What am I actually supposed to be getting better at?
There is a strange problem that I keep running into with AI.
I use it regularly and my work is primarily focused on AI adoption at scale right now. I try and experiment with new tools, new models and new ways of working as much as I can. I’m probably more involved with AI than the average person.
And yet, I still sometimes find myself wondering, what exactly am I supposed to be getting better at?
If I follow all the different advice it takes me all over the place. Learn this new tool, build this new agent, try this new model… just automate something and experiment more so you become AI-native and rethink your career.
My head starts to explode when I think of all of the different directions and I have realized that anyone can spend an impressive amount of time learning about how AI can save time without actually saving any time.
I’m guessing I’m not alone with this problem. The more I’ve worked through this myself, the more I’ve realized that “use AI more” isn’t actually a useful goal at all. Neither is “get better at AI” or “just experiment more with AI”.
All of these are too broad to tell me exactly what to do differently when I sit down at my desk Monday morning.
So instead, I’ve started approaching it differently and instead of beginning with AI, I begin with the work I need to do.
Where am I spending too much time on something and what is frustrating me right now? What am I doing on repeat simply because it’s the way I’ve always done it? What am I producing that takes me three hours to do when the actual value I bring happens in the 20 minutes before or after I produce it? Where am I doing work that probably shouldn’t exist in its current form at all?
Then I start to think through how could I do it differently and I experiment. Not with AI in general but with that specific piece of work and this is where I think we sometimes get AI adoption wrong. Experimentation matters, it’s important to explore what’s possible, but experimentation without direction can become it’s own form of overwhelm and anxiety. You end up with a collection on interesting tricks or things you can “show” someone, instead of a fundamentally different way of working.
I’ve found that the experiments that teach me the most aren’t necessarily the flashy ones, they’re the ones where I take something that I actually do and figure out how do I question the entire workflow.
Do I need to do this in the first place?
Does it need to happen this way with these steps?
What part of this could AI do if I could figure out how to get it to do it?
What part still absolutely needs me involved?
And if I changed it, what would I do with the time or capacity that I got freed up?
That last question actually matters way more than I used to think.
Saving 30 minutes insn’t particularly transformative if I immediately fill those 30 minutes with more meetings or minutes of low value work.
The bigger opportunity is learning how to redesign the way we work because AI exists in the first place and that’s different from just learning AI.
This is the skill that many of us are trying to develop right now that matters for our future. Now how to become an AI expert but how to look at work we’ve done for years with fresh eyes and be willing to ask the hard question, “would I still do it this way if I was redesigning this today?”
Your Anchor for this week
Pick one thing you do regularly that creates friction in your life. Don’t start by asking how AI can help. Start with the work itself:
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Notice the friction. Ask yourself why are you spending more time, energy or attention than the outcomes seems to justify?
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Question the workflow. If you were designing this work today, right now, would you do it the same way?
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Run one small experiment. Use AI to change just one part of the workflow in your actual work, not just as a demo or as an example.
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Notice what changed. Did anything get faster or better? What didn’t change and what is still required of you?
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Redesign again. Keep the part that worked and change what still isn’t working or is causing friction. You might find that as you alleviate friction in one place, a new friction pops up.
You don’t need 47 new AI use cases under your belt, you just need one real piece of work that is done differently. That will teach you more than another list of prompts or playbooks that you look at once and file away never to be looked at again.
🩵What I’m loving
With all the talk and focus on AI, I’m loving the human moment this week after listening to episode 438 of the Mel Robbins Podcast with behavior researcher Vanessa Van Edwards. They talk about how nonverbal signals can affect how confident you appear even before we’ve really started speaking: posture, what we do with our hands, and even where we position ourselves in a room. Having something valuable to say and making sure people can actually see and hear that value are two different skills. It’s worth a listen if your thinking about your own presence and influence.
The future of work isn’t asking us to throw away everything we know. It’s asking us to get much more intentional about what we carry forward, what we change and what we can finally stop doing.
That’s a very different challenge than simply learning another tool.
Remember, you’re still the author of what comes next.
Warmly,
Heather
Anchored in Possibility™
The future belongs to those who know who they are when everything changes.
