How to Implement AI in Your Legal Department Responsibly: The Practitioner's Go/No-Go Guide

How to Assess AI Readiness Before You Deploy: The Go/No-Go Framework

Most AI implementation failures are governance failures, not technology failures. The tool worked, but the AI use case wasn’t defined clearly enough, the inputs weren’t controlled, or the outputs weren’t reviewed before they caused a problem.

The following five-step framework, adapted from our Go/No-Go Readiness Checklist we co-presented at CLOC, gives legal operations teams a structured way to evaluate any AI use case before deployment.

Step 1: Define the use case explicitly. Identify the specific workflow, the users who will interact with the tool, and the document or data types in scope. Examples: contract review for NDAs over $500K, litigation research for employment matters, invoice review for outside counsel billing. Vague use cases produce vague governance. If you can’t explain the purpose, workflow, and user base in two sentences, the use case isn’t ready.

Step 2: Map your inputs. Identify what data will enter the AI tool and flag sensitive categories upfront — privileged communications, personal data, confidential business information, trade secrets. Confirm those categories are approved for that specific tool. Apply the minimum necessary principle: only include what’s needed, redact where possible.

Step 3: Control your outputs. Define who reviews AI outputs before they are relied on, acted on, filed, sent to a counterparty, or published externally. Every external-facing output — court filing, regulatory submission, client communication — requires a defined, documented approval step. That approval step is what makes the output defensible.

Step 4: Understand your vendor’s data practices. Know where your data is processed and stored, how long it is retained, and how it is deleted. Verify the full subprocessor chain — not just the primary vendor, but every third party they engage to deliver the service. Confirm that contract terms extend confidentiality, data handling, and incident notification obligations through that entire chain.

Step 5: Confirm EU AI Act classification if applicable. If the tool is used in any context involving EU-established individuals — employees, counterparties, or data subjects located in the EU — assess whether it qualifies as a high-risk AI system under Annex III of EU Regulation 2024/1689. If high-risk classification applies, confirm the vendor’s conformity obligations under Articles 9–17 and your own deployer obligations under Article 26, including maintaining logs, ensuring human oversight, and informing affected individuals where required.

Data Quality: AI Is Only as Good as Its Source

Every AI governance framework eventually comes back to data. The sophistication of the model matters less than the quality of the information it draws from. An AI tool operating on stale, inconsistent, or inaccurate data will produce stale, inconsistent, or inaccurate outputs, and those outputs will carry the appearance of authority because they came from an AI system.

Six data quality dimensions apply to every source your AI tools access:

Accurate. Does the data correctly reflect reality? Are the contract terms in your matter management system the actual signed terms?

Complete. Are all required fields populated? Are there gaps that would cause an AI tool to draw incorrect inferences from missing data?

Consistent. Is the same information represented the same way across systems? Inconsistent naming conventions, date formats, and terminology create confusion that propagates through AI outputs.

Up to date. When was this data last reviewed and refreshed? Is there a defined cadence for updates, and is someone accountable for maintaining it?

Rule-compliant. Does the data follow your organization’s defined formats, taxonomies, and governance rules?

No duplicates. Are there conflicting versions of the same record? Duplicate data is one of the most common causes of AI output errors in legal contexts.

For each source your AI tools access — contract management system, matter management platform, billing data, document repository, internal knowledge base — define a source of truth, name an owner, and establish a review schedule. AI should pull only from approved, published, reviewed content. Draft documents, unreviewed summaries, and deprecated pages should be explicitly excluded.

When a major system change occurs (like an upgrade, a field rename, a platform migration), trigger a review to confirm your AI tools are not relying on stale or broken connections.