Is my company's data valuable to AI? Answer these 10 questions
Your company's data is likely valuable to AI developers if you have 50+ full-time employees at peak (contractors excluded), several years of records across many business systems, records that show how work turned out, the rights to license them and a sponsor willing to consider an exclusive license. Answer the ten questions below to find out.
The short answer
Your company's data is valuable to AI developers when it records real work in depth: who did what, in which system, and how it turned out. Size and age matter because they decide how much of that record exists. A 120-person IT services firm with eight years of tickets, project files and client email can have more to offer than a far larger company that deleted its archives last year.
The reason is a shift in what AI developers build. They are moving from models that answer questions to agents that carry out multi-step tasks, and training those agents takes examples of how tasks are done inside real businesses. Those examples are scarce on the public web, which is why company documents are valuable for AI when they are permissioned and rights-cleared.
Who this self-check is for
It is written for owners, CEOs and CFOs of US companies. If an advisor, investor or peer forwarded it to you, the ten questions take a few minutes and need no data, passwords or exports. Answer for the company as a whole, including acquired businesses whose records you still hold.
Nothing here commits you to anything. A license only happens if you later agree a price and terms and sign, and the company keeps ownership of its data throughout.
The 10-question self-check
Tick each statement that is true today.
Scale and history
- 1. Headcount. At its peak the company had 50+ full-time employees (contractors excluded).
- 2. Years of operations. You have several years of documented operations, mostly in English; five to ten or more years is stronger.
- 3. Archives survived. Mailboxes of former staff, retired tools and old file servers were archived or exported rather than deleted.
Systems and substance
- 4. Many systems. Work is recorded across email, Slack or Teams, shared drives, CRM, finance, a support desk, project tools or engineering tools. Most strong companies run 10-15 or more.
- 5. Outcomes are visible. Records show how things ended: tickets resolved or escalated, deals won or lost, quotes accepted, approvals granted or refused, projects delivered on time or late.
- 6. Someone can export it. An internal admin, IT lead or managed service provider holds admin access and could produce exports if you asked.
Rights and restrictions
- 7. It is your record. The company created the material in its own work. It is not mainly records you hold for clients as an agency, outsourcer or processor.
- 8. It is not mainly personal or patient data. The value lies in business activity, not in consumer personal data or medical records and claims.
- 9. Not already licensed for AI. The same records have not been licensed to an AI developer or data buyer before.
Sponsor
- 10. Someone can say yes. An owner, CEO, CFO or other authorized representative would consider a one-time payment for a license that is typically exclusive for AI training for an agreed term.
How to read your score
Count the ticks, but treat questions 7, 8 and 9 as knockouts: a no on any of them outweighs a high total.
| Result | What it likely means | Next action |
|---|---|---|
| 9-10 ticks, no knockouts | A strong candidate on paper | Run the company fit checker, then apply |
| 7-8 ticks, no knockouts | Promising, with gaps to confirm | Find out who controls exports and how far archives go back |
| 5-6 ticks | Possible later, not now | Preserve archives and exports before any system changes |
| A no on 7, 8 or 9 | Not licensable as things stand | Resolve consent or narrow the scope first, or stop here |
| Under 50 full-time employees at peak | Below the baseline | Revisit only if headcount grows |
A high score is a reason to look closer, not a promise of a deal or a price.
Are old emails and archived systems worth anything?
Often, when they connect to outcomes. An eight-year-old thread that runs from a customer complaint to a credit note to a changed procedure shows a full decision cycle; a folder of newsletters and calendar invites shows almost nothing.
| Record | More useful when | Less useful when |
|---|---|---|
| Email archives | Threads follow a deal, project or dispute to its end | Mostly notifications, marketing or auto-replies |
| Support tickets | The conversation, internal notes and resolution are kept | Tickets were auto-closed or their bodies purged |
| CRM history | Stage changes, notes and won or lost reasons are logged | Only a contact list survives |
| Shared drives | Proposals, SOPs and reports exist in dated versions | Templates and duplicates dominate |
| Engineering tools | Code reviews, pull requests and linked issues are intact | Only final builds remain |
| Retired systems | A complete export was kept at shutdown | The vendor account lapsed with no export |
Continuity is the common thread. Records from several systems that refer to the same customers, projects and dates are worth more together than any single system, and an export from a tool you stopped using years ago still counts.
