What Data Do AI Companies Buy From Businesses?

Why AI buyers want this data now

AI development is moving from models that mainly answer questions toward agents that perform work.

Agents need to navigate software, make decisions, complete multi-step tasks, recover from mistakes, and reach a measurable result. That requires a different kind of training material.

Scale AI says nearly half of its new data-training projects now involve reinforcement-learning environments where models practice realistic tasks. Reuters has also reported growing demand from frontier AI labs for more complex training data and environments.

That helps explain why operating histories are interesting.

A folder of invoices records what happened.

A history showing the problem, investigation, decision, action, and result can teach a model how work gets done.

Where to look inside your company

Here is the simplest way to think about the major categories.

Current programs support this broader view of company data.

Scale actively seeks data from sectors including software, manufacturing, field services, freight, energy, and other operating businesses. Prism highlights support histories, code traces, financial reasoning, scientific reasoning, and cross-system workflows. micro1 seeks engineering, sales, finance, support, logistics, and internal-operations data. Dayda sources support records, sales calls, CRM histories, code activity, meetings, and other business datasets.

The categories are broad because the important part is not the software vendor.

It is what happened inside the workflow.

What does a strong dataset actually look like?

A few examples make the distinction clearer.

Customer support

Prism currently highlights longitudinal support histories, while micro1 and Dayda also seek support and customer-operations data.

The asset is not 500,000 pieces of text.

It is 500,000 examples of problems being handled.

Field service

Imagine an HVAC company.

An invoice tells you that a compressor was replaced.

The operating history might tell you:

• the equipment and fault code
• what the technician observed
• possible causes considered
• diagnostic steps taken
• why a repair was chosen
• what parts were installed
• whether the customer called back

That history captures experienced technicians solving real problems.

And this is not a theoretical category. Scale explicitly lists HVAC, plumbing, and electrical field-service companies among the businesses it wants to work with.

Software engineering

Engineering is one of the clearest areas of current demand.

Prism highlights code-generation and editing histories. Dayda sources commit histories, reviews, and issue data. Specific Marketplace works with sources including GitHub, Jira, and Confluence.

Finished code tells you what was built.

The surrounding history can show why it was built that way.

Manufacturing and operations

That history can capture years of accumulated operating knowledge.

Scale actively targets manufacturing and industrial businesses. The General Data Company specifically points to maintenance logs, quality checks, decisions, and outcomes as useful workflow data.

Routine records show normal operations.

Exceptions often show the judgment.

Sales

micro1 seeks CRM and sales workflows, while Dayda sources B2B sales calls, transcripts, and CRM activity.

The contact database tells you who existed.

The workflow shows how people sold.

Slack and email are often connective tissue

Internal communications deserve special treatment.

A company might have millions of Slack messages or years of email. That volume alone does not tell you much.

Their real value often appears when they connect work happening elsewhere.

For example:

Zendesk ticket → Slack discussion → Jira task → GitHub change → Zendesk resolution

or:

Salesforce opportunity → Gong call → internal email → proposal → won/lost result

Scale lists Slack, Teams, email, documents, and project systems among relevant data sources. Specific Marketplace also works with workplace data from systems such as Slack, Google Workspace, Jira, Confluence, and GitHub.

There is transaction evidence too. Forbes reported that cielo24 monetized roughly 13 years of Slack, Jira, email, and Google Drive history during its wind-down.

The interesting asset was not simply “old Slack.”

It was years of company history spread across systems.

What makes one company's data stronger than another's?

When DataDeals evaluates an opportunity, four things matter early.

1. Can you reconstruct the work?

Can you follow a case from beginning to end?

The records can live in different systems. They just need enough identifiers, timestamps, or shared context to reconnect the trail.

2. Can you see the judgment?

The useful history contains decisions.

Diagnosis. Approval. Rejection. Escalation. Trade-offs. Changes in direction.

3. Can you see what happened afterward?

A repair is more informative if you know whether it held.

A sales conversation is more informative if you know whether the deal closed.

A manufacturing decision is more informative if you know whether the defect returned.

4. Would this history be difficult to recreate?

A buyer can scrape public information or generate synthetic examples relatively cheaply.

It cannot cheaply recreate ten years of real technicians diagnosing equipment, engineers fixing production bugs, underwriters making decisions, or operators responding to unusual failures.

That is what makes operating history scarce.

For the deeper valuation question, see How much is company data worth?.

What we would not take to market first

If we were reviewing a company's systems, a few things would usually fall toward the bottom of the list.

Generic documents. A giant shared drive is not automatically an asset.

Public information. If essentially the same data is available elsewhere, the buyer has less reason to pay your company for it.

Contact lists. Who a company contacted is much less interesting than how deals developed and why they succeeded or failed.

Raw conversations with no result attached. Calls, emails, and messages become more useful when you know what they were about and what happened next.

Records that cannot be connected. If the problem, discussion, action, and result exist but cannot practically be linked, much of the workflow disappears.

Our first instinct is to look for:

scarce expertise + repeated decisions + connected work + recorded outcomes

Find the strongest dataset inside your company

You do not need to inventory every file.

Pick one important workflow.

Ask:

Where do people in this business repeatedly encounter difficult problems, make judgments, take action, and record what happened?

Then fill this in:

Your notes are not saved or sent.

1. ProblemWhat started the work?
2. InvestigationWhat did people examine?
3. DecisionWhat did they decide?
4. ActionWhat did they do next?
5. OutcomeWhat happened in the end?

Now check five things.

Which systems hold each step?

Maybe the problem starts in Zendesk, the discussion happens in Slack, the action appears in Jira, and the result returns to Zendesk.

Can you connect them?

Look for shared IDs, job numbers, ticket numbers, customer IDs, equipment IDs, project IDs, timestamps, or other references.

Is the result recorded?

If the outcome exists only in someone's memory, it is not part of the dataset.

How much history survived?

Check what happened when the company changed systems.

A ten-year-old company with eight months of retained records does not have ten years of usable history.

What needs special handling?

Identify customer information, employee communications, patient data, confidential material, third-party content, or other records that may affect what can be included.

Rights determine what can ultimately go into a transaction.

They do not change the first question:

Is there an interesting asset here?

If you find one

Once you identify a workflow with real depth, the next questions are:

Who wants this kind of data? What portion can we license? How should we package it? What might buyers pay?

DataDeals helps companies answer those questions from the seller's side.

For pricing, read How much is company data worth?.

For the full transaction process, read How to sell your company's data to AI companies →.