AI Applied: AI Slack
The following is a future historian’s view of potential events that might occur today or in the near future. It is intended to give some insight into the potential uses, applications, and consequences of the use of AI in the marketing industry and beyond.
In late 2023, Slack, the all-purpose communication hub used by over 100,000 businesses, added an AI query capability to their application. Previously, the rudimentary search function could only search based on keywords and user names, restricting its use to the information known by the user.
Essentially, the AI consumed all data within a company’s Slack channel, assessing the information, intent, mood, subject, and patterns of all user conversations and shared data, including documents, links, pictures, videos, and other media. For an additional fee, the AI could also access patterns of in-formation collected by all Slack channels managed by the application to make judgments based on conversations within a single network.
The new search function massively enhanced each user’s ability to find conversations based on in- tent, rather than simple terms. The reach of these searches was restricted by the level of each person in the company.
For example, a lower-level user could search based on general project information, such as, “who was the last person to work on the Weyland project.” This capability allowed any user, no matter how limited their knowledge of the subject they were searching to get immediately up to date on the status of projects and other company events.
However, for higher-level users in management positions, the capability was expanded to recognize patterns in user conversations and comments to judge potential actions. For example, “who in the company is unhappy with their manager and why.” Slack would then as- sess all conversations to look for patterns of negativity, dissatisfaction, and mood by as- sessing conversations rather than simply look- ing for specific terms.
It could also be used to predict the future actions of employees by accessing application-wide patterns in all companies that use the application. The most useful function was to assess the conversations of employees who left their positions and when. Using this data, any manager could ask the question, “which employees will leave the company within the next 3 months,” to find future holes within the organization. The information could then be used to help prevent the exit, or speed it up to avoid complications in the future.