Business data
Records your company already has
Everyday business records can capture something AI needs: real problems, decisions and outcomes. Start with what your company already creates—not new data collection.
Customer conversations
Support tickets, email threads and service resolutions can show how a question becomes an answer. Useful context may include the issue, the actions taken and the outcome.
Operational workflows
Process logs, task histories and documented decisions can explain how work gets done. Sequences of steps and exceptions may be more informative than isolated entries.
Documents and knowledge
Internal guides, technical documentation and approved business documents can capture specialist knowledge. Ownership, confidentiality and third-party material need review.
Structured business records
Catalogs, inventory histories and transaction records may describe real-world activity. Completeness, consistent fields and clear context help establish whether a dataset is useful.
Useful context, not just volume
A complete conversation with a resolution may be more informative than many isolated messages. Describe linked events, timestamps, decisions and outcomes, as well as any missing fields or duplicates. Counts alone do not establish commercial value.
What to leave out
Flag passwords, payment details, customer identifiers, restricted health information, confidential third-party material and anything your company cannot license. Do not send those records in an enquiry; agree exclusions and review requirements first.
Prepare a simple inventory
For each collection, note its purpose, owner, approximate count, date range, language and available export format. Identify one system owner who can explain the fields and one legal or privacy contact who can review permissions.
These are examples, not approved data categories. Suitability depends on the records, rights, privacy considerations and AI-lab demand. Do not send customer information or confidential files in an initial enquiry.