For decades, your Records and Information Management (RIM) program has rested on a simple premise: a record is a document, such as a contract or a policy memo, created or received in the course of business and retained according to a record retention schedule. Artificial intelligence is upending that premise, and most organizations haven't caught up.

The Problem: AI Outputs Don't Always Fit Traditional Records Categories

Consider what happens when an employee uses a generative AI tool to summarize a set of contracts, draft a client communication, or analyze regulatory obligations. What is the resulting output? Is it a record? A draft? Work product? Something else entirely? And what about the prompt itself, or the underlying model's training data that shaped the response?

Traditional retention schedules weren’t built for this. They assume records are static, identifiable, and attributable to a person or department. AI-generated content challenges all three. Outputs can be ephemeral, iterative, and hard to trace to a single author or business function. Worse, the same tool might generate materially different outputs from the same prompt—so which version, if any, is the "official" record?

The Risk: Spoliation, Discovery Failures, and Compliance Gaps

When organizations adopt AI tools without updating their RIM frameworks, they create governance gaps with real consequences. Courts are increasingly treating AI-generated content, including prompts, model responses, and interaction logs, as discoverable. It’s not exempt simply because it’s novel. If your organization can’t demonstrate a coherent approach to classifying and retaining AI content, you’ll be exposed when it matters most.

The Path Forward

The good news? You don’t need to reinvent your RIM program from scratch. The foundation you need may already exist: your records inventory and retention schedule. These tools, often dismissed as back-office compliance exercises, are now essential to responsible AI deployment. But they require deliberate updates to reflect how AI is actually used.

• Evaluate and update your retention schedule. Not every AI output needs to be retained. Like email, much of it may be transitory and appropriate for auto-disposition. Determine whether existing record categories or examples capture AI-generated content or whether new categories are needed.  The key is to focus on substance, content and use of the output. In making the determination, consider whether the AI output is relied upon in decision-making or incorporated into a final deliverable.

• Ensure your records inventory reflects AI use. Identify which AI tools your organization uses, what outputs they produce, and where those outputs live. Don’t overlook browser-based tools and AI notebooks employees use outside sanctioned platforms, as these create pockets of unmanaged data your current inventory almost certainly misses.

• Update your legal hold process. Your legal hold procedures should explicitly cover AI tools, including prompts, outputs, and activity logs. Disable auto-delete settings, export chat histories, and coordinate with IT to understand how logs are retained. Don’t overlook personal accounts employees may be using for work-related queries. Make sure custodian interview templates inquire about AI usage on matters potentially related to the issues subject to preservation.

• Provide clear user guidance on storing final outputs. Employees should be instructed that any final work product generated or revised using AI must be saved in designated record repositories. Generally, AI tools themselves should not be treated as systems of record.

• Apply shorter retention to transitory AI data. Where AI outputs are not records (such as drafts, iterative outputs, or convenience summaries), organizations should implement shorter retention periods aligned to operational need. Retention should be intentional and policy-driven, not default to indefinite storage.

• Build cross-functional alignment. IT, Legal, Compliance, and Records Management all need to be at the table. AI governance can’t be owned by a single function, and policies governing when and how AI outputs should be saved require buy-in across the organization.

The Bottom Line

AI isn’t just creating new data; it’s forcing organizations to reconsider what qualifies as a record. The question is whether your RIM program is changing with it. The organizations that adapt their retention schedules, inventories, and governance processes accordingly will be far better positioned to manage risk. In an AI-driven environment, it’s not just what you create—it’s what you choose to treat as a record that defines your exposure.

For a deeper dive into how records inventories and retention schedules can anchor your AI governance strategy, watch our recent webinar on this topic: Your Organization’s Most Undervalued AI Governance Tool: Records Inventories and Retention Schedules