Emails - we hate them but cannot live without them. Today, email is the unit of communication between peers, but it is used as a unit of work, e.g. a task. And task needs a lot of parameters to be actionable, assignable, trackable, and traceable. The format of an email and its features are not really suitable for project management. Hence, we ended converting emails to tasks manually.

In this post, we will give you several tips on converting an email into a standard task of a PM tool like Asana or monday.com. We will use A.I. models to process and extract information from emails to provide the context for a created task.

Note: we assume that there is already some preprocessing done for emails. And the email is already classified using some criteria. The tips below are not really valid for cleaning up your mailbox - we will cover that in other posts. 

Option 1: Extract data from attachments

This option is relevant for cases where the key information is transferred via attachment. For example, if you receive a client work order request on a general-purpose address like requests(at)acme.com and work order details are in an attachment, you can create a task extracting information from it.

monday.com:

Asana:

Option 2: Structured notifications

Many emails are notifications from different tools. For instance, when the case is Freshworks CRM is won, you can receive an email notification with case details. In the same way, you can configure some tools to send you notifications about the events. 

Such notifications can be configured and are structured. It means that the email has the same format, only some values change. This allows using A.I. to process the email notification and extract data that can be added to a task.

After you configure where you want the information to appear, you can process such emails to tasks. See below how it works:

Options 3: Use Custom Extraction Feature

The last option would be to use a custom extraction feature in the Bitskout platform. This feature allows you to add several examples of messages and specify what to extract from them. This way A.I. will try to find a pattern in such messages and extract the data that you need:

When you create such model in Bitskout, the key part would be to specify examples. A.I. will use those examples to understand the pattern and find data in incoming similar emails3-. This also means that some preprocessing should be done to categorize the emails. 

Once the examples are given, our A.I. will then extract information from similar message allowing you to convert unstructured text from email into standard tasks. 

Here is the example:

Thus, in conclusion, we demonstrated three ways how you can improve your productivity and project management processes with help of A.I. By automating part of your emails processing can give you a lot of time for meaningful work.