How to choose your first AI automation pilot
Start with one repetitive task whose output a person can check. Test it against the current process before giving it more responsibility.
A useful first pilot has a clear input, an expected output, and a responsible reviewer. It should help answer a business question: does this approach improve the task enough to justify its costs and ongoing care?
Choose a task you can explain
Look for repeated reading, sorting, extracting, searching, or preparation. Examples include pulling specified fields from a standard document, finding an answer in approved internal material, or drafting an internal summary for review.
Avoid starting with “automate the business.” Pick a bounded task. If a fixed rule or ordinary system connection can do it reliably, test that option too. AI does not need to be part of every improvement.
Check the information source
Identify where the inputs come from, whether the business has permission to use them, and which information the pilot actually needs. Determine how the source stays current and how a reviewer can trace an answer back to it.
Internal assistants need approved material and a way to acknowledge missing information. Document extraction needs representative formats, including incomplete and unusual examples. A demonstration using one clean input cannot establish that the full task works.
Define what the system may do
Separate preparing information from taking action. A first pilot might draft a summary or suggest a route while a person approves the result. Define the actions, access, and exceptions during scoping rather than leaving them implicit.
Start with tasks whose mistakes are detectable and whose consequences are limited. Decisions affecting people's finances, employment, health, or legal position need appropriate specialist oversight and a separately defined scope.
Build a representative evaluation set
- Typical examples from the task you want to improve.
- Incomplete inputs and cases with missing information.
- Conflicting, duplicate, or unusually formatted information.
- Questions that the approved source cannot answer.
- Cases that should reach a person rather than proceed automatically.
For each example, record the expected result and what would make the output unacceptable. Keep the evaluation cases separate from examples used to tune the pilot where practical.
Measure the whole task
Compare the current process and the pilot using the same cases. Measure completion time, review time, corrections, missed exceptions, and recurring provider costs. Include the effort needed to maintain the information source and workflow.
Decide what counts as success before reading the results. A faster draft is not necessarily a faster completed task if a person must spend more time checking it. Record results as observed measurements, with the size and limits of the test.
Make the next decision explicit
The pilot can lead to expansion, revision, or a decision to stop. Review whether the output is useful, mistakes are visible, and someone can own the workflow. Agree on monitoring, review responsibilities, costs, and handoff before broader use.
Data organization is often part of the foundation. Business Data Hub illustrates incoming report tracking and source history. Those details help a team establish where information came from and whether the expected inputs arrived.
A one-page pilot brief
Task: the repeated step we want to improve.
Inputs: the approved information and representative examples.
Output: what a useful result looks like.
Review: who checks it and how exceptions reach them.
Limits: what the system may and may not do.
Evaluation: time, mistakes, costs, and the threshold for continuing.
Owner: who maintains the workflow after the pilot.