What makes business data ready for AI?

An AI pilot may work on a selected extract and struggle when it meets the next month's records. A familiar field has changed, a contract has been amended or a recipient cannot access the evidence. The demonstration covered only part of the work.

The pilot can restart around those specific gaps. The team can identify the records and rules its task needs, then build a process that maintains them beyond the demonstration.

Define readiness around the task

A defined business task makes data preparation possible to evaluate. Its decision determines the required records and freshness. Its recipient determines the detail needed to understand the answer. It also establishes what a useful answer would change in daily work.

A question about a monthly operating measure needs an agreed period and calculation. A question about a document needs the relevant passage and its context. The same model can serve both, but the preparation and evaluation differ.

Choose a first scope that can be examined in full. A recurring investigation may need several sources; each should contribute to the decision it supports. Adding unrelated data makes the pilot larger without making its success easier to judge.

Establish the meaning of the answer

The question determines which meaning of a valid record applies. Invoicing, recognition and receipt of payment are different events, even when they concern the same work. Accurate values from the wrong event can produce an answer that is internally consistent and unsuitable for the decision.

A business model connects the entities, relationships and measures used in the question. It also records which dates and exclusions each calculation needs. This gives reports and AI a common interpretation to use when someone asks a familiar business question.

Quality checks should follow those definitions. Duplicate keys matter when they repeat a transaction in a total. Missing assignments matter when they remove people from a departmental comparison. The checks become more useful when their effect on the answer is understood.

A stalled pilot may therefore need a revised model or a missing source before it needs a different AI model. The delivery team can trace a known answer to the records and calculations required, then address the specific gap.

Turn documents into usable evidence

AI can use documents when the answer remains traceable to the relevant source and context. A contract or PDF can supply a passage to read, fields to analyze, or both. The chosen task determines which form will be useful.

For repeated analysis, selected document fields can be extracted into rows. A contract identifier and end date can support a renewal review when the row also points to the page containing the date. Field descriptions need to explain what to extract, including how to distinguish an original term from an amendment.

An extracted value needs review against the document. A confidence label can help prioritize inspection, but it does not establish that the value is correct. The original page, document version and surrounding language let the reviewer assess what the extraction captured.

In Databasin, PDF extraction can create queryable records with source-file, source-page and run information. When document archiving is enabled, citations can open the retained document at its page. The OCR task is currently beta. Our team can implement the extraction and review workflow around the business use.

Apply access to the whole path

Access rules must follow the evidence through to its recipient. Permission to see an aggregate measure may leave the individual records restricted. The analytical route must preserve that distinction when generating the answer and displaying supporting material.

Document permissions belong in the same design. The intended recipient needs authorized access to the source behind a citation. Copying a restricted file into a new location should not accidentally broaden the audience for its contents.

The model-provider route is another part of this design. The implementation establishes which records or passages are sent for processing and where that processing occurs. These choices belong with the organization's deployment and access requirements.

Read-only investigation also needs to remain distinct from operational action. An explanation can help a person decide what to do. Any task that changes records requires its own authority and conditions, even when the investigation that preceded it was successful.

Test answers people already understand

Known cases reveal whether the AI uses the right records and reasoning. A completed investigation provides an expected result and the evidence behind it. Cases involving corrections, missing inputs and ambiguous wording help test the difficult parts of the task.

For a numerical answer, the reviewer should inspect the executed query. The population, dates and joins need to match the question. A query that runs successfully may still calculate the wrong measure, so execution alone cannot establish correctness.

For a document answer, the reviewer should follow the citation and read the relevant passage. The passage may support a date while leaving an exception unresolved. The explanation should state what the passage supports and which exception still needs review.

Testing should also include an unavailable source or permission. The workflow needs to show an incomplete state or request review when it cannot support an answer. A confident-looking response to missing evidence can be more difficult for a recipient to detect than a visible failure.

Keep the result useful over time

The pilot needs continuing work as its inputs change. A new source record, definition or document version can alter the answer that recipients have learned to use. The operating process needs to detect those changes and repeat the relevant acceptance cases.

During rollout, the team shows recipients what the explanation covers and how to inspect its evidence. It also establishes where they can report a problem. Their use of the output reveals whether the workflow reduces investigation or merely adds another summary to read.

Continuing support can cover data preparation, the AI task and improvements to the surrounding process. These responsibilities should stay connected because a failure can occur at any point between the source and the decision. The useful outcome is a business capability that keeps working beyond the selected demonstration.

Example: reviewing contract renewal dates

This illustrative renewal review follows extracted contract dates through amendment checks, a review window and inspection of the source pages.

Extraction makes the documents easier to examine as a group. The source pages and exception states keep the review connected to what the documents actually say.

Decisions before routine use

1. Select a useful decisionChoose a recurring question whose answer can be checked. Agree what the recipient needs to see before using that answer.
2. Approve the business meaningApprove the definitions, periods and document versions to use. Decide which conflicting evidence should stop the workflow for review.
3. Set access and authorityDecide who may inspect the inputs and who may act on the result, keeping investigation permissions distinct from operational actions.
4. Accept the operating arrangementApprove an operating arrangement that maintains the inputs and evaluation cases. Include support for the people using the result after the pilot.

Sources