How Law Firms Should Evaluate AI Tools Before Buying Them

Cory D. Raines
Aug 11
10 min read

Updated: 2 days ago

Law firms have more AI options than they did even a year ago, but that has not necessarily made choosing one easier. Legal AI products now promise faster research, contract analysis, document review, drafting, due diligence, knowledge management, and increasingly, the ability to complete multi-step legal workflows with limited human involvement.

A polished demonstration can make almost any of these products look impressive. The harder question is whether the product will actually improve the work lawyers perform every day.

That distinction matters as firms move beyond experimentation and begin making larger, longer-term investments in AI. Accuracy is obviously important, but it is only one part of the evaluation. Firms also need to consider confidentiality, security, workflow integration, verification, adoption, pricing, and what happens when a product that worked well for twenty lawyers is suddenly being used by hundreds.

Choosing legal AI should ultimately look less like buying software and more like evaluating a new way of performing legal work.

Start With the Legal Work, Not the AI

The first question should be surprisingly simple: what problem is the firm actually trying to solve?

“Improve productivity” is not a particularly useful answer. Reviewing 500 contracts faster is. Reducing the time required to prepare an initial litigation chronology is. Making decades of prior work product easier to search is. Producing a first-pass diligence report in hours rather than days is.

Defining the work this way changes the purchasing process. Instead of comparing products primarily based on feature lists and demonstrations, the firm can evaluate whether a particular system improves a specific legal workflow. It also creates something concrete to measure later.

If the firm cannot identify the work the technology is supposed to improve before buying it, determining whether the investment actually succeeded becomes considerably harder.

This is especially important because the same AI product may be extremely useful in one practice area and offer relatively little value in another. A transactional practice reviewing large volumes of similar agreements has different needs from a litigation team analyzing discovery, and both have different needs from lawyers primarily engaged in counseling or negotiation.

There is no reason the evaluation process should pretend otherwise.

How Law Firms Should Evaluate AI Tools for Accuracy

Legal work presents an unusual problem for AI evaluation because an answer can be mostly correct and still be unusable.

A contract review system that identifies nine significant provisions but misses the tenth may appear highly accurate on paper. If the missing provision materially changes the client's risk, the percentage is not particularly comforting.

The same issue appears in legal research, due diligence, drafting, and document analysis. Firms therefore need to test AI systems against realistic legal assignments rather than relying on vendor demonstrations or generalized benchmarks alone. Give the system the kinds of documents the firm's lawyers actually encounter. Compare the output against experienced attorney review. More importantly, pay attention to what the system misses and whether those failures follow a pattern.

The objective is not to prove that the technology works. By now, we know that modern AI systems can perform impressive legal work. The more useful objective is understanding when the system works reliably, where it struggles, and how much attorney review is still required before its output can be trusted.

This is also why firms should be cautious about reducing AI performance to a single accuracy score. A system that performs exceptionally well on routine provisions but struggles with unusual contractual structures may still require significant review in the matters where the lawyer's judgment matters most.

The nature of the errors matters just as much as the number of errors.

Confidentiality Has to Be Part of the Buying Decision

Law firms possess some of the most sensitive information a client can provide. Before attorneys begin uploading that information into an AI platform, the firm needs to understand what happens to it.

That means looking beyond whether the vendor says its product is “secure.” Firms should understand how information is stored, how long it is retained, whether customer data can be used to train or improve models, who can access it, what third parties are involved in processing it, and what happens to the information when it is deleted.

The distinction between consumer and enterprise AI products matters here as well. Two versions of the same underlying technology may have materially different contractual protections, retention practices, administrative controls, and data-use policies.

These are not questions that should be addressed after lawyers have already incorporated the product into client work.

I discussed the confidentiality issues in greater detail in AI and Attorney-Client Confidentiality: What Lawyers Should Understand Before Using AI Tools.

Evaluate the System, Not Just the Model

A lot of attention in AI still goes to which underlying model is the most capable. That comparison matters, but it can also be misleading when evaluating legal technology.

The model is only one component of the system a lawyer ultimately uses. Retrieval, document processing, prompts, integrations, workflow design, source material, evaluation methods, and other technology surrounding the model can substantially affect the quality of the final output.

