Slack, a business to business collaboration application was born out of a growing frustration with email. Employees in organizations big and small were spending almost a quarter of their work day  on email, and yet felt out of the loop on what was going in the organization.
Slack’s value proposition was to be the modern email: to allow users to communicate with each other in a more quick and concise fashion while allowing for better transparency and relevance. And Slack took off, becoming the fastest growing enterprise application in history . But as Slack made workplace conversations easier, quicker and more transparent, there was a lot more information available, and instead of being overwhelmed by 50 emails employees were now overwhelmed with 100 slack messages instead. What was supposed to fix email fatigue had made the information overload problem worse. But that’s where machine learning comes in. By making machine learning an integral part of product development, Slack can filter the signal from the noise for its users and live up to its promise of being a more productive way to communicate.
Fortunately, Slack is aware of and has started to tackle information overload as their CEO acknowledged: “The flip side is there’s a lot more information. We’ve already put in hundreds of little things that collectively reduce the impact of massive flows of information in Slack.” 
Some of these little changes they have made in the short term are to put in better user controls such as to control notifications based on channels, to mute channels or people, and a do not disturb mode to turn off notifications automatically based on time of day.
In 2016, Slack also recognized the importance of Machine Learning in solving this problem and made a key organizational change with an eye towards the longer term. They established a Search, Learning and Intelligence group (SLI), with the charter of making users more productive, informed and collaborative.  Progress from the team has been slow so far. Their major product launch called Highlights, which was supposed to show users their important unread messages, has not been received well on social media, with most users finding the suggestions irrelevant. 
I believe that while Slack has identified the right problems, management hasn’t taken the right steps to fix it and I would do three things differently.
In order to understand what content is relevant to someone, Slack needs to understand who a given person cares about in their organization. So the first thing I would do is to first focus on using machine learning to develop a robust “Organization Graph” akin to Facebook’s “Social Graph”  which maps the strength of the relationship between a given person and each of their Facebook friends and powers their ranking algorithms . Getting this right will serve as a foundation upon which many product features can be built such as an improved search functionality and an improved Highlights.
Second, I would begin to use third party data from apps connected to Slack (e.g., Google Calendar and Dropbox) through its growing platform in the machine learning algorithms. This data on activity like which users often share files or have meetings or work on tasks with each other can be extremely useful in determining the aforementioned Organization Graph and identifying what messages are important to users at a given moment of time.
Slack can use Machine Learning to create an Organization Graph which it can leverage to identify high-signal content personalized for each userLastly, from an organizational perspective, the SLI team located in New York is the only one currently leveraging machine learning at Slack which is not enough in the long-term. It is also located away from the other product teams which are in San Francisco and actually own the product surfaces that make up Slack (e.g. the Channels or Direct Messaging surface). To remedy this, I would narrow the scope of the SLI team to focus on the broad machine learning algorithms and foundational infrastructure such as building out the Organization Graph which can then be used by the other product teams and encourage and equip other product teams to build on top of this infrastructure using machine learning to improve the product surface they own.
One thing I struggle with is how to balance “fixing” information overload with allowing for transparency and a feeling of being informed. After all, one of the upsides of Slack is that it makes much more information within an organization accessible to everyone. In an ideal world, Slack could present a list of the 10 messages you really need to see, filtering away the 100s which were noise. But perhaps amongst that noise there was some serendipity lost as well?
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