AI reduces my mental load. That’s all. And that’s enough.

I firmly believe that agile work is the best approach when it comes to delivering good software quickly. This doesn’t depend on a specific method, but rather on keeping the Work-Feedback Loop closed and short. That’s why this is my main focus right now.

And I’m annoyed by two things.

On one hand, I hear at conferences, in podcasts, and in my feed that AI is changing everything. That agile work will become obsolete because of it. That teams need to reinvent themselves because a prompt will now handle everything. On the other hand, I hear that AI is the devil’s work, that you should just stay away from it, that it’s all wildly overrated.

In my experience, AI is a tool like Excel (okay, it’s significantly more powerful than Excel, but still just a tool). Like other tools, this one can be used effectively to support agile work. This article presents my pragmatic view of AI.

I’ve been using AI since ChatGPT was released in December 2022 - first out of curiosity, then systematically, then with a sense of disillusionment, and finally pragmatically. I’ve tried a lot of things, wasted a lot of time, and found a few things that really work. That’s what this is about.

My Context

I’m the team lead of a Kanban team with seven people. We work on projects for different, but always the same, clients. I’m not a developer. My work happens in two directions: internally - supporting the team, designing processes, writing user stories, assisting with concept development, etc.; and externally - planning projects with clients, developing strategies with clients, and organizing regular meetings and other coordination sessions.

My daily toolset: Jira, Confluence, Slack, email, calendar, Joplin for notes.

The tool I use for AI is called Claude Cowork. The key reason: It integrates directly with Jira, Confluence, Slack, email, calendar, and Joplin. That makes a difference, which I’ll explain in a moment.

A word on data before anyone asks: I’ve granted AI read-only access to my email and calendar. That’s a decision I made consciously, and one that everyone must make for themselves. I’m not offering advice on this, because it depends on your own situation, your employer, and your personal risk assessment. I’m just reporting what I do.

What AI can really do - and what it can’t

Before I get into specific use cases, here are a few fundamental observations from three years of practical experience:

AI amplifies. If something already works well without AI, AI can make it better. If something is bad, AI makes it worse. That sounds trivial, but it isn’t if you take an honest look.

AI produces a lot. The trap is that you feel productive without actually being so. I’ve experienced this myself. You let AI generate something, it looks good, you feel efficient. But did it really save time? You have to ask yourself this question, again and again, with uncomfortable honesty.

AI can’t think. Anything that requires experience, judgment, or creativity is beyond AI. AI can’t write a user story that’s truly good. Understanding the requirements, identifying the gaps, asking the right questions - that’s human work. What AI is good at: reading large amounts of text quickly, building summaries, finding connections between data points that you yourself would have overlooked if you were in a hurry.

AI makes mistakes. Still. And that won’t change completely. “Human in the loop” isn’t an option; it’s a must.

Use Case 1: Preparing for a Regular Meeting

This is my best example because it most clearly illustrates the difference between “AI as a chat tool” and “AI with access to data.”

I have a so-called “skill” in Claude Cowork, which is basically an instruction file that describes what the AI should do. This skill does the following: It checks the calendar to see when the last regular meeting with the respective client took place. It reads the minutes and notes from the last meeting from Joplin - or, for some clients, from Confluence. It searches Jira for everything that has changed since the last meeting: new stories, status updates, blockers. It reads the relevant emails from the interim period.

From all of this, it generates a structured template: What should I address in the next regular meeting? Which points remain open? What has changed?

What stands out here: The AI finds connections that I, as a human, would have overlooked if I were in a hurry. An email in which a client mentioned an expectation that isn’t included in any story. A status in Jira that doesn’t match the current status in the last note. These cross-references between multiple data sources are the real added value, not the generated text.

Of course, I edit the generated note because AI makes mistakes. And because I want to have the final say. But it’s a good start, and the time savings are real.

Use Case 2: Daily Prep

I’m a participant in the daily stand-up, not a facilitator, but I contribute my perspective as a team lead. I have a dedicated skill for this.

This skill looks at the latest daily log from Joplin and then scans Jira, Slack, and email since the last daily. It generates a new report: What should I address today?

That sounds simple, but it’s actually quite useful in practice. If there are status updates in emails or Slack that haven’t been updated in the corresponding stories, the skill picks up on that. It identifies stories that have been stuck in a status for too long. It sees when someone has sent in a sick note and links that to the story assignments, with a note that the team should check if someone else can take over.

A human can also spot these connections. I review them myself as well, because AI makes mistakes. But the AI finds them reliably and quickly, even when I’m under time pressure. That’s the point.

Use Case 3: General Meeting Preparation

For other meetings, such as project meetings, refinements, or more unstructured coordination sessions, there’s a similar but more flexible skill. It also pulls information from all connected sources and generates a summary of what might be relevant.

The results here are slightly worse than with the Jour-Fix skill. The reason is simple: the more structured a meeting is, the better AI can prepare for it. With the Jour-Fix, the skill knows exactly what to look for. In a more open-ended meeting, the search space is larger, and the AI is less certain about what’s truly important.

Still, this skill is a good starting point. I have to edit the generated note more thoroughly, but I’m not starting from scratch, and that saves time.

I have other smaller use cases, such as summarizing information and analyzing documents, but I think everyone does that by now, so I haven’t focused on them in this article.

What all this means

AI is no substitute for agile work. It doesn’t make teams faster, processes leaner, or feedback loops shorter. People have to do that. With experience, discipline, and the will to truly improve things.

What AI does: It relieves me of the cognitive overhead of laboriously gathering information from various sources. The result is that I can devote my energy to what it’s really needed for - my team, my clients, and the substantive (intellectual) work.

Reducing mental load isn’t a glamorous goal. It’s not “AI changes everything.” But it’s real, measurable, and noticeable every day.

That’s enough for me.