For the lawyer using the product, the underlying model may ultimately matter less than whether the entire system reliably completes the work.

That means testing the actual workflow from beginning to end. Can lawyers easily provide the documents they need analyzed? Can the system handle the size and complexity of the matters the firm works on? Does it identify the sources supporting its conclusions? Can an attorney verify an answer without effectively recreating the entire assignment? Can the product work with the document management and knowledge systems the firm already uses?

A technically impressive model inside a poor workflow can still be a bad product.

This is one reason legal AI evaluations should move beyond simply asking which model a vendor uses. Firms are purchasing the performance of the complete system, not access to a model in isolation.

Verification Should Be Designed Into the Workflow

AI verification is often discussed as though it is something a lawyer does after receiving an answer. That is only part of the issue.

A well-designed legal AI workflow should make verification easier.

If a system summarizes a lengthy agreement, can the lawyer quickly reach the provisions supporting the summary? If it performs legal research, can the attorney identify and review the underlying authorities? If it analyzes hundreds of documents during diligence, can counsel trace a significant conclusion back to the document that produced it?

The harder an AI product makes it to verify its own work, the less valuable some of its apparent efficiency becomes.

This is particularly important because AI errors are not always obvious. A fabricated citation may eventually be caught by someone checking the authority. A plausible but incomplete summary or subtly incorrect interpretation may be much harder to identify.

The risks associated with relying on AI-generated legal work are discussed further in Risks of Using AI in Legal Work: What Lawyers and Law Firms Should Know and AI Hallucinations in Court: What Lawyers Can Learn from Recent Sanctions and Citation Errors.

Look at What AI Will Cost After Adoption

AI pricing deserves more attention than it gets during the purchasing process.

The initial numbers are usually easy to understand. A firm sees the annual subscription price, the number of licenses, and perhaps the cost per lawyer. What happens once people actually begin using the technology can be more complicated.

Different products may price access by user, usage, credits, tokens, particular functions, or some combination of those approaches. Even where the pricing structure is relatively simple today, firms should understand how the economics change as adoption increases.

This distinction may not matter much during a limited pilot. It can matter considerably if a product eventually becomes part of everyday work across multiple practice groups.

Before entering a long-term agreement, firms should model what the product costs under successful adoption, not merely what it costs during the trial period.

What happens if usage doubles? What happens if it increases tenfold? Are there limits that attorneys are likely to reach? Are certain capabilities priced separately? Does increased automation reduce costs elsewhere in the workflow, or does it simply add another technology expense?

A successful AI implementation should eventually create economic value somewhere. Firms need to understand where that value is supposed to come from and whether the pricing model allows them to capture it.

Determine Who Owns the AI-Assisted Work

Every legal AI workflow should eventually reach a human being who owns the result.

That responsibility cannot belong to “the AI.”

If a system drafts a contract, someone needs to review it. If it summarizes discovery, someone needs to understand the limitations of that summary. If an AI system begins completing several steps of a workflow autonomously, the firm still needs to determine when a lawyer must intervene and who has authority to approve, override, escalate, or stop the process.

These questions become more important as AI systems move from generating answers to taking actions.

Traditional legal workflows usually have fairly recognizable lines of responsibility. Associates prepare work. Senior attorneys review it. Partners ultimately answer to clients. AI can complicate that structure because portions of the work may happen automatically and at a scale that makes traditional review more difficult.

Firms should decide how responsibility works before the technology becomes embedded in everyday practice.

Run a Pilot That Resembles Real Legal Work

A pilot should be designed to discover weaknesses, not confirm a purchasing decision that has effectively already been made.

I would rather see a firm test a product deeply with twenty lawyers than immediately distribute it to two hundred. The smaller group can work with real assignments, difficult documents, different practice areas, and matters where the firm already knows what strong work product should look like.

Then the firm can measure what actually happened.

Did the tool save meaningful time? What did attorneys have to correct? What types of errors appeared repeatedly? Did lawyers trust the output too much or too little? Did the product improve the quality of the work? Did attorneys continue using it after the novelty wore off?

