Guide
When AI Belongs in a Business Workflow
Questions to ask before adding AI to a business process, including input quality, review, access, and the cost of mistakes.
AI can help with documents, summaries, unusual patterns, and repetitive classification work. Its value depends on the job, the available information, and what happens when the output is wrong.
A short review of the workflow can show whether AI is useful, where a fixed rule is enough, and where a person should remain responsible for the decision.
Describe the task in working language
Write down what arrives, what the team needs to learn from it, and what action follows. A task such as reviewing incoming documents is still too broad. Identifying the document type, extracting specific fields, and flagging missing information are clearer pieces of work.
Check the quality of the input
AI needs enough context to produce a useful result. Scanned documents, inconsistent labels, missing history, and conflicting records can weaken the output.
Use representative examples during testing, including incomplete and unusual cases. A clean demonstration file says little about how the workflow will behave on an ordinary week.
Decide what a person must review
Review is especially important when the output affects money, customer communication, compliance, access, or a person's record.
The system should show the source material beside the proposed result when possible. It should also make uncertain cases easy to find instead of presenting every answer with the same confidence.
Compare AI with a fixed rule
Stable rules are often the better tool for exact checks. Dates, totals, required fields, and known identifiers can usually be handled predictably.
AI becomes more useful when the work involves varied language, documents with changing layouts, summaries, or patterns that are difficult to express as a fixed list of conditions.
Set limits before connecting production data
Agree on the information the system can access, where it will be processed, who can use the output, and how long records will be retained.
Begin with sample data or read-only access when practical. Expand access after the behavior and review process are understood.
Test the cost of a mistake
Ask what happens when the system misses something or produces a wrong answer. A low-risk draft may only need a quick review. A high-impact decision may require stronger validation, approval, and an audit trail.
A useful AI feature fits inside a process people can understand and supervise. Its job, inputs, limits, and reviewer should be clear.