4 min read
What makes a business record useful for AI?
Start with a complete task: the request, the work, and the result.
Look for a sequence of work
A record becomes easier to understand when it shows what someone wanted, what the team did, and what happened next. Those three parts give a buyer context for a learning task.
For example, a support ticket might include the original issue, the questions asked, the troubleshooting steps, and a confirmed resolution. This is an illustrative example, not a Korra customer record.
Keep the context that explains decisions
A final answer alone may hide the most useful part of the work. Role labels, task status, relevant dates, and approved links between records can explain why a decision was made.
A buyer should be able to tell an ordinary case from an exception. Preserve useful context within the agreed privacy limits.
Describe quality before counting volume
Count complete tasks as well as individual rows or messages. Note duplicate records, automatic replies, missing outcomes, inconsistent fields, and retired guidance.
Ten thousand disconnected messages and ten thousand complete cases are different datasets. Explain what your count represents.
Confirm the right to use the source
Ownership of a system does not settle every right in its contents. Customer agreements, employee information, third-party materials, and confidential discussions may restrict a proposed licence.
Start with a source inventory. Identify the sources that need review before anyone prepares a sample.
Put it into an inventory.
Describe the records your team holds. You can start without a raw export.
See if your data qualifies