Akin operationalizes embedded AI firmwide across 65+ million documents

Like many Am Law firms, Akin needed to move beyond isolated AI pilots into operational, firmwide intelligence applied directly to client matter content, precedent, and institutional know-how. Layering a separate AI tool onto the stack risked moving sensitive documents outside the firm’s secure document ecosystem. The firm required a way to unlock search, review, and knowledge reuse across more than 65 million documents while preserving governance, auditability, and client confidentiality.

Akin embedded NetDocuments AI capabilities—including Smart Answers and the Prompt Generator—directly inside its longstanding NetDocuments DMS rather than exporting documents to external AI tools. Cross-functional working groups spanning IT, Innovation, Knowledge Management, and more than 100 practice-group pilot users validated the approach, then partnered with NetDocuments Professional Services on a global rollout with tailored live training in New York, Dallas, Washington, and Houston. The firm is also deploying AI auto-profiling to populate more than 40 metadata fields across docketing files to improve searchability and structured knowledge reuse.

More than 900 lawyers now interact with over 65 million documents through firmwide embedded AI. An Energy partner reported saving up to 4 hours when processing 400–500-page reports; litigation teams have created client briefings for newly filed matters within hours; and knowledge counsel have automated complex document comparisons, term extractions, and summarizations. Lawyers have identified semantic search as the primary use case, shifting from keyword queries toward concept-driven discovery of precedent and work product.

• • Embedding AI in the system of record can unlock firmwide scale without moving client data outside the secure DMS.
• • Cross-functional pilots plus practice-specific training accelerate adoption among both advanced AI users and early adopters.
• • Pairing generative search/summarization with AI metadata enrichment turns a DMS into a structured institutional-knowledge platform.