AI only works where the process is clear.
Without defined inputs, rules and outcomes, artificial intelligence becomes an expensive layer of improvisation.
Technology does not fix an indefinite flow
If each person performs the task in a different way, there's no benchmark to evaluate the AI's response. The model can produce compelling text and still create more revision, risk and rework.
First, describe the job without citing the tool. What information comes in, what decision needs to be made, what exceptions exist and what should come out at the end.
Start with a verifiable task
Good first applications have a clear boundary and allow you to check the answer before it affects the customer or the operation.
- Sort requests and forward them to the right queue.
- Summarize documents with mandatory fields.
- Prepare a first response for human review.
- Find discrepancies in records following defined criteria.
Define the person's role
Not every output should trigger an automatic action. In financial, legal, medical or other decisions that directly affect the client, the review needs to be explicit. The person should know what to check and have access to the source used by the AI.
The aim is to reduce mechanical work without hiding responsibility.
Measure the entire process
Generation time alone proves no value. Compare the total time, the number of corrections, the cases requiring an exception and the quality of the accepted output.
When the flow is clear, AI occupies a specific place. When it is not, AI only makes improvisation faster.
How do you pick a first AI use case?
Choose a task with known input, verifiable output, and limited impact when error occurs. Classification, extraction, summary and drafting often allow for objective comparison. Irreversible decisions, unsupervised service and financial actions require greater controls before any autonomy.
| Criteria to be fulfilled | Question | Positive sign |
|---|---|---|
| Entry | Does the information come in a recognizable format? | Recurring documents, fields or messages |
| Exit | Can one person verify the result? | answer, category or comparable data |
| Risk | Can a mistake be stopped or reversed? | review before action |
| Volume | Is there enough repetition to measure? | frequent and stable sampling |
| Value | Does the gain show up in the whole process? | less waiting, correction or mechanical work |
NIST organises AI risk management into four functions: govern, map, measure and manage (The NIST AI Risk Management Framework is designed to:, 2024). For an SME, this starts with clear responsibility, registration of use, quality criteria and a defined response when the model fails.
How do you test quality without relying on a demonstration?
Put together a sample of common cases, difficult cases, and situations that shouldn't be answered. Compare the output to an accepted standard and record hit, correction needed, review time and risk. A demonstration chosen by the supplier shows possibility. A sample of your process shows adequacy.
Evaluate consistency as well. The same input should produce responses within an acceptable limit. When the variation is high, narrow the scope, improve context and instructions, or keep the decision with one person.
What should be monitored after launch?
Measure use, cost per task, total time, correction rate, exceptions and incidents. Keep the version of the model and the instructions when necessary to investigate results. If the model changes, the quality needs to be tested again.
The OECD recommends transparency, security and accountability throughout the lifecycle of AI systems (The OECD AI Principles, 2024). In practice, someone needs to know where the AI operates, what data it uses and who is responsible for the final decision.
What mistakes appear in the first projects?
- Choosing a process too big for the first test.
- Measure only generation speed.
- Failure to separate model flaw, rule flaw and lack of context.
- Sending sensitive data without verifying provider, retention and access.
- Automate the action before validating the quality of the recommendation.
Frequently asked questions about AI in processes
Do I need an autonomous agent to get started?
No. An AI stream can classify, summarize, or suggest while the person remains responsible for the action. This design often produces safer learning and makes it clear where autonomy would actually save work.
How do you know if AI pays?
Compare total cost, revision time, error reduction and capacity released. The price per call of the model is only one part. Integration, monitoring, security and maintenance also come into the bill.
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