AI agent or ordinary automation? A buyer’s decision guide
If the business rule fits on a sticky note and both systems already hold the necessary fields, ordinary automation is a strong first choice. AI becomes useful when a person currently reads, interprets or summarizes messy inputs before the rule can run. Many practical projects use both.
Follow one purchase request
Imagine a department emails, “The new starters need two more screens like the ones upstairs.” A person must interpret the request, identify missing information and decide who should clarify it. An AI component could extract “two displays,” look for an approved catalog match and prepare a question about the model. An employee verifies the selection.
Once the item, quantity, department and spending limit are known, route the approval with explicit rules. A controller should be able to inspect the limits and see why the transaction went to a particular manager. A change to the approval policy then requires updating a rule, without rewriting how the agent reads requests.
Run the substitution test
Ask what would happen if a customer or employee submitted a structured form instead. If a dropdown and two required fields eliminate the whole difficulty, test that small change before buying a custom agent. If the team would still be reading attachments from outside organizations, the case for an interpretation step is stronger.
Document each decision in three columns. Record the input, what judgment is required and who owns the final action. For the monitor request, the email reader owns extraction, the requester confirms the catalog item and the purchasing system applies the threshold. Compare proposals on whether they preserve these boundaries. A technically impressive demo adds little if no one can explain who authorized the order.
Put this into practice
Bring a sample of the work and the tools your team uses. We can help define a first project, who reviews it, and how to tell if it works.
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