AI makes unfinished operating work easier to see. A team wants automation, but the request arrives in five formats. Leaders want a summary, but nobody agrees on the source record. Employees want a faster answer, but the decision rule changes by manager.
The technology is not creating every problem. It is removing the tolerance for work that depends on memory, private judgment, and disconnected data.
Choose the Business Result Before the AI Use Case.
A use case should begin with work the company needs to improve. Name the customer, employee, operating, risk, or financial result. Then identify the task, decision, information, and owner involved.
This prevents the company from measuring activity such as prompts created or tools activated while the underlying business result stays the same.
Standardize the Input Before Automating the Output.
AI can work with varied information, but the business still needs minimum facts and a reliable source. If every request starts differently, the generated output will carry the same uncertainty.
Define required inputs, approved sources, ownership, privacy limits, review responsibility, and the record that should remain after the work is complete.
Keep Human Judgment Visible.
Some work can be automated. Some work can be prepared by a system and approved by a person. Some decisions should remain fully human. The company needs to decide which is which.
Document the review point and the evidence the reviewer should examine. A vague instruction to check the output is not a control.
- What task is the tool performing?
- Which sources may it use?
- Who reviews the output?
- What error would create meaningful harm?
- Where is the final decision and reasoning recorded?
Train the Workflow, Not Just the Prompt.
Employees need to understand when to use the tool, what information can be entered, how to evaluate the result, what to correct, when to stop, and where the completed work belongs.
Managers need a separate view. They must know what good use looks like, which measures matter, and how to respond when people create unapproved workarounds.
Measure Work Quality and Operating Value.
Time saved matters, but it is not the whole business case. Review cycle time, rework, error rate, customer outcome, decision quality, adoption, support needs, and the amount of work that still moves outside the approved system.
AI adoption becomes business transformation when the company redesigns the work around a measurable result and gives the new method an owner.
Research and further reading
Sources Used in This Field Note
Written by Megan Logan
Founder of Prestige Pointe Partners in Raleigh, North Carolina