How to Evaluate AI for Patent Work

Most legal teams aren't evaluating AI well, and it's costing them the ability to feel the real impact.

At &AI, I've worked through evaluations with dozens of law firms, corporate IP departments, and investment funds. Some come with structured processes, defined KPIs, and clear decision criteria. Others show up as observers, curious about what's out there but with no framework for deciding what matters.

Here's how to build an evaluation process that actually reflects the demands of patent work.

Start with the workflow, not the technology

The most common mistake patent teams make in evaluation is not knowing what they're evaluating. They schedule demos, watch impressive presentations, and walk away without clarity on whether the tool solves a problem they actually have.

Don't evaluate "patent AI." Evaluate whether a tool can draft a claim chart for an IPR petition that correctly maps prior art references to each claim limitation. That level of specificity changes what you test, how you measure results, and which vendors make it past the first conversation.

The strongest use cases for evaluation share three traits: they're high volume (you do this work often enough to generate meaningful data), they follow consistent structure (the task has a repeatable logic to it), and they have measurable outcomes (you can tell whether the output is good or not). In patent work, claim chart generation, prior art search, and invalidity contentions drafting all fit this pattern.

Define accuracy at the citation level

When a tool generates a claim chart, every citation needs to be verifiable. If it says "Column 4, lines 23-26," that reference needs to exist exactly as cited and needs to actually support the claim mapping. If it identifies a prior art reference as anticipating a specific limitation, the identified passage needs to hold up under scrutiny.

This means your evaluation should include side-by-side comparison of AI-generated work product against attorney work product, scored at the individual citation level. Not only "did it find good references" but "are these specific citations accurate, complete, and defensible."

Most evaluation frameworks discuss quality of output and performance consistency as criteria. For patent work, make those criteria concrete: build a gold-standard test set from recent matters your team has handled, run the AI tool against it, and score the results with the same rigor you'd apply to an associate's work.

Measure performance at the edges

Every AI tool performs well on clean, straightforward inputs. The meaningful evaluation happens at the boundaries. When you evaluate a patent AI tool, include these scenarios deliberately. They're the cases where you need AI assistance most (because they're time-intensive and complex) and where the tool is most likely to expose its limitations.

Calculate ROI with billing model awareness

If your team bills hourly for prior art search and the AI tool cuts search time by 60%, your invoices get smaller. That's not automatically a win. The ROI question for patent teams isn't just "does this save time" but "what does the saved time enable."

There are several ways efficiency translates to value in patent practice. It might mean your team can handle more IPR petitions without adding headcount. It might mean associates spend less time on mechanical search and more time on the analytical work that develops their skills and serves clients better. It might mean faster turnaround wins you new business from clients who previously went elsewhere because your capacity was tapped.

The point is to think through the second-order effects before the evaluation starts, not after. A tool that saves 40% on prior art search is worth different amounts to a firm that's capacity-constrained versus one that's struggling with realization rates versus one that's trying to compete on turnaround time.

Align with the vendor on success criteria

The most productive evaluations I've been part of, on both sides, are the ones where the team tells the vendor exactly what they need to see.

If your evaluation hinges on claim chart accuracy for semiconductor patents, communicate that. If the deciding factor is whether the tool integrates with your existing document management system, make that clear from day one. If you need to see performance on a specific patent family that represents your typical workload, provide it.

This transparency eliminates the noise. The vendor can focus interactions on what actually matters to your decision, and you avoid sitting through demonstrations of features that aren't relevant to your practice.

Vendor transparency and partnership alignment matter in any evaluation. For patent teams, I'd frame it more directly: treat the evaluation as a collaboration, not a performance. Give the vendor your hardest problems. Watch how they respond. Not just with the product, but with their team and their willingness to adapt.

The evaluation process is a preview of the partnership

How a vendor behaves during evaluation tells you how they'll behave after you sign. Do they rush to close, or do they invest time in understanding your workflows? Do they acknowledge where their tool struggles? 

The best evaluations I've seen treat the process as a four-step sequence: define the specific workflow you're evaluating, identify the KPIs that map to your actual pain points, quantify how those KPIs translate to business value, and align with the vendor on what success looks like. Each step builds on the last, and skipping any of them leads to evaluations that generate activity without producing clarity.

Patent teams deserve evaluation frameworks built for the precision their work demands. Start there, and the right tool becomes a lot easier to find.