The time for spring cleaning is upon us. Closets. Garages. Attics. All are more than likely do for their annual check up and clean out. Law firms, on the other hand, may have landed at an even more daunting space on the chore wheel: they need to get their data AI-ready.
It’s not billable, not glamorous, and you can’t boast about it at partnership meetings. But if your firm doesn’t make progress on data readiness in 2026, you’ll enter 2027 increasingly unable to compete on AI capabilities. And unlike past technology waves where you could wait and see, this AI arms race is moving too fast for that strategy.
Spring Cleaning for Your DMS
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Law firms sit on millions of documents such as motions, briefs, agreements, memos, and templates. In theory, this powers impressive AI applications. In practice, most of this content is unstructured: created in Word, scanned from paper, uploaded without systematic organization like unique filenames or tags. This misses the contextual metadata that AI needs.
Here’s what this looks like. Say we analyze statutory data from New York, California, and Illinois. AI can read every word perfectly. What it can’t do is tell you which statute comes from which state, because the statute text doesn’t say “this is a California statute.” It just states the law.
When someone asks, “What does California law say about non-competes?” Your AI genuinely doesn’t know which documents are California law. You need metadata tags: “this document = California statute,” “this document = labor law topic,” “this document = tech industry.”
Multiply this gap across practice areas and document types, and you see why firms that skipped organizational work struggle while others deploy sophisticated tools.
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Nobody Wants This Job
The resistance is rational. Data cleanup requires human effort for tasks like reviewing documents, applying tags, and verifying accuracy. It can’t be fully automated. And frankly, if you’re allocating human resources, you’d rather apply them to billable work.
I talk to research staff at firms who completely understand why this matters. They see the connection between data quality and AI capability. But they’re working with minimal investment because getting partnership approval for “we need people organizing files for six months” is genuinely difficult.
The firms that invested early, before AI became trendy, are now showing clients actual AI tools they’ve developed in-house during pitches, not future plans. They’re connecting vendor APIs seamlessly. They’re winning business on technical capabilities competitors can’t match.
Do the math. One million documents? Maybe 300,000 are truly critical. Of those, perhaps 20,000 need metadata enrichment, and the rest work fine for AI based on text alone. Deliver that first 20,000 in Q1. Build your AI application on top of it. Measure results. Then decide on phase two.
This creates a business case that executives can understand: targeted investment, quarterly deliverable, measurable outcomes. Not an indefinite commitment based on faith.
Lead With Your Superpower
Don’t ask “which data should we organize?” Ask “which data supports what makes us different?” Your firm’s primary differentiator, where you’re genuinely recognized as experts, that’s your starting point.
Known for workers’ compensation expertise? Make that content AI-ready first. Securities transactions? Start there. This focused approach lets you deploy meaningful AI capabilities in your specialty area while competitors are still trying to organize everything simultaneously.
The business development advantage is immediate and concrete. Instead of telling prospects, “we’re exploring AI applications,” you can say “we built an AI tool on our proprietary workers’ comp precedent library and decades of matter experience. No other firm can offer this capability.” That’s differentiation you can demonstrate in live demos, not just promise in pitch decks.
This matters because clients are in their own AI learning curve. They’re asking firms not just whether they use AI, but how that AI leverages the firm’s specific expertise. Generic AI tools are available to everyone. AI built on your firm’s proprietary, organized content? That’s an actual competitive advantage.
After phase one delivers results, measure carefully. Did you win pitches based on the AI capability? Close matters faster? Increase wallet share with existing clients who value the technology advantage? Those metrics become your business case for expanding to the next practice area. And once infrastructure is built, adding more data is dramatically simpler than initial setup.
Waiting Isn’t a Strategy
Many firms hope AI will eventually handle unstructured data well enough without human help. That’s not happening, at least not in timeframes that matter for competitive positioning. AI cannot infer context that isn’t there. It can’t determine statute jurisdiction if nobody tagged it. This requires human knowledge.
Right now, clients ask during pitches how you’re using AI to deliver value. Firms with AI-ready data demonstrate tools. Firms without it discuss pilots. Clients notice, and that gap widens quarterly.
Firms that are winning treat this as competitive imperative with quarterly goals, not someday project. They’re investing in unglamorous foundation work while others wait for easier answers that aren’t coming.
Your 90-Day Plan
Start with three questions: What practice area defines our advantage? What documents support that? What metadata makes those documents AI-useful?
Then commit to 90 days. Your research staff and senior associates know which content matters most. Aim for meaningful progress you can build on next quarter, not perfection.
Quarterly progress on data readiness is a spring project worth initiating in 2026. Prioritize focused work on content that supports what makes you special. Like the gym, starting is the hardest. Unlike the gym, your competitors are already there, and skipping it costs you pitches and clients.
Nicole Stone is Director of AI & Agentic Solutions Product Management at Wolters Kluwer Legal & Regulatory U.S., where she leads product strategy and development for digital legal content and technology solutions. With over 22 years of experience in legal technology and a background as a practicing attorney, she focuses on integrating emerging technologies, including generative AI, into products that serve legal professionals.