The AI FOMO Problem
And How to Actually Solve It
I’m feeling so much FOMO right now with how much is going on in AI. It’s so hard to keep up.
On top of that, I’m on parental leave for a couple more weeks, so I feel even further removed from everything that’s happening. Every time I check in, there’s a new model. A new capability. A new company doing something that feels like it should matter but I can’t tell if it actually does.
The short cycle of AI that we’re seeing is crazy.
I can remember a time when the iPhone release was a big enough deal that people took off work to stand in line. We’d wait a year, sometimes longer, for the next version. The pace of innovation felt fast, but you could keep up.
We aren’t quite to that level with AI, but the amount of “new” that happens now happens in a matter of weeks or days, instead of years. Models get updated. Features get added. Entire companies emerge and disappear before you’ve even figured out what they do.
Here’s what I think this creates for HR. Working with AI to build new processes at work and keeping up with all the innovation is almost two full-time jobs. And most of us don’t have one full-time job to dedicate to this, let alone two.
I’ve heard from a few people in HR around this, and the approaches basically fall into two camps.
Either their whole team is trying to keep up with the changes. Everyone’s reading newsletters. Watching demos. Testing tools. Attending webinars. And nobody’s actually implementing anything because they’re too busy staying current.
Or they’re relying on an external training company to come in and tell them what they need to know. Which feels efficient until you realize the training was built three months ago and half of it is already outdated. Plus, it’s generic. It doesn’t account for your specific team, your specific challenges, your specific workflows.
Both approaches miss the point. You can’t keep up with everything. And you don’t need to.
If you have a team of HR professionals, you should be working with them to leverage their individual skills to get over this hurdle. Not everyone needs to be good at everything. In fact, trying to make everyone good at everything is exactly why teams get stuck.
Do you have anyone that loves building new things for the team? The person who gets excited about automating processes or creating templates or figuring out how to make something work better?
Do you have anyone that can synthesize huge amounts of information and distill it down? The person who reads constantly, connects dots across different sources, and can explain complex things in ways that make sense?
Do you have anyone who is a highly skilled communicator? The person who knows how to bring the team along, who can translate technical concepts into practical applications, who can get buy-in without making it feel forced?
You should be asking yourself these questions and thinking about who on your team will fit this. Not who you wish would fit it. Who actually does.
For the builder, have them start to work on bringing AI into the daily workflow across the team. Not theory. Not possibilities. Actual implementation. They’re the ones testing tools, building prompts, creating systems that the rest of the team can use.
They don’t need to understand every technical detail. They need to understand what works and what doesn’t in your environment. They’re figuring out how to use AI to write better job descriptions, analyze survey data, draft policy updates, whatever makes sense for your team.
For the thinker, have them keep up with trends and bring new ideas on how to leverage new tech. They’re your filter. They’re reading the newsletters, watching the demos, and deciding what’s noise and what’s signal.
They’re not bringing you every new thing that comes out. They’re bringing you the three things this month that might actually matter for what you’re trying to do. They’re translating “here’s what’s happening in AI” into “here’s what this could mean for us.”
For the communicator, have them build the plan of how you bring this to the rest of the team. Because you can have the best tools and the best insights, but if nobody knows how to use them or why they should care, nothing changes.
They’re creating the training. The documentation. The “here’s how this makes your job easier” messaging. They’re addressing the concerns, answering the questions, and making sure adoption actually happens.
From my perspective, this approach solves the two problems most teams have with AI right now.
First, it makes the scope manageable. Nobody’s trying to do everything. The builder isn’t responsible for staying current on every trend. The thinker isn’t responsible for implementation. The communicator isn’t responsible for technical decisions. Everyone has a lane.
Second, it plays to strengths instead of forcing weaknesses. Your builder gets to build. Your thinker gets to think. Your communicator gets to communicate. They’re doing what they’re already good at, just applied to AI.
And here’s what happens. Your team actually moves forward instead of just staying busy. You implement things. You learn what works. You adapt. You get better.
This sounds clean when I write it out. In reality, I know it’s messier.
Not every team has all three of these people. Sometimes you have two builders and no thinker. Sometimes your best communicator is also your best builder and they’re stretched too thin.
There’s also the question of how much time to dedicate to this. Is it 10% of someone’s role? 50%? Do you create a formal responsibility or keep it informal? I don’t have a perfect answer.
And then there’s the reality that pulling in AI is going to be hard at first because we are all learning. Even if you divide responsibilities perfectly, everyone’s still figuring it out. There will be false starts. Tools that don’t work. Time spent on things that don’t pan out.
If you can find the right people to focus in the right areas, you’ll be in a much better place than trying to make everyone an AI expert.
Plus, you’ll be leading your team based on their strengths. Which is what good leadership has always been about, whether AI is involved or not.
The FOMO of AI isn’t going away. The pace isn’t slowing down (at least not fully). But you don’t need to keep up with everything. You just need to keep up with what matters for your team.
Who on your team is already showing interest or capability in one of these areas? And what would it look like to actually let them run with it?
Interesting observation about the shortcomings of training — curriculum gets outdated within months, and it's not designed for the specific challenges of the organization.