When your AI system breaks—and what to do about it

The announcement of a new AI model or feature fills me with a mixture of anticipation and dread. I’m excited to see what “new thing” I might be able to do, but I also know that some of my existing processes and systems might break as a result. Every AI tool comes with the disclaimer that it can make mistakes. You might set up a workflow that is perfectly fine one day and perfectly awful the next. It’s an ongoing challenge. Unlike traditional software, once an AI system is up and running, it’s not set-it-and-forget-it. Even a system that seems to be working fine needs an occasional check-in or fine-tuning. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/11\/work-better-1.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/11\/work-better-mobile-1.png","eyebrow":"","headline":"\u003Cstrong\u003ESubscribe to Work Better\u003C\/strong\u003E","dek":"Thoughts on the future of work, career pivots, and why work shouldn\u0027t suck, by Anna Burgess Yang. To learn more, visit \u003Ca href=\u0022https:\/\/www.workbetter.media\/\u0022\u003Ewor…