Legal AI Benchmarks: The Overlooked Blind Spots

In my last article, Building Your Own Legal Benchmarks for LLMs and Vendor AI Tools, I outlined how legal teams can structure their own evaluations to test whether AI actually works for their workflows. That piece focused on setting up a structured benchmarking process: defining legal tasks, building datasets, and selecting evaluation metrics.

Once you have a benchmark in place, the next challenge is ensuring it tests for the right risks. Too many evaluations focus on simple accuracy tests, measuring whether an AI tool extracts key clauses or retrieves case law correctly in ideal conditions. Real legal work is rarely that clean.

AI tools need to do more than just identify clauses. They need to reason across multiple documents, handle incomplete or ambiguous inputs, and provide reliable, explainable results. If firms are not testing for these challenges, their benchmarks may be giving them a false sense of confidence.

This article builds on the last by looking at where legal AI benchmarks often fall short: the overlooked challenges that determine whether an AI tool is actually fit for legal workflows.

1. Multi-document reasoning: can AI actually connect the dots?

Legal analysis does not happen in isolation. A contract references annexes, schedules, and previous agreements. A regulatory review involves policies, guidance, and case law. Due diligence requires sifting through hundreds of documents that overlap and contradict each other.

Yet most AI benchmarks only test single-document tasks. That is not how legal work happens.

The real test

If an AI tool is reviewing a contract and comes across "subject to Schedule 3," can it:

Find Schedule 3 and apply it to the analysis?
Compare similar clauses across different agreements?
Resolve contradictions between overlapping contracts?

If an AI system is not being tested on these kinds of challenges, it is not being tested properly.

How to benchmark this

Run evaluations that require retrieving and reasoning across multiple documents, not just extracting a clause from one.
Introduce conflicting clauses across different agreements. Does the AI just summarise both, or does it flag inconsistencies?
See if the AI can handle multi-step legal logic, not just surface-level text matching.

2. Handling imperfect inputs: what happens when documents are not clean?

Most AI tools are tested on structured, digital text. In reality, firms deal with scanned contracts, handwritten amendments, missing pages, and redacted sections. If an AI tool only works on clean documents, it is not fit for purpose.

What often gets overlooked

Scanned PDFs with OCR errors: does the AI recover key information, or does it fail when the text is not perfectly readable?
Handwritten notes in contracts: does it register amendments, or does it ignore them completely?
Redactions and missing information: does it still provide useful insights, or does it just guess?
Diagrams, tables, and embedded images: if an obligation is outlined in a site plan instead of the main lease text, can the AI recognise and process it?

How to benchmark this

Test AI on real-world document conditions, not just perfect digital text.
Introduce handwritten edits, redactions, and OCR noise. Does the AI handle these well or does it fail?
If a contract references an obligation shown in a table, image, or diagram, can the AI correlate the visual data with the text?

If a tool struggles with these cases, it is probably not ready for deployment in a legal workflow.

3. RAG and document retrieval: is AI finding the right information?

Many vendor tools rely on Retrieval-Augmented Generation (RAG), where AI searches a document set before generating an answer. In theory, this makes AI more reliable, but in practice, retrieval failures are common. The system might:

Pull from the wrong section, retrieving an unrelated indemnity clause instead of the one governing liability.
Overlook key references, failing to recognise that an NDA references a separate confidentiality agreement.
Surface irrelevant case law, selecting superficially similar but legally useless precedents.
Rely on simple fuzzy matching instead of legal understanding by just returning results based on surface-level similarity rather than the underlying legal concept.

How to benchmark this

Ask the AI precise legal questions and verify whether it retrieves the right content, not just something vaguely relevant.
Introduce ambiguous clauses. Does the AI find all the relevant sections, or does it take the easiest match?
Test how well AI recognises synonyms and variations of legal terminology. If asked for contract length, does it find "Term of Agreement" or "Duration," or does it only match the word "Length"?
If retrieval logs are available, check what the AI actually searched, not just what it outputs.

Legal documents often use different terminology for the same concept. If an AI system cannot understand contextually relevant synonyms and retrieve the correct legal provisions, then it is not actually performing retrieval—it is just matching words.

4. How it handles changes over time: can it track contract evolution?

Contracts do not just exist in a single version. They evolve. A single clause might go through dozens of small edits over months or years. Some are legally meaningless, others highly significant.

Most AI benchmarks do not test this. They look at a single snapshot of a document rather than whether AI can track how contract terms shift over time.

What AI needs to handle

Comparing versions of a contract and highlighting meaningful changes.
Recognising when slight wording differences alter legal meaning.
Flagging when a clause has become riskier across different drafts.

How to benchmark this

Feed the AI multiple versions of the same contract. Does it detect key changes?
Introduce common negotiation redlines. Does the AI highlight material shifts in liability?
Check how AI scores risk across different drafts. Does a slight indemnity clause tweak get flagged?

For firms dealing with long-running contracts or regulatory updates, this kind of benchmarking is critical.

5. Usability and explainability: does AI show its work?

Even when AI gets an answer right, legal teams need to know why. A system that extracts a clause correctly but cannot justify its decision-making is not fit for high-stakes legal work.

Common failures in explainability

AI gives the right answer but cannot show where it got it from.
AI changes its response between runs with no explanation.
AI gets a legal question wrong but does not indicate uncertainty.

How to benchmark this

Ask AI to explain its reasoning. If it cannot, it is a black box.
Run the same tests multiple times. Does it give consistent results, or is it unpredictable?
See if AI flags uncertainty. Does it admit when it is unsure, or does it confidently state incorrect conclusions?

In a legal setting, auditability matters as much as accuracy. If an AI tool cannot trace its results, it cannot be trusted.

Push solutions harder: these tests matter

Too many AI benchmarks focus on best-case scenarios. But real-world legal work is messy, multi-layered, and full of edge cases. AI tools need to be tested where they are most likely to fail, not just where they look good.

Firms should not be afraid to push solutions on these evaluations, because if an AI system cannot handle multi-document reasoning, imperfect inputs, retrieval errors, or explainability, then it is not ready for legal work, no matter how impressive it looks in a demo.

The legal industry needs to move beyond accuracy in perfect conditions and start holding AI to the standards of actual legal practice. The best AI is not the one that performs well in a benchmark, it is the one that does not break down under real legal pressure, much like what we look like for in people doing the work.

Legal AI benchmarking needs to constantly adapt, if a firm is testing AI for contract review, due diligence, or compliance, it needs to stress-test AI on:

Multi-document logic
Messy, real-world documents
Correct and explainable retrieval
Tracking legal changes over time
Auditability and justification

These are not edge cases. They are the work.