The Pressure to Move Fast
There’s a particular kind of pressure that comes with being an SME leader right now. AI is everywhere. Your competitors are “adopting it.” People around you are talking about it. And somewhere in the back of your mind, there’s a nagging feeling that you should be doing something with it, even if you’re not entirely sure what.
So you start exploring. You try a tool… then another… You watch a webinar… You read an article about automation… You wonder if you should be using it for customer service… or operations… or forecasting… or all three… And somewhere in that process, the exploration stops feeling like learning and starts feeling like drowning.
This is overwhelm. And it’s not because you’re moving too slowly. It’s because you’re moving without clarity.
The Real Problem Isn’t the Tools
Here’s what I’ve noticed: when people feel overwhelmed by AI, they usually think the problem is that they haven’t found the right tool yet. So they keep searching. They try ChatGPT, then Claude, then some specialised platform. They read more articles. They attend more webinars. They’re looking for the thing that will suddenly make it all click.
But that’s not where the problem is.
The problem is that they haven’t defined what they’re actually trying to solve. Not in theory, but in practice. They’re reacting to pressure instead of responding to a real need. And until that changes, no tool is going to help.
I experienced this myself recently. I wanted to stay current with industry developments—new tools, emerging practices, shifts in how people are thinking about process improvement and automation. Sounds straightforward, right? But the moment I tried to turn that into an AI solution, it got complicated fast. Which developments? Which industries? How do I get fresh information instead of relying on training data? How do I synthesise it all? What format do I actually want the output in?
I was drowning in possibilities before I’d even started.
The Way Forward: Five Things to Consider
What I realised is that the answer wasn’t more AI. It was going backwards. Back to pen and paper. Back to first principles. And then, only then, taking small, deliberate steps forward.
Here’s the framework I’ve found useful:
1. Consider Taking a Break
The pressure to adopt, to move fast, to stay ahead—it creates reactive decision-making. You’re not thinking clearly because you’re anxious. So the first thing to consider is stepping away from the noise for a bit. Not forever. Just long enough to reset.
Put down the tools. Stop reading the articles. Let the pressure settle. This isn’t procrastination. It’s clarity work.
2. Consider Focusing on First Principles
Once you’ve stepped back, ask yourself a simple question: what problem am I actually trying to solve?
Not “how do I use AI?” That’s the wrong question. The right question is more specific. For me, it wasn’t “how do I use AI to stay current?” It was “how do I stay informed about industry developments without drowning in information?” That’s a very different problem.
Get brutally specific about what you want the outcome to be. What does success look like? What’s the constraint you’re working within? What’s actually broken right now?
Write it down. On paper. This matters.
3. Consider Starting Manual and Iterative
Once you know what you’re solving for, don’t jump to automation. Do it by hand first.
This sounds counterintuitive when we’re talking about AI. But it’s the most important step. Start small. Take deliberate steps. Experiment. See what works and what doesn’t.
In my case, that meant subscribing to a few newsletters, setting aside time to read them, and manually collecting the articles that felt relevant. It’s not elegant. It’s not automated. But it’s deliberate, and it’s teaching me something.
4. Consider Experiencing the Friction
As you work through the manual process, pay attention to what’s awkward. What’s repetitive? What’s time-consuming? What feels like it shouldn’t have to be this way?
That friction is data. It’s telling you something important about what the system actually needs to be.
When I started manually collecting articles, I noticed patterns. Certain types of content were more useful than others. Some sources were noise. The manual process revealed what actually mattered, and what I could ignore. That’s invaluable information. I wouldn’t have seen it if I’d jumped straight to an automated solution.
5. Consider Then Deciding What’s Worth Automating
Only after you’ve lived with the manual process for a bit, only after you understand the problem deeply, consider whether AI or automation actually helps.
You might decide the manual approach is fine. You might decide it’s worth your time. Or you might see a clear opportunity where automation would genuinely improve things. But you’ll be making that decision from a place of understanding, not from marketing promises or FOMO.
The Point
The path forward with AI isn’t about finding the right tool. It’s about thinking clearly first. It’s about getting specific about what you’re solving for. It’s about being willing to do things the slow way, at least at first, so you understand what you’re actually optimising for.
Overwhelm comes from trying to solve everything at once. The antidote is clarity, small steps, and the willingness to experience a bit of friction before you automate it away.
Consider starting there