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Legal AI is most useful when it supports a clearly defined task and its output can be checked against reliable sources or the underlying documents. In-house teams should choose tools by testing them on representative work, reviewing how they handle confidential information, and setting human-review requirements that reflect the consequences of an error. The available evidence supports that selection approach—not a defensible ranking of vendors.
What legal AI can help in-house teams do
Generative AI may assist with legal research, contract review, due diligence, document review, regulatory compliance, and drafting. Those are possible uses, not guarantees that a particular product performs them reliably. A tool that is useful for summarizing a set of documents, for example, may not be suitable for extracting every relevant clause or producing research that can safely support advice.
The American Bar Association’s Formal Opinion 512, issued July 29, 2024, is U.S. professional-responsibility guidance. It says lawyers using generative AI must consider their duties, including competence, confidentiality, communication with clients, supervision, candor toward tribunals, and reasonable fees. The opinion does not replace jurisdiction-specific rules, client obligations, or company policy.
Define the job before comparing tools
Start with a specific workflow, not a broad requirement to “use AI.” Write down what the tool is expected to produce, which materials it may use, who will rely on the result, and what a mistake could cost. That definition determines which candidates and safeguards are relevant.
- Task and jurisdiction: Identify the legal work and jurisdictions involved. Test the tool only for the scope you intend to use.
- Inputs and outputs: Specify the documents or questions the tool receives and the form of answer expected, such as a clause inventory, summary, draft, or research response.
- Success criteria: Decide how you will judge accuracy and completeness, and how a reviewer can trace an answer to the source material.
- Error impact: Consider who might be harmed, what decision could be affected, and whether an error could reach a client, counterparty, regulator, or tribunal.
These are practical evaluation criteria derived from ABA guidance and discussion; they are not a standardized industry scorecard.
Compare candidates on representative work
A product demonstration is not enough to show how a tool handles your team’s documents or constraints. Run a controlled pilot on representative tasks and compare outputs with work checked by qualified reviewers. Include routine examples as well as difficult ones: incomplete records, ambiguous language, multiple documents, or questions that require a source to support the answer.
| Evaluation area | What to examine | Useful pilot evidence |
|---|---|---|
| Task and jurisdiction fit | Whether the tool addresses the defined workflow and the relevant legal context. | Results on the specific task and jurisdiction the team plans to use, rather than a general demonstration. |
| Accuracy and completeness | Whether material facts, clauses, issues, or requested fields are found and represented correctly. | A reviewer’s comparison of outputs with the source documents, including omissions and incorrect results. |
| Traceability | Whether reviewers can locate the source for an answer and check that it supports the claim. | How easily a reviewer can verify citations, quotations, extracted fields, or document references. |
| Data handling and access | What information is processed, who can access it, whether third parties receive it, and whether it is retained or reused. | Answers supported by vendor documentation, contract terms, and the organization’s technical review—not a marketing claim alone. |
| Workflow fit | How the tool fits the team’s existing processes and where its output enters the matter or approval chain. | Whether intended users can complete the task with the proposed review and escalation controls. |
| Review and escalation | What human checks are required and what happens when output is uncertain, incomplete, or consequential. | A workable reviewer procedure tested alongside the tool, not assumed after deployment. |
Score candidates against the same criteria, but do not let a single aggregate score conceal a critical weakness. Strong performance on one task does not establish fitness for another, and favorable output quality cannot compensate for data practices the organization cannot accept.
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Human review should be designed around both the task and the harm an error could cause. The ABA’s Formal Opinion 512 emphasizes lawyers’ continuing responsibility to understand relevant capabilities and limitations. Its 2026 workflow discussion likewise describes verification checkpoints for complex extraction and multi-document work.
Rank #3
| Example use | Review approach |
|---|---|
| Drafting or summarizing for internal orientation | Check material statements against the underlying documents before relying on them; do not treat a polished answer as proof of correctness. |
| Contract review or multi-document extraction | Verify extracted clauses and fields against the source documents, and check whether relevant material was omitted. |
| Legal research supporting advice | Verify authorities, quotations, and propositions against authoritative sources and assess whether the research answers the actual question. |
| External filings or other consequential work | Require meaningful lawyer review of the complete work product before it is used or submitted. |
These are risk-based operating recommendations, not claims that any tool can meet a particular accuracy threshold. The ABA’s 2026 article reports a Vals Legal AI Report evaluation in which none of the tested tools found all three relevant disclaimer clauses. It also reports that the best-performing tool accurately extracted 62 percent of fields in a multi-document extraction task. That percentage describes the reported task evaluation only; it is not a general measure of legal AI accuracy. The underlying VLAIR report should be consulted for its methods and context before drawing further conclusions.
Review confidentiality and security for the actual use
Whether particular information may be used with a tool depends on the client, matter, task, and tool—not simply on whether a product is described as secure. Before approving a workflow, map what information enters the system, where it goes, who can access it, whether it is shared with third parties, and how it is retained or reused.
Rank #4
- Review vendor practices, contractual terms, and applicable client or organizational requirements.
- Involve information-security and privacy expertise in assessing access, data flows, and vendor controls.
- Confirm which information the proposed workflow would expose, including information included in prompts or uploaded files.
- Document the use that was reviewed and the conditions under which it is permitted.
The ABA’s business-law discussion recommends mapping data flows, vetting provider practices, and involving security and privacy expertise. Product assurances should be verified contractually and technically for the intended use. The sources available here do not establish the controls of any named product.
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A successful test is only one input to deployment. Set rules for who may use the tool, what work it may support, when review is mandatory, and how problems are escalated. Make clear that AI output is work requiring appropriate professional judgment, not a substitute for it.
- Assign an owner: Identify who approves the workflow, maintains its scope, and coordinates legal, operations, security, and privacy review.
- Write the permitted-use boundary: State the tasks, information, users, and review conditions covered by the approval.
- Give reviewers a procedure: Specify how to check outputs against source material and what to do when the tool cannot support, complete, or confidently answer the task.
- Reassess when circumstances change: Review the workflow when the task, tool, vendor terms, or relevant obligations change. Formal Opinion 512 describes the responsibility to understand AI’s changing benefits and risks as “not a static undertaking.”
An ABA article discussing the CLOC State of the Industry report says that 85 percent of corporate legal departments had formal AI oversight in 2024. That is a second-hand figure reported by the ABA article, not a direct examination of the original CLOC report, and it does not show that a particular oversight model is effective.
What the available evidence does—and does not—show
The ABA materials provide professional-responsibility guidance and practical workflow discussion; they are not a comparative product study. The reported extraction result illustrates why task-specific verification matters, but it cannot rank products or predict performance on a different team’s documents. No named-vendor ranking, product performance claim, or product security assessment is established by these sources. In-house teams should make those judgments through a defined, representative evaluation and a review of the data controls for their own proposed use.
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