent fixing outputs. Keep task complexity and reviewer differences visible rather than attributing every change to the platform.

Our organization usage metrics can help identify activity patterns. The time-savings figures are estimates. Use your observed task times to assess the pilot's results.

Zscaler, an enterprise cloud-security company, ran a 90-day GC AI pilot with its legal team. After reviewing usage and listening to the team, Director of Legal Operations Amanda Cincotta made adoption the team’s top priority. She said the pilot made adoption “not optional anymore” and “priority number one.” The example shows why usage data and qualitative feedback belong in the same expansion decision.

Ask participants why they stopped using a workflow. Missing source access, unclear instructions, and extensive corrections call for different remedies. Usage alone cannot identify which one applies.

Decide Whether to Expand, Remediate, or Stop

Give the sponsor the completed acceptance record, reviewed outputs, and unresolved exceptions. Decide by workflow and user group; approval for one task should not silently extend to every legal matter or connected application.

Use three decisions:

1. Expand: Required tests passed, reviewed outputs meet the agreed standard, and owners can support the next group.
2. Remediate: A specific issue has an owner, a temporary restriction, and a defined retest.
3. Stop: An access or output failure prevents approved use, or the team cannot resolve a material condition.

Prepare the support route before inviting more users. Name a legal contact for output questions, an IT contact for sign-in and application failures, and a security contact for suspected exposure. Give users examples of what to report and where to preserve the relevant evidence.

Recheck after changes to identity configuration, roles, sharing, connectors, approved data, or workflow instructions. Add a scheduled review date for exceptions that could otherwise remain open indefinitely.

Plan interruption and exit separately from correcting completed work. Disabling an Agent Connector revokes affected personal connections and can remove dependent Contract Intelligence sources. Review those dependencies first when circumstances permit. Revoking access does not reverse an email that was sent or another completed external action.

For a stopped workflow, assign someone to investigate affected outputs and coordinate any necessary correction. Record retention and deletion requests under the applicable terms.

Test the Workflow Before You Expand It

The acceptance record brings the rollout decision back to evidence: who had access, what data and systems were used, whether reviewed outputs met the agreed standard, how controlled actions behaved, and which exceptions remain open. The goal is not a flawless demonstration. The acceptance record should provide enough evidence to show whether this specific workflow can expand, needs remediation, or should stop.

To run the same kind of test in GC AI, start with one approved workflow and the documents, permissions, and reviewers it requires. Use the acceptance record above to track what passes, what needs remediation, and what should remain out of scope.