The Future | Machine Learning

  • Updated

As a preface, these features are in BETA. Some small features are being implemented now into the platform, but full release of these features is slated for this Summer!

Machine Learning 🤖 lends very advanced insights into how to be more productive. Here are a few places that ClickUp will be automating your workflow and implementing ML:

1. Time Tracking/Time Estimates

When you are active in a task or change to action statuses, time will be automatically recorded.
This will also headway for proper time estimations when compared to how long was originally estimated ✔️

2. Task Assignees

Allow the way you work, to be understood. Typically assign blog tasks to your content marketer, or bugs to your QA developer? Save time in automatically assigning tasks with vocabulary understood by ClickUp ML engines.

3. Automatic Priorities

Allowing cues about what you name a task, add to the description, and what you say when you tag a coworker to automatically assign different levels of priority to a task!

EX. Comment: @Janet Can you take a look at this as soon as you can = Urgent 🚩
Title: Routine Content Audit = Normal 🏳️

4. Progress Percentage

Hand-in-hand 🤝 with Time Estimates, managers will be able to get a high level overview of a task's completion by simply peering at the status.

5. Smart Search

Finding tasks shouldn't be like searching for a needle in a haystack. Speed up the time it takes to find tasks by prioritizing tasks that you have searched for before, have synonymous terms, or you have been involved in. 

6. Automatic Status Cycle

Why should you manually update your statuses if you are already giving contextual cues to move to the next stage? Allow comments to nudge a status into the next stage all the while communicating the progress like you normally would!

EX. Comment: @Josh ready for your final review = Review 🔎

7. Productivity Reporting

With all of these powerful insights gained from Machine Learning, there will be a high level overview of what team and individual statistics need your attention. Some possibilities from ML incorporated into reporting include:

  • Insights into burn down charts

  • Where the most time is being waisted

  • How quickly are urgent tasks resolved

  • How long it takes for a user to respond to tasks they are tagged in

This is just the beginning! If you are reading this, you are probably just as stoked about being as efficient at your job as possible. 

If you have any ideas about where we should look into expanding our machine learning, drop us a suggestion here!

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