Why Do AI Labs Need Real Enterprise Data?

From answering questions to doing work

Early language models were judged mostly on how well they could write or answer questions. The focus has shifted toward agents: models that take an objective, use tools, make decisions and produce a result. A coding agent might be asked to fix a bug across several files. A business agent might be asked to reconcile invoices or update records in a CRM.

To get good at this, a model needs to see many examples of the full journey, not just the finished product. It needs to see what the goal was, what steps a capable person took, which tools they used, where they changed course and how the result was checked. That is a different kind of data from the articles and forum posts that make up much of the public web.

The shift also raises the bar for realism. An answer that is roughly right may be fine in a chat, but an agent that updates the wrong customer record or ships a broken change causes real problems. Developers therefore need examples that reflect the actual conditions agents will face, including the inconvenient details.

What the public internet is missing

Public sources are rich in finished, polished content. They are thin on the messy middle of professional work, and they rarely include the context that explains why decisions were made.

• Open-source code is plentiful, but most of it lacks the private product documents, business constraints and internal discussions behind real commercial software.
• Public how-to content shows idealised processes, not how teams actually handle exceptions, escalations and edge cases.
• Business systems like ERPs and CRMs are almost never visible publicly, so there are few examples of real records, workflows and the decisions made inside them.
• Long-running histories, where a project evolves over months or years, are rare outside private repositories and ticketing tools.

Why synthetic data is not a full substitute

AI developers do generate synthetic data, and it has real uses. But synthetic examples are produced by models or scripts that already carry assumptions about how work is done. They tend to be cleaner and more predictable than reality. Real enterprise data contains the odd naming conventions, half-finished migrations, conflicting requirements and pragmatic workarounds that real agents will face once deployed.

Real data is also useful as a check. Even when synthetic data is used for training, developers need realistic tasks and outcomes to evaluate whether a model actually performs well in conditions it was not designed around.

What makes enterprise records especially useful

The most useful records capture a complete workflow: the objective, the tool use, the decision process and a verifiable outcome. A pull request linked to an issue, with review comments, test results and a release record, tells a complete story. So does a support ticket that moves from customer report through diagnosis to a confirmed fix, or a purchase order that flows through approval, receipt and payment in an ERP.

Scarcity matters too. Data from specialised domains, older technology stacks, regulated industries or unusual workflows is harder to find than generic material. Completeness matters as well, since a history with large gaps is harder to learn from than one where the links between steps are intact.

Industry context adds another layer. The way a hospital schedules procedures, an insurer assesses a claim or a manufacturer handles a quality deviation follows rules and conventions that a general model will not pick up from public text. Records from these settings, once properly de-identified, can teach that context directly.

Training and testing both need real examples

AI developers use data in two broad ways. Training data is what a model learns from. Evaluation data is what it is tested against, to see whether it can actually perform a task it has not seen before. Both benefit from realism, but evaluation is where the gap between tidy examples and real work becomes most obvious.

A model can look capable on simple, artificial tasks and then struggle when faced with a real ticket that is vaguely written, a real codebase with years of history, or a real ERP screen with dozens of fields. Realistic tasks drawn from genuine operations help developers find those weaknesses before a model is put to work in a business like yours.

What is less useful

Being honest about what is not in demand saves time. Material that largely duplicates what is already public, such as copies of open-source libraries, generic marketing pages or standard templates, adds little. Very fragmented records, where only final outputs survive and the steps in between are lost, are harder to learn from. And data that cannot be cleanly separated from personal or client-owned information may not be worth the effort of including at all.

What this means for your business

If your company has years of history in code hosts, project trackers, support tools or business systems, you may hold exactly the kind of material that is in short supply. That does not mean every archive is valuable, or that all of it can be shared. Ownership, privacy and confidentiality come first, and some material will be out of scope.

The practical step is a conversation. With Momentra, the first contact needs only basic company details and no raw data. The value assessment then looks at data type, completeness, scarcity and the tasks it could support. Nothing is reviewed until an NDA sets out confidentiality, permitted use, retention and security, and sensitive information is removed or replaced through professional anonymization before further use.