Be Curious and Experiment
Teaching AI While Everything Keeps Changing
Since the start of the year, I’ve been leading AI training across my company. It’s been a hefty assignment, but I’ve always found that teaching something to others forces you to make sure you understand it well enough yourself.
The added challenge here is just how quickly everything has been moving. Even when it comes to this Substack, there have been multiple times when I wanted to write about something, only to find that it had already changed by the time I sat down to do it. Everything really is becoming suddenly automated.
Over the past 6 months, I’ve organized three distinct initiatives to drive AI education and adoption across the company:
Company-wide AI 101 training
Function-specific training tracks
An AI hackathon
Company-wide AI 101
Even though the tools themselves have significantly evolved, I believe the overarching content in my AI 101 deck still holds up. It covered what is AI, the main tools, basic examples, responsible use, and where AI may be heading.
The goal was to help everyone understand how AI fundamentally works and give them the basic building blocks to jump in. Hopefully, even the more experienced people learned at least one new thing.
Function-specific training tracks
Next came separate training tracks for Engineering, Product and Design, Customer Experience, Sales and Partnerships, Marketing, Security and IT, and Finance, Operations, People and Legal.
I co-led the Finance, Operations, People and Legal track, but I also sat in on most of the others. Each followed the same basic outline—tools and setup, best practices, and AI in action—but the content varied substantially by function.
In my own session, I learned about Google Apps Script and interactive Claude artifacts. In the Consumer session, I learned about Replit and just how accessible it has become to build and publish your own mobile app. In the Security session, I learned that you should apparently never threaten your chatbot—unless you want it to blackmail you.
I came away wishing each track had included more hands-on-keyboard time. Watching someone show off what they built can be inspiring, but it isn’t the same as building something yourself.
The AI hackathon
The third piece was an AI hackathon, designed to create exactly that hands-on opportunity.
The premise was simple: dedicate a full day to building automations that make our work faster, smarter, or better.
Giving everyone the dedicated time was important. It is difficult to step away from the current process long enough to rethink it. Even when someone knows a task could be automated, the daily work keeps arriving. The hackathon created protected time to put pen to paper, roll up our sleeves, and actually build something.
About a third of the company participated. It was especially exciting to see strong participation from Sales, a group that is often underrepresented in traditional hackathons.
We held a dedicated build day, followed by a demo day one week later to accommodate time zones and scheduling conflicts. Each person or team added their project to a shared presentation, and we awarded prizes in three categories:
Biggest Time Saver: The automation that could win back the most hours
Biggest Unlock: The most impactful solution to an existing bottleneck
Most Likely to Scale: The project with the greatest company-wide potential
The projects included Claude Skills, Slack bots, Claude artifacts, Convey agent jobs, and automations that combined multiple tools. I even used AI to build the scoring workbook for the judges.
It was genuinely inspiring to see what everyone created. It was also valuable to uncover similar projects being developed by different teams that should be collaborating.
Keeping the momentum going
Outside of those three initiatives, I also created a Slack channel (#ai-tips) and started hosting weekly AI office hours.
The office hours were created as a place for people to get advice when they aren’t sure how to approach something. Increasingly, the question isn’t whether a task can be done with AI. It is which of the many possible approaches makes the most sense.
The sessions have become a helpful forum for talking through those options. Some people even come simply to listen, hear what others are trying to build, and learn more about how AI can be used.
My biggest takeaway is that the best way to learn AI is to put it into practice, push the boundaries of what you believe is possible, and create regular opportunities to talk with other people who are learning alongside you.
I’ll end with what I’ve told my coworkers throughout this process:
