By SYG Consult, LLC · Published
Begin with a task, not a model
The most convincing AI demonstration is not necessarily the best operating improvement. Start with a repeated task that has an identifiable input, a useful output, and someone responsible for acting on that output. If the task cannot be described plainly, adding a model will not clarify it.
For example, drafting a first pass at a response to a standard inquiry may be useful. Letting a model decide whether to make a binding promise to a customer is a different proposition. Both may look fluent in a demo; only one has a straightforward review boundary.
Apply a four-part filter
First, ask whether there is enough trustworthy context available at the moment of use. Second, decide what a wrong answer would cost and whether a person can catch it before it matters. Third, check whether the output fits naturally into the existing workflow. Fourth, define how success will be measured against the current way of working.
A good candidate might summarize a long case history for an employee who will verify it. A weak candidate might produce a confident recommendation from incomplete records with no way to trace its sources. The distinction is less about technical novelty than about control.
Pilot with a review loop
Run a narrow pilot on real, representative examples. Capture the time spent reviewing and correcting results, not just the time saved generating them. Track omissions, exceptions, and cases where the right action was to abstain or escalate.
Keep the human decision point explicit. If the pilot reduces total effort without lowering accuracy or accountability, expand it deliberately. If it merely shifts work into checking an unreliable output, improve the source data or the process before scaling the technology.