A Practical Method for Choosing the Right AI Work Boundary

Description and use

<p>Teams often talk about adopting AI as if the hard part were selecting a model. In practice, the harder decision is choosing the boundary around the work. A tool can draft, sort, compare, summarize, or recommend, but a useful implementation also defines what remains visible to a person. That boundary determines whether an experiment becomes a dependable workflow or a source of quiet rework.</p> <h2> Begin with the decision, not the feature </h2> <p>Start by describing the decision that the workflow supports. Who needs an answer? What will they do with it? Which errors are merely inconvenient

Problem

<p>Teams often talk about adopting AI as if the hard part were selecting a model. In practice, the harder decision is choosing the boundary around the work. A tool can draft, sort, compare, summarize, or recommend, but a useful implementation also defines what remains visible to a person. That boundary determines whether an experiment becomes a dependable workflow or a source of quiet rework.</p> <h2> Begin with the decision, not the feature </h2> <p>Start by describing the decision that the workflow supports. Who needs an answer? What will they do with it? Which errors are merely inconvenient

Outcome

<p>Teams often talk about adopting AI as if the hard part were selecting a model. In practice, the harder decision is choosing the boundary around the work. A tool can draft, sort, compare, summarize, or recommend, but a useful implementation also defines what remains visible to a person. That boundary determines whether an experiment becomes a dependable workflow or a source of quiet rework.</p> <h2> Begin with the decision, not the feature </h2> <p>Start by describing the decision that the workflow supports. Who needs an answer? What will they do with it? Which errors are merely inconvenient

Evidence

A Practical Method for Choosing the Right AI Work Boundary

Source