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\u00a0 AI-ready.<\/p>\n
It\u2019s not billable, not glamorous, and you can\u2019t boast about it at partnership meetings. But if your firm doesn\u2019t make progress on data readiness in 2026, you\u2019ll 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.<\/p>\n
Spring Cleaning for Your DMS<\/strong><\/strong><\/p>\n 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.<\/p>\n Here\u2019s what this looks like. Say we analyze statutory data from New York, California, and Illinois. AI can read every word perfectly. What it can\u2019t do is tell you which statute comes from which state, because the statute text doesn\u2019t say \u201cthis is a California statute.\u201d It just states the law.<\/p>\n When someone asks, \u201cWhat does California law say about non-competes?\u201d Your AI genuinely doesn\u2019t know which documents are California law. You need metadata tags: \u201cthis document = California statute,\u201d \u201cthis document = labor law topic,\u201d \u201cthis document = tech industry.\u201d<\/p>\n Multiply this gap across practice areas and document types, and you see why firms that skipped organizational work struggle while others deploy sophisticated tools.<\/p>\n Nobody Wants This Job<\/strong><\/p>\n The resistance is rational. Data cleanup requires human effort for tasks like reviewing documents, applying tags, and verifying accuracy. It can\u2019t be fully automated. And frankly, if you\u2019re allocating human resources, you\u2019d rather apply them to billable work.<\/p>\n 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\u2019re working with minimal investment because getting partnership approval for \u201cwe need people organizing files for six months\u201d is genuinely difficult.<\/p>\n The firms that invested early, before AI became trendy, are now showing clients actual AI tools they\u2019ve developed in-house during pitches, not future plans. They\u2019re connecting vendor APIs seamlessly. They\u2019re winning business on technical capabilities competitors can\u2019t match.<\/p>\n 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.<\/p>\n This creates a business case that executives can understand: targeted investment, quarterly deliverable, measurable outcomes. Not an indefinite commitment based on faith.<\/p>\n Lead With Your Superpower<\/strong><\/strong><\/p>\n Don\u2019t ask \u201cwhich data should we organize?\u201d Ask \u201cwhich data supports what makes us different?\u201d Your firm\u2019s primary differentiator, where you\u2019re genuinely recognized as experts, that\u2019s your starting point.<\/p>\n Known for workers\u2019 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.<\/p>\n The business development advantage is immediate and concrete. Instead of telling prospects, \u201cwe\u2019re exploring AI applications,\u201d you can say \u201cwe built an AI tool on our proprietary workers\u2019 comp precedent library and decades of matter experience. No other firm can offer this capability.\u201d That\u2019s differentiation you can demonstrate in live demos, not just promise in pitch decks.<\/p>\n This matters because clients are in their own AI learning curve. They\u2019re asking firms not just whether they use AI, but how that AI leverages the firm\u2019s specific expertise. Generic AI tools are available to everyone. AI built on your firm\u2019s proprietary, organized content? That\u2019s an actual competitive advantage.<\/p>\n 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.<\/p>\n Waiting Isn\u2019t a Strategy<\/strong><\/strong><\/p>\n Many firms hope AI will eventually handle unstructured data well enough without human help. That\u2019s not happening, at least not in timeframes that matter for competitive positioning. AI cannot infer context that isn\u2019t there. It can\u2019t determine statute jurisdiction if nobody tagged it. This requires human knowledge.<\/p>\n Right now, clients ask during pitches how you\u2019re using AI to deliver value. Firms with AI-ready data demonstrate tools. Firms without it discuss pilots. Clients notice, and that gap widens quarterly.<\/p>\n Firms that are winning treat this as competitive imperative with quarterly goals, not someday project. They\u2019re investing in unglamorous foundation work while others wait for easier answers that aren\u2019t coming.<\/p>\n Your 90-Day Plan<\/strong><\/strong><\/p>\n Start with three questions: What practice area defines our advantage? What documents support that? What metadata makes those documents AI-useful?<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/strong><\/em><\/p>\n The post Your Real Spring Cleaning Project: Get Your Data Organized<\/a> appeared first on Above the Law<\/a>.<\/p>\n 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\u00a0 AI-ready.<\/p>\n It\u2019s not billable, not glamorous, and you can\u2019t boast about it at partnership meetings. But if your firm doesn\u2019t make progress on data readiness in 2026, you\u2019ll 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.<\/p>\n Spring Cleaning for Your DMS<\/strong><\/strong><\/p>\n 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.