If your organization is exploring AI for records management, you’re not alone—and you’re not late. In a recent Gimmal education session, most attendees reported they were still exploring AI with no concrete plans yet. That’s normal. The smartest first step isn’t choosing an AI tool—it’s preparing your data so AI only learns from the right information.

Here’s a practical roadmap from the session, led by Jeff Tujetsch, on how Gimmal Discover helps you get AI-ready, without risking sensitive, proprietary, or misleading content.

Why Data Readiness Matters

AI is powerful, but indiscriminate. If you point it at everything, it can:

• Learn from sensitive data (contracts, HR files, legal communications, proprietary designs)
• Expose personally identifiable information (PII/PHI)
• Absorb outdated or redundant content that skews results

The goal: give AI only what you intend. That starts with four steps—identify, inventory, include/exclude, and enforce.

Step 1: Identify What You Have

Most organizations can’t fully describe what’s on their file shares, in SharePoint, OneDrive, or email. You don’t need perfect certainty—you need a systematic approach.

Think in high-level categories:

• Confidential: contracts, HR, invoices, legal communications
• Personal: employee or customer data (PII/PHI)
• Proprietary: formulas, designs, processes, trade secrets

Gimmal Discover helps you identify these with:

• Metadata analysis: file names, extensions, paths, dates, owners, sites, libraries, mailboxes
• Content search: keywords, phrases, Boolean expressions (AND/OR/NOT, proximity)
• Pattern detection (regex): Social Security numbers, national IDs, credit cards, driver’s licenses, health data, and more

Tip: Don’t draw hard lines on day one; expect to iterate as you learn.

Step 3: Decide on Inclusion vs. Exclusion

There are two viable stances:

• Inclusion-first: Build a clean, approved corpus for AI to learn from
• Exclusion-first: Block or remove sensitive or non-relevant content and allow everything else

Practically, most teams “cater to the exception”: exclude the smaller sensitive set rather than curating the entire estate—especially when dealing with hundreds of terabytes or more.

Don’t Skip ROT Remediation

Redundant, obsolete, or trivial data can pollute AI outcomes just as much as sensitive data can create risk. Clean it up early. Old or irrelevant content leads to misinformation and noise; remove or archive it before building your AI corpus.

Best Practices from the Field

• Start small, then scale: Pilot with a known subset where you can validate results.
• Simulate before labeling: Tune your rules to reduce errors.
• Use composite rules: Combine metadata + content for better precision.
• Keep labels manageable: You can create unlimited labels, but aim for fewer than 100 to avoid operational drag.
• Visualize for buy-in: Use dashboards to show progress and justify decisions.

Common Questions Answered

• Can we combine metadata and content? Yes—use AND conditions across path, extension, owners, and content matches.
• Can we tailor rules by department or region? Absolutely—Gimmal Discover supports fully customizable criteria per group or geography.
• Is there a max number of labels? No hard limit, but fewer, well-defined labels are easier to manage.
• Should we process ROT for AI readiness? Yes—remove outdated or trivial content to protect AI quality and reduce risk.

The Bottom Line

AI readiness isn’t about turning on a feature—it’s about curating your data with intention. Gimmal Discover helps you:

• Find and quantify what matters
• Label and protect what shouldn’t be learned
• Isolate what should be learned
• Carry those controls forward into Microsoft 365 and Copilot

Do that well, and when you’re ready to turn on AI, your data will be ready too.