AI Projects Often Lack Good Record Keeping

Across the legal profession and corporate world, firms are approving AI tools, launching pilots, and exploring how generative AI can improve productivity. But beneath that momentum lies a quieter operational reality: the challenge isn’t about a willingness to adopt AI itself, but whether legal information governance and data management practices are mature enough to support AI workflows securely, reliably, and defensibly.

Many law firms have successfully deployed AI in isolated use cases, but rushing to scale those initiatives often exposes the shaky condition of the information environment underneath them. As one information governance leader recently said, “The organizations struggling most with AI aren’t necessarily behind on technology. They’re discovering their information environment was never designed for what AI is now trying to do.”

One of the clearest trends emerging across law firms and large organizations is the growing demand for records and information governance work directly tied to AI initiatives. This is not because organizations suddenly became passionate about records management, but because AI is forcing them to confront information management issues that have been tolerated for years.

As firms begin piloting AI, familiar operational challenges quickly emerge. Search results become inconsistent. Duplicate and outdated content surfaces unexpectedly. Permissions expose information more broadly than intended. Users lose confidence in AI-generated outputs because the underlying information lacks consistency, quality, and traceability.

Another operational leader observed, “AI didn’t create these problems. It just made them impossible to ignore.” The result is rarely catastrophic failure. More often, users begin questioning whether outputs are complete, accurate, current, or trustworthy enough to rely on—and once confidence declines, adoption slows.

Security presents an equally important challenge. AI calls up whatever information users are permitted to access. Organizations with inconsistent permissions, decentralized repositories, or outdated security models may unintentionally expose sensitive information once they begin using AI workflows.

Much of today’s AI conversation focuses on models, vendors, and productivity gains. Yet the more immediate challenge often sits elsewhere. AI systems rely entirely on the quality, accessibility, structure, and governance of the information they interact with. Simply put, messy information produces unreliable AI.

Poorly maintained repositories, obsolete records, inconsistent naming conventions, duplicate content, and weak metadata all reduce confidence in AI-generated responses. For law firms especially, defensibility is critical. AI-generated summaries, recommendations, and research still require validation, traceability, and accountability.

Regulators, clients, and courts are increasingly unwilling to accept “the AI generated it” as an explanation for inaccurate or unsupported information. Organizations need environments where information is not only accessible, but secure, traceable, and defensible.

One of the biggest misconceptions in the market is that AI readiness is primarily a technology roadmap. In reality, it is an information governance roadmap.

Achieving this level of maturity requires significant foundational work. Leading organizations are modernizing retention schedules, improving metadata, consolidating repositories, cleaning up classification structures, strengthening search capabilities, and implementing defensible disposition programs before expanding AI across the enterprise.

Governance should not be viewed as slowing innovation. It is what makes AI trustworthy, scalable, and operationally valuable. By investing in understanding, organizing, securing, and governing the information AI depends on, law firms can realize the greatest long-term value from AI.

Building a governance environment capable of supporting AI at scale is not a 90-day initiative. For many organizations, 18 to 24 months is a far more realistic timeline for establishing governance frameworks, modernizing information, strengthening security, and preparing operationally for broader AI adoption.

Those that rush deployment often spend far more time correcting governance failures, security issues, and unreliable outputs than they saved by rushing forward. As one information governance consultant said, “AI increases the speed and scale of what you can do. But it also exposes your governance weaknesses at the same speed and scale.”

Before expanding AI initiatives, organizations should understand the condition of the information environment AI is about to interact with. That means understanding what information exists, where it lives, who owns it, whether it is secure, whether it is defensible, and whether information governance practices are mature enough to support AI responsibly.

Organizations that answer those questions first will be far better positioned to realize AI’s full potential. Records and information governance leaders are now emphasizing the importance of information governance in AI adoption, and their guidance can help organizations move from isolated pilots to sustainable, enterprise-wide AI adoption, especially for law firm management and implementation of simple safe AI projects.