Editor’s note: This was originally published on Slack’s engineering blog. At Slack, we’ve long been conservative technologists. In other words, when we invest in leveraging a new category of infrastructure, we do it rigorously. We’ve done this since we debuted machine learning-powered features in 2016, and we’ve developed a robust process and skilled team in the space.

We recently heard from some in the Slack community that our published privacy principles weren’t clear enough and could create misunderstandings about how we use customer data in Slack. We value the feedback, and as we looked at the language on our website, we realised that they were right. We could have done a better job of explaining our approach, especially regarding the differences in how data is used for traditional machine learning (ML) models and in generative AI.

Slack has enabled small businesses around the world to do more with less. From delivering world-class customer experiences to centralising communication with internal teams and external partners, Slack has helped teams everywhere to make the most of every resource. The bottom line? Small businesses rely on Slack to power their operations and grow their business at scale.