The Real Blocker
The blocker to AI adoption isn't the technology. It's overwhelm.
I have watched designers on the same team have completely different experiences with AI. Some were building custom agents and quietly reinventing their workflows. Others did not know where to start. Neither group was wrong. The pace is relentless. New models, new tools, new ways of working, every week.
Nobody can keep up alone. What looks like a skill gap on the surface is usually something quieter underneath. Overwhelm, and under the overwhelm, fear. Fear of looking slow. Fear of being the one who has not tried the newest thing. Fear that admitting a gap will cost you standing on the team.
Nobody can keep up alone.
When Expertise Becomes Status
AI fluency is quietly turning into a status game. That is the real risk.
Across our industry, knowing the tools is turning into a competition. When expertise becomes a competition, people stop admitting what they do not know. The overwhelmed go quiet. The confident get louder. The gap widens. What you end up with is not a team. It is a group of individuals protecting their edge.
That is not a training problem. It is a leadership problem. Culture is designed, and if you do not design it, status games design it for you. The moment I noticed the pattern on my own team, I stopped optimizing for how advanced the top of the team was, and started optimizing for how safe it felt to be in the middle.
Culture is designed. If you don't design it, status games design it for you.
The Principle
Value experts for how many people they bring along, not how advanced they are.
This is the single sentence I keep coming back to with my team. It reframes the incentive. The most advanced person is not the most valuable one. The most valuable one is whoever has just brought two more people up to their level. That principle shows up in performance conversations, in who gets recognized in reviews, and in how we assign new tools.
Once teaching becomes the thing that earns respect, the tone of the team changes. People start asking basic questions in public channels. Experts start writing short internal Looms instead of hoarding hacks. The overwhelmed stop hiding, because they can see what the reward system is actually rewarding.
The Hackathon
We built the environment around that principle. Two days, in person.
The principle only matters if the environment enforces it. So we ran a two-day, in-person hackathon designed around a handful of deliberate choices. Every choice was aimed at the same thing: making it safe to be a beginner.
- Small groups, in person. Psychological safety does not scale to a 60-person webinar.
- Volunteer experts who committed to handholding, starting with environment setup. That first hour is where most people quietly give up.
- A live project instead of a tutorial. Day one was a hands-on workshop. Day two, everyone built and shipped actual things.
- Designers and engineers side by side. Not two tracks. One room, mixed pairs, shared context.
- Explicit respect for different paces. Not everyone moves at the same speed, and that is not resistance. That is how teams work.
What The Learning Actually Looked Like
Designers picked up the tools faster. Engineers rebuilt how they exposed the system.
The most interesting outcome was not the prototypes. It was the direction the learning ran. Designers, freed from the fear of looking slow, picked up the AI tools faster than anyone expected. Engineers, watching designers try to prototype against production surfaces, discovered new ways to expose our Storybook components so designers could build production-quality coded prototypes instead of static mockups.
That is the point most training programs miss. Confidence comes from shipping with a tool, not watching a demo of it. And when designers and engineers ship together, the tooling itself gets better, because the system starts to shape itself around actual use.
How We Kept It Going
The hackathon was the ignition. The rituals kept the fire on.
A hackathon is a moment. Culture is a habit. We protected the momentum with dedicated time to experiment, weekly shared demos where the point was to show unfinished work, and team dinners that were not about the tools. We measured the impact deliberately. Confidence with AI tools and belief in their value both rose significantly, on the same team, in the same quarter.
The numbers matter, but the tell was softer. Designers who had been quiet in AI conversations started running them. The gap between the top and the middle of the team stopped widening.
The Leadership Move
Give your team permission to be beginners.
This is the piece leaders can control. You do not need a bigger budget. You need to make it obvious that beginner status is safe. Say it out loud in team meetings. Reward the person who taught someone else this week, not the person who used the newest model. Kill the private DM culture around AI wins by asking experts to share in the open.
The teams that thrive with AI will not be the ones with a handful of guarded experts. They will be the ones where teaching is the thing that earns respect. Confidence is built, not mandated. And a team where confidence is built is a team that keeps compounding, long after the current tool cycle is old news.
Give your team permission to be beginners.
Read Next
Two companion pieces if you want to go deeper.
- How To Run a Design Hackathon That Actually Ships/writing/design-hackathon-playbook
- How To Restructure a Design Team for the AI Era/writing/restructure-design-team-for-ai