The fastest-growing business app is relying on machine-learning tricks to fend off a deluge of messages—as well as competition from Facebook and Microsoft.
Some of the technology is already live. One feature shows which people within a company talk about particular topics most often in Slack and where those discussions take place. The information, which appears when users conduct searches in Slack, is meant to pinpoint subject experts so people can direct questions to their most knowledgeable and accessible colleagues. Another feature, added last year, evaluates all of a user’s unread messages, across all Slack channels; highlights up to 10 of the ones its algorithms deem most important; and presents them in a single list.
Both innovations rely on a data structure that Weiss calls the “work graph.” It essentially looks at companies that use Slack and analyzes how the people within them are interrelated, where in the app their discussions are taking place, and what topics are being discussed. If the term sounds familiar, it’s because Google and Facebook have similar structures—the “knowledge graph” and the “social graph,” respectively. But while Google studies public data and Facebook promotes the idea of a single, global network of relationships, Slack thinks of the work graph as specific to each company—a representation of how work is structured within it.