AI & Automation.
Every week, hours are lost. Not because too little work is being done. But because tasks end up in the wrong system.
Email is the most widely used channel for tasks in projects. At the same time, it is the worst system for managing them. Emails are read, mentally noted, and then forgotten. Or manually transferred to the board with information loss along the way. Or they are never found because they are buried in an inbox with 500 other messages.
This costs time. It costs nerves. And it costs money.
The good news: the problem is solvable. Not through a new email client or another meeting. But through an automated workflow that analyzes incoming emails, extracts tasks, and creates them directly in the right system.
No manual effort. No lost tasks. Full transparency across the team.
Those who want to understand how this works technically will find the details in the rest of this article.
The goal is to not just read incoming emails, but to process them systematically.
The workflow handles the following steps: detect new email, analyze content, extract tasks, generate structured tasks, and automatically create them in the system.
The result: emails become a structured source of tasks.
For emails to be processed automatically, they first need to be made technically accessible. This requires a brief consideration, as two well-known protocols come into play: IMAP and POP3.
POP3 downloads emails locally and partially removes them from the server. IMAP, on the other hand, leaves emails on the server and allows them to be read and processed in parallel. For this use case, IMAP is the better choice. Emails remain in the user’s inbox and can simultaneously be processed by the workflow.
In n8n, a corresponding trigger is set up that fires with every new incoming email.
For emails to become real tasks, the extracted information needs to be transferred into a system. In this example, a Kanban board in Jira is used.
The connection is made via the available interfaces and nodes. New issues can be created automatically via API token and user access.
The workflow creates a new task on the board for each identified task, making it visible, plannable, and traceable.
The central component of the workflow is the analysis of email content. An AI agent in n8n is used here, connected to an AI model.
The agent’s job: understand email content, extract relevant information, and generate structured data.
Typical pieces of information include the task title, description, priority, and a possible deadline.
The quality of the results depends significantly on the prompt used. Only when it is clearly defined what a task is and how it should be structured do usable results emerge.
An email often contains not just a single task, but several. To ensure that each of these tasks is captured in the system, the tasks extracted by the AI agent are first made available as a structured list.
In the next step, these individual entries are processed separately within the workflow. This means each identified task is passed on individually and created as its own task on the Kanban board. This way, a single email can generate any number of structured tasks.
The automation creates an end-to-end process: email, analysis, structure, task.
The benefit is immediately visible. Tasks no longer get lost in the inbox. Information is made centrally available. Manual transfer is eliminated. Self-organization is significantly simplified.
The technical implementation of such a workflow is comparatively straightforward today. The real challenge lies not in connecting the systems, but in structuring the content in a meaningful way.
Emails are often unclearly worded, contain no clearly defined tasks, or are missing important information such as responsibilities or deadlines. In a production environment, such cases need to be specifically handled — for example through filters, validation logic, or additional rules for prioritization.
In this use case, that was deliberately left out in order to clearly demonstrate the basic functionality and potential of the solution.
What becomes clear nonetheless is that email remains one of the central entry points for tasks in many companies — while simultaneously being an unsuitable system for managing them in a structured way.
Through the combination of automation and AI, these two worlds can be meaningfully connected. Tasks are created where they are formulated and simultaneously transferred into a system where they can be planned, prioritized, and tracked.
With comparatively little effort, this approach creates transparency, stabilizes processes, and makes daily work measurably more efficient.
Many companies struggle with exactly these problems — often without consciously recognizing them.
Together we can quickly identify where automation and AI can create concrete value.