Those questions tell the firm much more than the number of activated accounts.

Usage is useful information, but usage is not the same thing as value.

For firms still determining where AI fits into legal work generally, How AI Is Actually Used in Legal Practice provides a broader look at the types of work where the technology is already being applied.

Adoption Cannot Be an Afterthought

A technically excellent product that lawyers refuse to use has very little value.

Firms should pay attention to why attorneys adopt some technologies and abandon others. Sometimes the issue is training. Sometimes the product interrupts an established workflow. Sometimes lawyers do not trust it. And sometimes the firm has purchased a sophisticated solution to a problem its lawyers did not consider particularly important in the first place.

AI implementation therefore cannot end when procurement signs the contract.

If a firm expects lawyers to change how they work, it needs to show them what the new workflow looks like, where AI fits into it, what remains their responsibility, and why the new approach is actually better.

This becomes even more important as firms move from optional AI tools toward systems embedded directly into legal workflows. At that point, adoption is no longer simply a question of whether attorneys know how to write prompts. It becomes an organizational question involving processes, incentives, training, supervision, and expectations.

The Best Legal AI Tool Depends on the Firm

I do not think there will be one legal AI platform that is objectively best for every law firm.

A litigation boutique, global transactional firm, plaintiff's practice, and corporate legal department may have completely different requirements. Their data is different. Their workflows are different. Their clients are different. Their economics and tolerance for risk may be different as well.

Those differences should drive the purchasing decision.

The better question is whether a particular AI system performs the work your lawyers actually need, under conditions your firm can trust, at a cost that continues to make sense when adoption grows.

That is harder to answer than asking which product has the most features or which model currently sits at the top of a benchmark. It requires firms to understand their own work before evaluating the technology intended to change it.

But that is also what makes the evaluation useful.

Legal AI is developing too quickly for firms to assume that today's leading product will necessarily remain tomorrow's leading product. Models will improve. Prices will change. New competitors will emerge. Capabilities that appear extraordinary today may become standard features surprisingly quickly.

The firms best positioned for that environment will not simply become better at buying AI.

They will become better at evaluating it.

Key Takeaways

Law firms evaluating AI tools should begin with the legal work they want to improve rather than the technology itself. Products should be tested against realistic assignments, with particular attention paid to the significance and patterns of errors rather than a single accuracy percentage.

Firms should also evaluate confidentiality, security, data retention, verification, workflow integration, pricing at scale, and human responsibility before deployment. A real pilot should measure outcomes rather than simply usage, and adoption should be treated as part of implementation rather than something expected to happen automatically after purchasing the product.

Ultimately, the best legal AI system is not necessarily the one with the most features or even the most capable underlying model. It is the one that reliably improves the firm's actual legal work without introducing costs or risks that outweigh the benefit.

Frequently Asked Questions

How should a law firm evaluate an AI tool?

A law firm should begin by identifying the specific legal work it wants the technology to improve and then test the product against realistic assignments. The evaluation should consider accuracy, confidentiality, security, workflow integration, verification, pricing, usability, and the amount of attorney review required.

What questions should law firms ask legal AI vendors?

Firms should ask how client information is stored and retained, whether customer data is used for model training or improvement, what security and access controls exist, which third parties may process information, how outputs can be verified, what integrations are available, and how pricing changes as usage increases.

Should law firms use general AI or legal-specific AI tools?

There is no universal answer. General-purpose AI models can perform well on many legal tasks, while legal-specific platforms may provide valuable legal content, workflow features, integrations, security controls, and verification capabilities. Firms should compare actual performance on their own legal work rather than assuming either category is inherently superior.

Can lawyers rely on AI-generated legal work?

AI can assist lawyers with research, drafting, review, analysis, and other legal tasks, but attorneys still need to exercise professional judgment and appropriately review AI-assisted work. The necessary level of review will depend on the task, the reliability of the system, and the consequences of an error.

What is the biggest mistake law firms make when buying AI?

One of the most significant mistakes is choosing a product before clearly defining the problem it is supposed to solve. An impressive AI system does not automatically create a useful legal workflow. Firms should understand what they want to improve and how they will measure that improvement before making a significant investment.