9 Tests to Run Before Your Law Firm Buys Legal AI

Buying legal AI should feel less like watching a demo and more like testing a new hire. Give the tool real matters. Check whether it respects existing permissions. Look closely at what it misses, not just what it gets right. Then decide who will review its work, where the output belongs, and how your firm will know the tool is helping.

That protects you from a common buying mistake: choosing a product that looks impressive in a controlled presentation and creates friction in daily practice.

What should a law firm test before buying legal AI?

1. Test the job, not the feature

Start with a specific job your team already does: review a medical record set, summarize a matter, draft a first-pass chronology, classify incoming documents, or find facts across a case file. Write down what a good result looks like before anyone sees the demo.

“We want AI” is not a useful requirement. “We want a paralegal to produce a review-ready chronology in less time, without losing source references” is. The second statement gives you something you can test, measure, and improve.

2. Use representative matters

A vendor’s sample matter is designed to make the product easy to understand. Your matters are not. Build a small evaluation set that reflects the documents, practice areas, languages, formats, and messy exceptions your team actually encounters.

Include an ordinary matter, a complex one, and a case that caused trouble before. Protect sensitive information as your policies require. The goal is to learn whether the tool handles your version of the work.

3. Measure completeness, not just polished answers

A confident-looking answer can still omit the fact that changes the conclusion. Filevine’s discussion of legal-AI evaluation makes this point directly: averages can hide weak performance on important slices of the work, and completeness matters when the cost of a missed item is high. Its evaluation approach also emphasizes expert review and feedback from production use.

Ask reviewers to mark what the tool missed, where it overreached, and which outputs required substantial correction. A short answer that cites every source may be more useful than a smooth paragraph that cannot show its work.

4. Check whether every important output is traceable

Your team should be able to move from an AI-generated statement back to the supporting document, page, message, or record. Test citations on the exact tasks you plan to automate. Open the cited source and confirm that it supports the claim.

Traceability helps a lawyer review the result and shows where the workflow needs a stop. If a tool cannot reliably point back to source material for a high-stakes task, narrow the task or keep it human-led.

5. Confirm that permissions follow the matter

Do not treat AI access as a separate security system. Clio’s recent guidance argues that legal AI should inherit the matter permissions a firm already uses. That is the right operating principle: a person should not gain access through an AI interface to content they could not open in the source system.

Test a normal user, a restricted user, a departed employee, and a matter whose access recently changed. Ask what happens to generated answers, saved conversations, indexes, and exports after permissions change. The answer should be understandable to the people responsible for the matter, not only to an IT specialist.

6. Watch what happens when the tool is uncertain

Give the system incomplete, conflicting, badly scanned, or ambiguous material. Then watch its behavior. Does it state a limitation, ask for more context, cite the conflict, or simply produce an answer?

Your workflow needs a clear response to uncertainty: stop, flag, route, or require review. “The model will usually get it right” is not a control.

7. Map the output into the next real step

An AI result is only useful if it reaches the person and system that need it. Decide whether the output becomes a draft, a task, a note, a structured field, a document, or a review queue. Define who owns the next step and what must be approved before anything moves forward.

This is where promising pilots often slow down. Our guide to why legal AI pilots stall explains why workflow design matters as much as model quality. A good tool dropped into a vague process still creates vague work.

8. Test adoption with the people who will use it

NetDocuments warns that AI adoption can stall when firms give people access without approved tools, real-workflow training, managed content, ownership, and measurement. Shadow AI is often a sign that the official path is unclear or harder than the unofficial one.

Put the tool in front of the lawyers and staff who do the job today. Ask them to complete the full workflow, not just try one prompt. Notice where they leave the tool, re-enter information, copy and paste, or keep a parallel document. Those workarounds are implementation requirements.

9. Build a business case around outcomes you can observe

Price matters, but “hours saved” is rarely enough. MyCase recommends evaluating need, workflow fit, security, provider reputation, price, return, and the value of a trial or demo. Translate those factors into an operating case your firm can revisit.

Choose a small set of measures: turnaround time, review effort, rework, missing information, adoption, or time between a trigger and the next completed step. Compare the new workflow with the old one. A product that saves drafting time but adds review and cleanup may still be useful, but you need the whole picture.

Tepconic’s view: buy the workflow, not the demo

The strongest legal-AI products are improving quickly. That makes disciplined evaluation more important, not less. Your firm is not buying a model in isolation. You are buying a workflow made up of source data, permissions, prompts or configuration, review rules, integrations, training, ownership, and measurement.

Tepconic’s judgment is simple: do not approve a firm-wide rollout until the tool has passed a small, representative evaluation in the environment where your people will use it. A vendor benchmark can establish credibility. Your own test establishes fit.

If you need help designing that evaluation, Tepconic can map the workflow, test the integration, and build the controls around your legal AI tools. You can also talk with Tepconic about an AI evaluation for your firm before a purchase or rollout.

Frequently asked questions

How should a law firm evaluate legal AI?

Start with one defined job, test it on representative matters, measure completeness and traceability, confirm permissions, and run the full workflow with the people who will use it. Evaluate the operational result, not only the generated answer.

What is the most important legal-AI security test?

Confirm that the AI follows the same matter-level access rules as the source system. Test restricted users and permission changes, then ask how saved outputs, indexes, and exports behave when access changes.

Should a law firm rely on a vendor’s AI benchmark?

A benchmark can help you understand a product’s general capability, but it cannot prove fit for your matters, data, review standards, and workflow. Use it as supporting evidence, then run a smaller evaluation with your own representative work.

Can Tepconic help us choose and implement legal AI?

Yes. Tepconic can help define the use case, design an evaluation set, assess workflow and integration requirements, build review and permission controls, and plan adoption. Contact Tepconic to scope a legal-AI evaluation for your firm.

Sources

Filevine: Finding Ground Truth: How We Evaluate Legal AI at Filevine. Clio: Your Legal AI Should Inherit Your Matter Permissions. NetDocuments: Why Legal AI Adoption Stalls Before It Starts. MyCase: AI in Law: A Guide for Legal Professionals. Partner material is used as a market signal; the evaluation framework above is Tepconic’s interpretation.