Automating repetitive tasks with AI.

I used to think my day was defined by the big projects, the creative breakthroughs. But honestly? For years, the bulk of my time was eaten alive by the small stuff. Sorting emails, generating boilerplate reports, copying data from one app to another. It was a digital death by a thousand cuts, and it was draining my will to tackle the work that actually mattered. My first reaction to the idea of AI automation was skepticism. It sounded complicated, expensive, and something for massive corporations, not for someone like me trying to manage their own workflow.

My first steps into AI automation

The turning point came when I decided to start ridiculously small. I didn't try to build a complex, multi-stage system. Instead, I picked one single, mind-numbing task: categorizing incoming client requests in my inbox. It was a simple rule-based task that I spent at least 30 minutes on every single morning. I used a simple AI-powered email assistant to learn the patterns of my manual sorting. It took an afternoon to set up and test, and I made a few mistakes along the way. But the next morning, when I opened my inbox and saw the work already done... it was a revelation. That small victory gave me the confidence to look for the next domino to fall.

Choosing the right task to automate

After that initial success, I got a bit overzealous. I tried to automate a complex content briefing process and failed spectacularly. The task had too many variables and required nuanced human judgment that I couldn't yet replicate. From that failure, I developed a simple framework for what to automate next. I ask myself two questions:

• Is it frequent? Does this task happen daily or weekly?
• Is it low-creativity? Is it a rules-based process that doesn't require deep, original thought?

If the answer to both is a resounding 'yes', it becomes a prime candidate for automation. This simple filter has saved me countless hours of frustration and helped me focus on automations that deliver real value.

The tools that actually made a difference

I didn't need a degree in computer science to get started. My toolkit is surprisingly simple. I lean heavily on no-code workflow builders that connect the apps I already use, like my project manager and communication platform. Think of them as digital glue. When an event happens in App A, it automatically triggers a pre-defined action in App B. Another game-changer for me has been AI-powered text expansion. I have shortcodes that can instantly generate entire email templates, status updates, or common code snippets, saving me from typing the same things over and over again.

Beyond the hype: what I learned the hard way

The biggest benefit wasn't just the time I got back. It was the mental energy. By offloading the monotonous cognitive load, I found I had more bandwidth for strategic thinking and creative problem-solving. It's like my brain was finally freed from the digital hamster wheel. I also learned that automation isn't a 'set it and forget it' solution. I schedule a brief review every quarter to make sure my automations are still relevant and efficient. Workflows evolve, and so should your systems. Taking that first small step was daunting, but it fundamentally changed how I approach my work, proving that AI automation isn't just for the big tech giants—it's for anyone who wants to reclaim their focus from the mundane.