Red flags that stop a license
Some answers end the conversation, at least for now:
- The records mainly belong to your clients, and they have not agreed to licensing.
- Most of the value is consumer personal data, or patient records and claims, with no licensing basis.
- Archives were deleted, or nobody can export the data.
- A court, trustee or assignee controls the company's assets and has not been involved.
- Records were produced with AI tools in order to sell them.
The rights side is covered in more depth in is it legal to license business data to AI developers.
What happens if you pass?
You control each step, and you can stop at any of them:
- Run the company fit checker, a preliminary, non-binding screen that asks for no contact details.
- Apply at sourcex.si/apply. SourceX then reviews size, history, data breadth and rights with you.
- If you qualify, your team completes a data inventory: each system, the years it covers and what can be exported.
- You agree one all-in price and the license terms before any buyer sees the opportunity. SourceX's fee is included, with no separate charges.
- AI labs and data buyers review it; once a company is deal-ready, buyers typically respond within about two weeks.
- Nothing is delivered until you sign and authorize it, under redaction rules agreed before work begins. Payment is one-time, typically within about 60 days of invoicing once the buyer selects the data.
How it works explains each stage. If you are weighing a license against building internal AI tools, read should we build our own AI model instead of licensing our data; the two can coexist. And if you assume only large enterprises can do this, the comparison of mid-sized company data and enterprise data sets out how the two differ.
If an advisor sent you this page
If the person who forwarded it is a SourceX referral partner, any reward they earn is a share of SourceX's fee and is never deducted from what your company receives. Ask them for their referral link before you apply so the introduction is recorded against them.
Next step
If you scored well, run the fit checker and apply at sourcex.si/apply. If you are an advisor or investor planning to forward this self-check to owners you work with, register as a partner first so your introductions are credited.
- Step 1Share your linkSend your personal link to a company you know.
- Step 2Company appliesThe company applies itself at /apply.
- Step 3Buyer selects and paysThe buyer selects and pays for the data and SourceX receives its fee.
- Step 4You get your rewardYour share of SourceX fees becomes payable.
Common questions
Do we have to share any data to find out whether it is valuable?
No. The self-check and the company fit checker need no data, passwords or exports. If you apply, SourceX asks about your systems, the years they cover and your rights, and your team later lists them in a data inventory. Records are only prepared after you agree price and terms, sign the agreement and authorize delivery, under redaction rules agreed before any work begins.
Our company was acquired or has wound down. Can its records still qualify?
Yes, if the data still exists. Operating, acquired and wound-down companies can all qualify, provided the records were preserved and someone with authority can approve a license. If a court, trustee or assignee now controls the assets, they need to be involved before anything proceeds. Headcount is measured at the company's peak, so a smaller team today does not rule it out.
Is a high score a promise of a deal or a price?
No. The self-check is a preliminary indicator, and so is the fit checker. Price depends on the depth of your records, how clean the rights are and buyer demand at the time. Nothing is binding until you agree price and terms and sign, and a deal happens only if a buyer selects the data.
We outsource IT to a managed service provider. Is that a problem?
Not in itself. Plenty of companies rely on an MSP for admin access, and the MSP can run exports when you authorize them. What matters is that the records are the company's own and that someone can produce complete exports from each system. If the MSP contract limits how data may be handled, review it before you start the inventory.
What should we do now if we might want to license later?
Protect the record. Before retiring a tool, cancelling a vendor or closing a former employee's mailbox, take a complete export and note what it covers. Where your policies allow, stop retention settings from purging tickets and chat history. Write down which systems hold which years. That preserves the option without committing the company to anything.
Related pages
- Why company documents are valuable for AI
- Is it legal to license business data to AI developers in the US?
- Check Company Fit for Data Licensing
- How SourceX US company data referrals work
- Should we build our own AI model instead of licensing our data?
- Mid-sized companies vs enterprises: whose data is easier to license for AI?
Free resources
- Enterprise value calculator — Enterprise value from equity value, debt and cash.
- Earnout scenario calculator — Probability-weighted earnout value and its present value.
- Profit margin calculator — Profit and margin across three scenarios.
- All free tools · MCP resource center
By SourceX Partnerships Team · Published 2026-10-09 · Updated 2026-10-09
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