Choosing AI tools for an in-house legal team: what to test before buying

Choosing AI tools for an in-house legal team: what to test before buying

Before you sign any contract for a legal AI tool, put it through a trial long enough for your lawyers to learn it, work through your real scenarios, and compare notes at the end. The glossy demo always looks good. Your pile of NDAs and vendor amendments is what tells you whether the tool belongs in your workflow — once your data-handling review clears which documents can go in. Choosing legal AI tools is less about features on a spec sheet and more about how the software holds up when a junior lawyer asks it a question it was not scripted for.

Why choosing AI tools for legal work needs more than a product demo

Vendor demos are designed to succeed. The sales engineer picks the contract, the clause, and the question. In your pilot, the contract is one you actually need to review, with typos, footnotes, and terms your industry uses that nobody outside it understands. That gap — between a staged demo and a Thursday deadline — is where most procurement decisions go wrong.

Axiom’s guidance on ai for in-house legal teams outlines an approach built around defined use cases, staff training, governance and structured pilots. These principles provide a starting point for setting evaluation criteria before comparing products. 

A serious test is not meant to confirm the demo. It is meant to break the tool in the specific ways your team will break it, and see what the vendor does about it.

What to test before buying: a practical framework

When choosing legal AI tools, assume every vendor claim needs a test behind it. The honest test runs long enough to cover onboarding, real work, and a proper retrospective — commonly six to eight weeks — and it goes to the lawyers who will actually use the tool. Not a procurement team. Not a champion who already likes the vendor. The people whose daily workload the tool is supposed to reduce.

Before any live document goes in, confirm with your privacy and security leads what the vendor’s data-handling terms allow for the trial. Then assemble a representative sample across the contract types your team handles most often, and give every tester the same prompt instructions agreed in advance plus a short onboarding, so you are comparing the tools rather than each tester’s prompt-writing skill.

Questions worth answering during the trial:

• Does it handle your industry’s language — regulated terms, defined acronyms, jurisdictional quirks — without inventing clauses?
• Can it read a scanned PDF as well as a native one?
• What happens on a 180-page master agreement versus a two-page order form?
• Does it keep its answers consistent across similar documents, or drift?
• How does it flag uncertainty? Silence on a risky clause is worse than a wrong flag.
• Where does the data go — your tenant, the vendor’s shared infrastructure, or a third-party model provider?

If a question above is hard to answer in the first few weeks, that is itself a data point.

Building a shortlist: evaluating legal AI software without vendor spin

Marketing pages are mostly interchangeable, so skip them. Three better sources: in-house peers in your industry, legal operations communities like CLOC and ACC chapters, and independent directories that list tools by category. Public directories will not vet legal tools the way an industry analyst would, but they are useful for scanning what exists and roughly what a seat costs.

When choosing legal AI tools from a crowded market, keep the shortlist small enough that each candidate gets real attention from the testers rather than a sprawling bake-off nobody finishes.

A scoring sheet your team can actually fill in

Give each tester a one-page scoring sheet. Treat the top block as mandatory — if a tool fails one of those checks, it drops out before you look at the scores below.

Resolve mandatory data-handling and security requirements before comparing performance. For each remaining tool, keep the underlying test results, including significant errors and the work needed to correct them. Use those findings to identify which tasks the tool can support and which limitations require further testing.

Legal AI tool selection: questions the sales deck won’t answer

Every vendor will tell you about their model provider, their SOC 2 report, and their roadmap. Fewer will give straight answers on the things that break adoption later.

Who owns the outputs if the tool drafts a clause? What is the vendor’s policy when the underlying model provider changes versions? What happens to your data if you leave? How is pricing recalculated if your headcount changes mid-contract? Is there a usage cap hidden under the “unlimited” plan? Which parts of the API are rate-limited?

Ask these in writing before the pilot ends. Written answers also become part of the diligence record if the tool misbehaves later and you need to show why you picked it.

Rolling the choice out to your in-house team

Once you pick a tool, the next test is adoption. AI tools for legal departments die quietly when nobody uses them, not because of a dramatic rejection. Scope the first deployment to one workflow — say, NDA first review — give it a named owner on your team, and set up a clear escalation path for the moments when the tool stumbles. Pair every lawyer with one other person who already uses the tool well. Hold a short review at month end and decide whether to expand, shrink, or stop.

This is why choosing legal AI tools is partly a buying decision and partly a change-management one — both have to work for the money to show up in next year’s budget line.

What to do after you have chosen AI tools

The decision does not end at signature. Schedule a formal review at the 90-day mark with three numbers in hand: how many matters passed through the tool, how many lawyers used it at least weekly, and what share of outputs needed meaningful human rework. If weekly usage is low by day 90, treat that as a signal to diagnose rather than a verdict — the cause might be the tool itself, the use case, an integration gap, training that did not stick, or trust that has not built yet. Each needs a different response before auto-renewal.

A long-enough trial, a shortlist small enough to test well, a scoring sheet with hard gates before soft averages, and a 90-day usage check — that sequence separates the legal departments quietly getting value from AI from the ones still paying for seats nobody opens.