Where does AI actually make sense in a business process?
AI complements rule-based automation rather than replacing it. It is strongest when text, documents or less structured information must be interpreted; predictable business rules should usually remain deterministic.
1. Start where people read and retype information
Invoices, orders, emails and meeting notes are good candidates for AI-assisted pre-processing: extract data, classify or summarise before the workflow continues.
- document data extraction
- email classification
- summarisation
- unstructured information processing
2. Keep decision responsibility explicit
AI output is not deterministic in every situation. For financial, legal or otherwise critical steps, define when automation is enough and when a person must review or approve.
- human review
- confidence thresholds
- exception handling
- auditability
3. Data access matters more than the model name
Enterprise AI is only useful when it can securely access the right documents, systems and business context. Permissions and data handling belong at the start of the architecture.
- business data sources
- access control
- privacy requirements
- audit and logging
4. Pick a measurable first use case
A good first AI use case has a visible current cost and a measurable outcome, such as processing time, manual data entry or time spent searching.
- baseline effort
- error rate
- cycle time
- number of human interventions