<\/p>\n Here\u2019s what this looks like. Say we analyze statutory data from New York, California, and Illinois. AI can read every word perfectly. What it can\u2019t do is tell you which statute comes from which state, because the statute text doesn\u2019t say \u201cthis is a California statute.\u201d It just states the law.<\/p>\n When someone asks, \u201cWhat does California law say about non-competes?\u201d Your AI genuinely doesn\u2019t know which documents are California law. You need metadata tags: \u201cthis document = California statute,\u201d \u201cthis document = labor law topic,\u201d \u201cthis document = tech industry.\u201d<\/p>\n Multiply this gap across practice areas and document types, and you see why firms that skipped organizational work struggle while others deploy sophisticated tools.<\/p>\n Nobody Wants This Job<\/strong><\/p>\n The resistance is rational. Data cleanup requires human effort for tasks like reviewing documents, applying tags, and verifying accuracy. It can\u2019t be fully automated. And frankly, if you\u2019re allocating human resources, you\u2019d rather apply them to billable work.<\/p>\n 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\u2019re working with minimal investment because getting partnership approval for \u201cwe need people organizing files for six months\u201d is genuinely difficult.<\/p>\n The firms that invested early, before AI became trendy, are now showing clients actual AI tools they\u2019ve developed in-house during pitches, not future plans. They\u2019re connecting vendor APIs seamlessly. They\u2019re winning business on technical capabilities competitors can\u2019t match.<\/p>\n 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.<\/p>\n This creates a business case that executives can understand: targeted investment, quarterly deliverable, measurable outcomes. Not an indefinite commitment based on faith.<\/p>\n Lead With Your Superpower<\/strong><\/strong><\/p>\n Don\u2019t ask \u201cwhich data should we organize?\u201d Ask \u201cwhich data supports what makes us different?\u201d Your firm\u2019s primary differentiator, where you\u2019re genuinely recognized as experts, that\u2019s your starting point.<\/p>\n Known for workers\u2019 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.<\/p>\n The business development advantage is immediate and concrete. Instead of telling prospects, \u201cwe\u2019re exploring AI applications,\u201d you can say \u201cwe built an AI tool on our proprietary workers\u2019 comp precedent library and decades of matter experience. No other firm can offer this capability.\u201d That\u2019s differentiation you can demonstrate in live demos, not just promise in pitch decks.<\/p>\n This matters because clients are in their own AI learning curve. They\u2019re asking firms not just whether they use AI, but how that AI leverages the firm\u2019s specific expertise. Generic AI tools are available to everyone. AI built on your firm\u2019s proprietary, organized content? That\u2019s an actual competitive advantage.<\/p>\n 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.<\/p>\n Waiting Isn\u2019t a Strategy<\/strong><\/strong><\/p>\n Many firms hope AI will eventually handle unstructured data well enough without human help. That\u2019s not happening, at least not in timeframes that matter for competitive positioning. AI cannot infer context that isn\u2019t there. It can\u2019t determine statute jurisdiction if nobody tagged it. This requires human knowledge.<\/p>\n Right now, clients ask during pitches how you\u2019re using AI to deliver value. Firms with AI-ready data demonstrate tools. Firms without it discuss pilots. Clients notice, and that gap widens quarterly.<\/p>\n Firms that are winning treat this as competitive imperative with quarterly goals, not someday project. They\u2019re investing in unglamorous foundation work while others wait for easier answers that aren\u2019t coming.<\/p>\n Your 90-Day Plan<\/strong><\/strong><\/p>\n Start with three questions: What practice area defines our advantage? What documents support that? What metadata makes those documents AI-useful?<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/strong><\/em><\/p>\n The post Your Real Spring Cleaning Project: Get Your Data Organized<\/a> appeared first on Above the Law<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" 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\u00a0 AI-ready. It\u2019s not billable, not […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-151303","post","type-post","status-publish","format-standard","hentry","category-above_the_law"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/151303","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/comments?post=151303"}],"version-history":[{"count":0,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/posts\/151303\/revisions"}],"wp:attachment":[{"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/media?parent=151303"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/categories?post=151303"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xira.com\/p\/wp-json\/wp\/v2\/tags?post=151303"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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