It started with a strange disconnect.
Everywhere I looked—LinkedIn, podcasts, Slack threads—people were talking about AI. Loudly. Constantly. But in real terms? I saw very few people using it in any meaningful way. Maybe they logged into ChatGPT to punch up some marketing copy. That was about it.
I was hitting networking events with local execs and marketers. The tone was familiar: vague concern, low energy, and almost no hands-on experience.
One moment stands out in my memory of observing this disconnect. A work friend had just returned from SXSW, having gone to every AI session they could find. But when I asked if they’d used a Custom GPT, they blinked. Hadn’t even heard of it.
Everyone sounded exhausted by AI — while barely touching it.
4 Traps That Keep Teams from Getting Started
Looking back, I think there were four factors contributing to this gap between AI awareness and AI adoption. I saw these same dynamics play out in my team’s behavior as well as my own. Here’s what I observed:
1. Treating AI Like It Can Wait
This was our first mistake. We knew AI was important, but it never felt urgent. We weren’t sure what it was good for. Plus, as a people-leader, I didn’t want to foist confusing tools on a team already stretched thin.
2. Trying AI Once, Then Stalling Out
The next trap: doing one shiny thing and calling it a day. Someone shows you how to brainstorm blog titles or write a better LinkedIn headline, and that becomes your one AI trick.
But that’s not adoption.
3. Getting Derailed by Tool Hype
This one is probably the single greatest contributor to AI inaction. AI-related content (I’m looking at you, LinkedIn) is a firehose, and most of it’s driven by incentives to sell from SaaS products with AI features. If you’re perusing the AI tool marketplace, it’s easy to feel like you need a 12-tool stack just to keep up.
But if you limit yourself to just one paid LLM account, you can get all the mileage you need for your first 6+ months of AI adoption.
4. Fear of Change, Masquerading
There’s a more subtle blocker I’ve noticed — one that’s harder to call out because it often sounds principled. It’s the person who says:
“I’m just not comfortable with AI because of the ethical issues.”
“It’s trained on stolen data.”
“I don’t want to support a tool that might displace people’s professions.”
These are valid concerns. I share many of them. AI’s business models, energy consumption, impact on workers, and IP theft should be scrutinized.
But here’s what I’ve learned: sometimes those concerns are real, and sometimes they’re a mask for something else — fear of change. Dealing with the negative externalities of AI is something we need to face as a society. But as practitioners, we should reflect on how to separate these concerns from an honest appraisal of our own change aversion.
The Approach That Actually Worked
At work, my team and my own AI use went from a culture reflecting all of these blockers to one that enthusiastically gained curiosity as a group. AI adoption went from a nagging anxiety to the professional development highlight of my team’s year. What moved the on my team needle wasn’t flashy. It was a few grounded practices, repeated.
Weekly Time, with Permission to Play
I block an hour every Thursday morning for AI content consumption and experimentation. I encourage my team to do the same. It’s when I’m alert but open to distraction. I might watch a video from Marketing Against the Grain, test a new GPT, or tweak a prompt I saw on LinkedIn.
Some of it’s great. Some of it’s noise. But every week, I bring back something—an idea, a question, a clearer sense of what these tools are actually good for.
Testing on Practical Applications—In and Out of Work
Rather than trying to create new capabilities on the team, we focused our experimentation on using AI to alleviate annoying tasks at work and at home.
One teammate built a Custom GPT to turn messy client interviews into clean case study drafts. It now saves us four hours per piece. Another teammate started her AI journey with a packing list for a family trip. That grew into a work event-planning Chat GPT process. Now she reaches for AI instinctively.
What I want to encourage is the development of the instinct around when AI might be useful… and also when it’s totally unhelpful!
Making It Social
Once every two weeks, my team gets together and we create space for one person to demonstrate a new use of AI or a new tool they’re getting value from. Our show-and-tells are casual. Someone tried a new workflow that saved them time and uncovered new insights. Someone else hit a wall after getting excited about the value of Deep Research. We talk about both.
Our most junior teammate turned out to be our boldest experimenter. She had no old habits to unlearn, and no fear of the unknown. These are great assets in building AI curiosity.
The Real Shift: A Culture of Curiosity
The change isn’t just in what we use. It’s how we think. There’s pride in a clever prompt. There’s confidence in asking, “Could AI help with this?” And, there’s a growing sense that we’re not just watching the wave—we’re surfing it.
That’s because using AI well takes skill: framing, judgment, creativity. The tools don’t replace those things. They reward them.
The shift I’m proudest of? We treat AI like a puzzle. How do we cut the grunt work without lowering quality? Where can we trust it? Where should we double-check?How do we stay human in the loop?
There are no final answers, just better questions. And slowly: better, more satisfying and interesting work.
One Step to Start
If AI feels overwhelming, don’t start by researching the best new tools. Start with your calendar. Block one hour. Pick one tool. Use it on something you already need to do. See what happens.
If it works, share it. If it flops, share that too. AI doesn’t need to be a strategy. It can be a habit.