AI-assisted data processing

Data tasks where AI can help

Validation is what makes it usable

AI extraction can be impressively accurate on typical examples and wrong in subtle ways on unusual ones: a misread digit, a date in the wrong format, a total taken from the wrong line. Validation rules catch many of these: totals that do not add up, values outside an allowed list, dates in the future, references that do not exist in your system. Items that fail validation go to a person.

Without validation, AI errors pass quietly into your records.

A sound data processing run

One-off clean-ups and ongoing processing

• One-off clean-up

  Standardising a product catalogue or tidying a customer list before a migration.

• Ongoing processing

  Extracting details from each new invoice or application as it arrives.

• Different checks

  One-off jobs can be reviewed thoroughly once; ongoing ones need continuous validation and sampling.

Personal and sensitive data

Data processing often involves personal information: names, addresses, financial details, sometimes health or employment data. Before any data is sent to an AI provider, check the provider's terms, remove or mask fields the AI step does not need and record the processing in your privacy documentation. For special category data, take advice before using external AI services at all.

People stay responsible

AI can do the reading and sorting, but the business remains responsible for the accuracy of its records and any decisions based on them. Designing the process so that people review uncertain items, check samples and can easily correct mistakes keeps that responsibility practical rather than theoretical.

Define the output first

The most important step happens before any AI is involved: deciding exactly what the output should look like. Which fields, in which format, with which allowed values? What should happen when a value is missing or unclear? A clear output definition makes the AI step easier to instruct, the validation rules easier to write and the results easier to import into your systems.

It also forces useful business decisions, such as how many product categories you really need or what counts as a complete record.

Data processing before a migration

A common use is preparing data for a move to a new system: standardising product attributes before a new store launches, or cleaning customer records before a CRM migration. Clean data migrates more smoothly and makes the new system useful from day one. The checks described here matter even more in this case, because errors are copied into the new system.