Give every AI workflow a clear owner

An AI workflow can begin as a small experiment and gradually become part of everyday operations. Over time, people may depend on it even when no one has explicitly taken responsibility for its behavior. An inventory helps make those dependencies visible.

Start with a practical record for each use case: its purpose, the people using it, its owner, the information it receives, its model or service provider, and the actions its outputs can influence. Add a plain-language description of what could go wrong and who would notice.

Ownership should cover the whole workflow. Engineering may operate the integration, while a business owner decides whether outputs are useful. A data owner can explain which information is permitted, and the people responsible for security can review the relevant controls. Record the decision owner for unresolved questions.

Pay particular attention to changes. A pilot that originally drafted text may later gain permission to send messages or update records. That change alters the consequences of an incorrect output. The review process should respond to expanded access, new data sources, new providers, and changed user groups.

Evidence makes reviews more useful. Link the use case to its evaluation examples, access rules, known limitations, and a history of material changes. Keep confidential prompt contents out of broadly visible tracking tools. Record the reason for decisions so a future reviewer can understand what was accepted and why.

Keep the initial inventory small enough to maintain. A named owner, a current description, and a clear review trigger are more useful than a detailed register that nobody updates. Build the habit of reviewing the record when the workflow changes.

This article presents a general operating approach. Organization-specific legal or regulatory decisions require appropriate review; the website does not claim that maintaining an inventory establishes compliance.