I Am No Longer Needed

How I Automated My Own Role as AI Lead — and What That Actually Feels Like

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This past week, I realized something uncomfortable: I had automated myself out of a significant portion of my job as a project manager. Not theoretically. Not as an exercise. I had actually done it — piece by piece, meeting by meeting, analysis by analysis — until I looked at what was left of my daily work and thought, this doesn't need me anymore.

I'm an AI lead at a mid-sized company in Germany. My job is to evaluate, implement, and manage AI solutions across the business. The irony is not lost on me that the tools I was hired to champion are the same tools that are now doing chunks of my job better than I was.

Let me be honest: AI has not done everything perfectly. There have been failures and outputs that needed heavy editing. But those failures are small compared to the sheer volume and quality of what it has delivered. The balance sheet tips overwhelmingly toward the machine.

Here's how it happened.

The Foundations: Automating the Boring Work First

It started with meetings. Every knowledge worker's life is dominated by meetings, and every meeting generates the same overhead: notes, action items, follow-ups, preparation for the next one.

I built an AI agent workflow that does the following:

1. Meeting transcription and notes. Our company uses Microsoft Teams, which provides automatic transcriptions — but they're often mediocre, especially when people switch between German and English mid-sentence (which happens constantly in a German company with an international team). So I set up my agent to take the original video recording and produce its own transcription, which is consistently better. From that transcription, it generates structured notes and actionable items.

2. Meeting preparation. Before meetings, I have the agent review all prior context — previous meeting notes, related documentation, open tasks — and generate a preparation brief. This includes:

• What should be discussed
• What goals we need to accomplish
• What information we still need to obtain

This alone saved me hours of pre-meeting research and preparation.

3. Weekly status reports. I connected the agent to Confluence (our internal documentation), Asana (our task management tool), and my calendar. By giving it a screenshot of my calendar for the previous week, it cross-references everything and produces:

• A summary of what I accomplished
• What's still in progress
• What's blocking me

It aggregates all of this into a structured report, then provides an analysis that I use to prepare for my weekly team check-in. What used to be a tedious and often frantic chore before a meeting — piecing together what I did all week from scattered notes and memory — now takes minutes.

The Case Study: Automating an Entire Project Workflow

The real revelation came with a specific project: finding a knowledge base solution for the company.

Three different departments needed this: IT service desk, customer care, and product management. Each had different requirements, different existing tools, and different stakeholders with varying opinions. The kind of project that typically takes months of meetings, exploration, and political navigation.

Here's what the traditional process looks like:

1. Initial request from stakeholders
2. Clarifying meetings to understand the real need
3. Requirements definition
4. Exploration of existing internal solutions
5. Research into external solutions
6. Evaluation and comparison
7. Final proposal

I automated nearly all of it.

The agent processed meeting transcriptions from stakeholder conversations. It digested documentation about our existing tools — Salesforce (our CRM) and Jira Service Management (underutilized in our IT department). It analyzed requirements from different departments, identified overlaps and conflicts, and helped synthesize a proposal.

What we essentially did was take all the knowledge that resided in my head and in the heads of experts across the company, and digitized it — either through written documentation or through the meetings themselves. The agent processed all of it into a form it could reason about, compare, and draw conclusions from.

The result wasn't perfect. It still needed my judgment, my understanding of internal company dynamics, additional non-documented constraints, and a sense of which solutions could actually get adopted. But it got us 80% of the way there in a fraction of the time.

The Vision: What Comes Next

Here's where it gets interesting — and a little unsettling.

I can see the next step clearly, even though we haven't implemented it yet: automating the stakeholder engagement itself. Instead of scheduling a dozen meetings to clarify requirements, imagine a chat interface where the agent engages stakeholders directly. It asks the right questions, clarifies ambiguities, captures requirements in a structured format, and then:

1. Evaluates what solutions are already available internally
2. Explores additional options from the market
3. Matches requirements against available technology
4. Produces a proposal for all stakeholders

A process that currently takes weeks of calendar coordination and meeting fatigue could happen asynchronously, thoroughly, and without anyone having to find a free slot on everyone's calendar.

This is the point where "I am no longer needed" stops being a joke.

How to Build This Yourself: A Practical Guide

If you want to start automating your own workflows, here's the approach that worked for me:

Step 1: Start with meeting overhead

This is the lowest-hanging fruit and the highest-ROI automation for most knowledge workers. Get an AI agent (Claude Code, a GPT-based workflow, or similar) that can:

• Transcribe meetings (especially if your corporate tool does a poor job)
• Extract action items and key decisions
• Generate structured notes

Step 2: Connect your data silos

The real power comes from connecting the agent to the tools where your work actually lives: task management, documentation platforms, calendars, email. The agent needs context to be useful. Without it, it's just another chatbot.

Step 3: Automate preparation, not just documentation

Most people stop at "AI takes my meeting notes." The bigger win is having AI prepare you for meetings. Feed it all prior context and let it tell you what needs to happen next.

Step 4: Build weekly synthesis workflows

Connect your calendar, task manager, and documentation tool. Let the agent create a weekly summary that you review and refine. This forces you to keep your tools updated (because the agent's output is only as good as the data it can access) and gives you a clear picture of where your time actually goes.

Step 5: Tackle a real project end-to-end

Pick a project with clear inputs (stakeholder requirements, existing tools, market options) and a clear output (a recommendation or proposal). Feed the agent everything, and see how close it gets. You'll be surprised.

The Part Nobody Talks About: Burnout

Here's the dark side that doesn't make it into the productivity threads.

The ability to multitask with agents — having multiple AI workflows running simultaneously, each handling a different project — has made me unbelievably productive. I can now accomplish things across administration, project management, and software development that I never imagined possible. For someone with my personality and working style, this is intoxicating.

So I keep pushing. Testing the limits. Seeing what else can be done. And because I have the kind of personality that doesn't stop when it should, this has taken a toll — both mentally and physically.

It's not just the hours of staying engaged with multiple agents across different tasks. It's the cognitive weight of the realization itself. You're running into the unknown, building capabilities you don't fully understand the implications of, and the uncertainty of where this all leads creates a specific kind of anxiety that's hard to explain to people who aren't living it.

There's a quote I keep coming back to: "If you don't feel a bit of vertigo, you're not going far enough." I feel the vertigo. Some days it's exhilarating. Some days it's just vertigo.

What "No Longer Needed" Actually Means

Of course, "I am no longer needed" is an exaggeration. The AI can't navigate office politics. It doesn't know which stakeholder to engage. It can't read a room or build the trust that makes people actually adopt new tools instead of ignoring them.

What's changed is where I'm needed. The mundane work — the transcriptions, the summaries, the research, the first drafts, the comparison matrices — that's handled. What remains is strategy, judgment, taste, and relationships. The things that are hardest to automate and hardest to define.

I feel the weight and the power of these AI agents hanging over my shoulders. It is both liberating and suffocating at the same time. Liberating because I can finally operate at the level I always wanted to — thinking about what matters rather than drowning in administrative overhead. Suffocating because the ground keeps shifting under my feet, and I can see it shifting under everyone else's too.

The honest truth is that automation freed me up to work at a higher level of operation and management. But it also removed the comfortable scaffolding of busy work that used to structure my days. When the busy work disappears, you're left with the harder questions: What should we actually be doing? What matters? What's the right call when there are no clear answers?

Those are the questions that still need a human. For now.