What AI labs look for in enterprise data

AI labs aren't buying company data to learn your secrets or to market to your customers. They're buying it to teach models how work gets done. Understanding exactly what they're looking for helps you decide what to include, what to leave out, and how to present your dataset so it's valued properly.

The short version: how work gets done

Models trained mostly on public text are good at writing and summarizing. They're much weaker at the messy, multi-step, context-heavy work that fills a real business day: triaging requests, coordinating across teams, handling exceptions, following (and occasionally bending) procedures, and making judgment calls with incomplete information.

Enterprise data is one of the few sources that captures that work as it actually happens. Labs use it to train systems that can act more like a capable colleague, and to evaluate whether those systems handle realistic situations well.

What they generally don't need is to know who said what. That's why de-identification is standard, and why it doesn't usually reduce the value of a well-prepared dataset.

Six signals of a valuable dataset

1. Real workflows, end to end

The most useful data shows a piece of work from start to finish: a customer request comes in by email, gets discussed in Slack, becomes a ticket, generates a document, needs an approval, and ends in a CRM update or invoice. Seeing the whole chain across systems is far more valuable than any one fragment.

2. Decisions and the reasoning behind them

Labs are especially interested in how people decide: weighing options, citing constraints, pushing back, escalating, and explaining trade-offs. Threads where people reason out loud are among the most valuable content a company has.

3. Handoffs and coordination

Work that crosses roles (sales to operations, support to engineering, finance to procurement) shows how organizations coordinate. Those handoffs are hard for models to learn from public data.

4. Procedures, and how they're actually followed

SOPs, playbooks and checklists describe the intended process. The surrounding conversations show the real one, including the exceptions. Together they're more valuable than either alone. See monetizing SOPs and internal documentation.

5. Time depth

Several years of history shows processes changing, tools being adopted, mistakes being corrected and teams scaling. That temporal depth is difficult to synthesize.

6. Domain expertise

Specialized knowledge in fields like real estate, healthcare administration, manufacturing, logistics, finance, legal or trading is valuable precisely because it's scarce in public data. Industry-specific workflows can command a premium.

Which systems tend to matter most

Connected systems are worth more together than apart. If you can include two or three that cover the same workflows, say so.

What makes data less useful

• Automated noise. Bot posts, notification emails and system logs add volume without adding much signal.
• Fragmented exports. Missing threads, orphaned replies and broken attachments make the data hard to use.
• Over-redaction. Stripping so much that the workflow is no longer understandable destroys value. Good de-identification replaces identifiers consistently rather than deleting context.
• Very short histories. A few months of data rarely shows enough variation.
• Narrow scope. A single small team or a single repetitive task offers limited learning signal.

What labs are not looking for

It's worth being explicit, because this is where most hesitation comes from:

• They're not looking to identify your employees or customers.
• They're not trying to replicate your product or compete with your business.
• They're not buying your customer list for marketing.

Contracts should reflect this, with clear permitted-use clauses, security obligations, and prohibitions on re-identification. Our licensing agreement checklist covers what to look for.

A quick self-assessment

Ask yourself a few questions about your own systems:

• Could an outsider reading a de-identified month of your Slack understand what your teams were working on and why?
• Do your documents show how plans changed, not just the final version?
• Do your tickets or CRM records show the steps between "opened" and "closed"?
• Are there processes in your business that require real expertise to do well?
• Do you have at least two or three years of continuous history in the main systems?

If you answered yes to most of these, your data likely has the characteristics labs look for. If you're unsure, that's normal. Part of a broker's job is to assess this with you before anything is shared.

How to present your dataset well

When you describe your data to a broker or buyer, lead with the things above: which workflows it covers, which systems connect, how many years of history, which roles and teams are represented, and what domain expertise it reflects. Rough volume estimates are enough to start.

DataOffer turns that description into a packaged profile, takes it to multiple labs, and brings you the best offer. You choose which systems are included, and nothing is shared until you approve the buyer, price and terms.

This guide is general information, not legal, tax or financial advice. Figures and ranges are illustrative; talk to qualified advisors about your situation.