By Rutvik Rau, CEO, August.
We have just published our ‘2026 Legal AI Planning Guide’, and I wanted to share why we wrote it and what we learned in the process.
Over the past year, I’ve talked to dozens of law firm partners and associates about their AI evaluations. The pattern is consistent: they ran a pilot, saw some impressive demos, maybe even got partners excited. Then six months later, nobody’s using the tool. When I ask what went wrong, the answers usually sound like this:
‘It worked great in the demo, but on our actual documents it kept making mistakes.’
‘We bought it for the Word integration, but the add-in was clunky and people stopped using it.’
‘It couldn’t learn our firm’s style, so everything it produced needed heavy editing.’
These aren’t edge cases, they’re the norm. And they point to a real problem: most firms are evaluating legal AI the wrong way.
The demo problem
Vendors are good at demos. They choose documents that make their tools look impressive, avoid edge cases, and show everything working exactly as designed. Then your lawyers try it on the messy, non-standard documents that fill their actual workload, and the results don’t match expectations.
The issue isn’t that vendors are being dishonest. It’s that a demo optimized for a 30-minute presentation doesn’t tell you what you need to know. Can the tool handle your 200-page credit agreements with unusual formatting? Does it maintain accuracy when processing documents at scale? Can it learn and apply your specific precedents and style guides?
You don’t find out until you’re already committed.
What to measure instead
We built this guide around five evaluation categories that actually predict whether a tool will stick:
Accuracy and reliability comes first because nothing else matters if the outputs are wrong. But accuracy isn’t binary. The question isn’t “does it make mistakes?” but rather “what kinds of mistakes does it make, how often, and can lawyers catch them easily?” A tool that occasionally gets a date wrong but always cites its sources is manageable. A tool that confidently invents case citations is not.
Firm knowledge integration separates commodity AI from something genuinely valuable. Every tool on the market can summarize a contract. The differentiator is whether it can do so while applying your precedents, matching your drafting style, and following your client-specific playbooks. Generic AI capabilities are table stakes now. The value comes from tools that learn how your firm works.
Platform integration determines whether people will actually use the tool. Lawyers work in Word, Outlook, and their document management systems. If accessing AI means breaking out of that workflow, uploading files manually, and toggling between applications, adoption falls off quickly.
Advanced capabilities matter because basic summarization doesn’t move the needle much anymore. The work that justifies AI investment is structured extraction across large document sets, systematic comparison against standards, and consistent application of review criteria at scale. Can the tool process a hundred contracts and flag specific clause deviations? Can it generate comparison matrices?
Vendor partnership predicts what happens after you sign. How the vendor behaves during your evaluation is the best indicator of how they’ll behave later. Do they respond quickly? Do they customize their approach to your practice areas? Are they transparent about roadmap and limitations? Or are they showing you the same generic demo they show everyone, promising features that may or may not ship?
Why we’re sharing this
We wrote this guide because we kept seeing firms make preventable mistakes in their evaluations. They’d focus on features that sounded good in theory but didn’t matter in practice. They’d skip testing the scenarios that would make or break actual adoption. They’d accept vendor promises about “coming soon” capabilities without pressing for specifics.
The result was a lot of expensive tools sitting unused and a lot of skeptical partners who’d been burned once and weren’t interested in trying again.
Our view is that legal AI works when it’s evaluated properly. Not with a checklist of features, but with structured testing on real work, clear scoring criteria, and documented evidence. The guide walks through how to do that: what questions to ask, what tests to run, what evidence to collect, and how to convert your findings into a defensible recommendation.
The self-interested part
Naturally, we built August to score well against these criteria. We made specific architectural choices because we thought they mattered: citations on every output, native Word and Outlook integration with context preservation, direct DMS connectivity, playbook functionality that applies your precedents consistently, and tabular review that handles volume without degrading.
But the guide isn’t August marketing. It’s the framework we wish firms were using when they evaluated us, because it focuses on the things that actually predict success. If you run this evaluation process and conclude that another tool is a better fit for your practice, that’s a good outcome. Better than buying something that doesn’t work and souring your firm on AI entirely.
Get a copy
The full guide includes detailed scoring rubrics, sample evaluation questions, red flags to watch for, and a decision framework for converting your pilot results into a clear recommendation. It’s designed for a 2-4 week evaluation window and structured so you can hand it to practice group leaders and get consistent feedback.
You can download it at this link. We’re also happy to talk through how firms are using it, what they’re learning, and how to adapt the framework for specific practice areas.
The legal AI market is moving fast, and there’s real pressure to pick something and move forward. But a rushed evaluation that leads to a bad purchase is worse than taking a few extra weeks to do it properly. This guide is our attempt to help firms get it right the first time.
To learn more about what we’re building at August, feel free to book a meeting with us here.
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[ This is a sponsored thought leadership article by August for Artificial Lawyer. ]
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