What is my company's data worth to AI companies?

It’s the first question every owner asks, and the honest answer is that the number is set by the buyer after reviewing your data, not before. What we can do is show you what buyers have published, explain why the range is wide, and walk through the factors that move a company up or down it.

What have AI data buyers published about compensation?

This market is new, and few buyers publish numbers. The clearest public figures come from micro1, an AI data company that on October 9, 2026 announced it will spend $1 billion over 12 months acquiring and licensing enterprise operational data (announcement).

micro1’s program page lists three tiers:

Published tier	micro1’s description
$100K+	Qualified Enterprise Data Partnerships
$500K+	Large-scale operational datasets or ongoing participation across multiple teams and business functions
$1M+	Highly unique, proprietary operational data with significant value for frontier AI development

Its referral page describes company compensation as “$100K–$2M+ for approved data packages,” typically for companies with 30+ employees.

Three things to keep in mind when reading these numbers:

1. They are published ranges, not averages or offers. They describe what approved partnerships can look like.
2. Many companies won’t be approved. The ranges apply only to companies the buyer accepts.
3. Each partnership is evaluated individually. The final number comes from the buyer’s review of your specific data.

Why is the range so wide?

Because companies’ data varies enormously. Two freight brokerages with 80 employees each can look identical from the outside, but one has a decade of clean TMS history, detailed exception-handling SOPs, and a well-maintained carrier-onboarding wiki, while the other runs on tribal knowledge and email. To an AI data buyer, those are very different assets.

The range also reflects scope. A package covering one team’s workflows is different from ongoing participation across sales, operations, finance, and support.

What factors determine what a company’s data is worth?

micro1 says it considers “dataset size and volume, workflow complexity, data quality, domain expertise represented, and the uniqueness of the operational knowledge contained within the dataset.” In practical terms:

Quality

Is the data complete, consistent, and accurate? Are SOPs current, or written once in 2017 and never updated? Do tickets and records capture why decisions were made, or just that they were?

Uniqueness

Does your data capture knowledge that’s hard to find elsewhere? Specialized niches, such as hazmat freight, healthcare IT compliance, or niche technical staffing, may be more valuable than generic workflows that many companies share.

Volume and history

More records over more years give a buyer more examples to learn from. Volume alone isn’t enough, but it matters.

Workflow complexity

Workflows with many steps, handoffs, exceptions, and judgment calls are what AI agents struggle with, so they tend to be more interesting to buyers than simple, linear tasks.

Domain expertise

Data that reflects deep expertise, such as how an experienced dispatcher, senior engineer, or veteran recruiter actually works, carries more signal than entry-level process.

Breadth across functions

Data spanning multiple teams (sales, operations, finance, support) can support larger or ongoing arrangements, which is reflected in the higher published tiers.

Can it be cleanly de-identified and licensed?

If your data is so entangled with customer confidential information or regulated personal data that it can’t be safely de-identified, or your contracts don’t let you share it, its practical value drops, sometimes to zero.

What makes company data worth less, or nothing?

• Little documentation. If processes live in people’s heads, there’s little to license.
• Short history. A year or two of records is thinner than ten.
• Mostly non-English records. Current buyers primarily want English-language data.
• Heavy restrictions. Customer contracts or regulations that prohibit sharing even de-identified data.
• Generic content. Off-the-shelf templates and public material add little.
• Outside the buyer’s current needs. Demand shifts by industry and workflow; a buyer may pass simply because it already has enough data like yours.

What could this look like in my industry?

We can’t give industry price points, because none have been published. But here’s what buyers’ published criteria suggest each of our focus industries may be able to offer:

• Freight brokers and 3PLs: load-booking and tendering workflows, carrier vetting and onboarding SOPs, exception and claims handling, TMS records, and the decision-making behind rate negotiation.
• IT services and MSPs: ticket histories from intake to resolution, runbooks, escalation and incident-response playbooks, client onboarding procedures, and QA on technician work.
• Staffing agencies: requisition intake, sourcing and screening workflows, submittal decisions, compliance and onboarding checklists, and ATS/CRM metadata.

In each case, the value comes from showing how experienced people make decisions, not from customer or candidate contact details, which would be removed.

How can I estimate where my company might land?

Before any buyer conversation, a rough self-assessment helps. Score yourself honestly:

• Do we have 30+ employees and operate mainly in English, in the US?
• Do we have written SOPs that people actually use?
• Do our systems hold several years of history?
• Do our records capture decisions and exceptions, not just outcomes?
• Is our work specialized in a way few competitors could replicate?
• Could we share data from more than one function?
• Are we confident our contracts allow sharing de-identified operational data?

The more “yes” answers, the stronger the conversation is likely to be. It still won’t produce a number; only the buyer’s review does that.

What should I ask a buyer about compensation?

When a buyer is interested, these questions help you understand an offer before you weigh it:

• What exactly is being paid for? Which systems, teams, record types, and date ranges are in the package.
• How was the number set? Which factors pushed it up or down, and what would change it.
• How is it paid? Lump sum, installments, or milestones, and what triggers each payment.
• Is there an ongoing component? Whether new data generated later could be licensed on similar terms.
• What work is expected from my team? Exports, interviews, reviews of de-identified samples, and roughly how many hours.
• What happens if the data falls short? Whether payment can be reduced if the delivered data doesn’t match what was described.
• Is there exclusivity? Whether you’re free to license similar data to other buyers.

The answers matter as much as the headline number. A smaller offer with a narrow scope, light preparation work, and no exclusivity can be a better deal than a larger one that ties up your team for months.

What else affects what I actually receive?

The headline figure is only part of it. Consider the time your team will spend on preparation, legal review costs, how payments are scheduled, and tax treatment. Ask your accountant and attorney before signing.

You should also understand how anyone helping you gets paid. In our case, we may receive a referral fee from a data buyer when a company we introduce signs an agreement. It costs you nothing.

How do I find out what my company’s data is worth?

The only way to get a real number is to have a buyer review your data. The first step is cheaper: find out whether you fit what buyers are looking for. Our one-minute qualification form asks about size, industry, and systems, with no data uploaded. If there’s a likely fit, we’ll set up a 15-minute call and, with your permission, an introduction.