TubeBuddy / BENlabs · 2023 – Present
Driving AI Adoption Through Results
The organization was skeptical of AI. I didn't argue — I built. A mobile app port in 4 weeks that contractors couldn't finish in a year.
The Situation
The organization had tried AI. Copilot for code completion. Some experimentation with older models. But the results hadn't been compelling — without investing in understanding model differences and effective prompting, it felt like fancy autocomplete.
There was reasonable skepticism. Early AI tools had limitations, and the hype often outpaced reality. Meanwhile, contractors had been porting our mobile app from Xamarin to MAUI for over a year — and they were still behind schedule.
Reasonable skepticism
Early experiments with AI tools hadn't delivered. The organization needed proof, not promises.
Stalled contractor work
External contractors had been porting the mobile app for 12+ months with no end in sight.
Technology choice concerns
The Xamarin → MAUI migration wasn't ideal. MAUI has less ecosystem support and fewer developers compared to alternatives like React Native.
Building the Skill
While others experimented casually, I invested deeply in understanding how to work with AI. Not just prompting — building a practice.
Research synthesis
Ingesting research data, finding patterns, generating insights. What used to take days of analysis could happen in a conversation.
Rapid prototyping
Building working prototypes during user interviews — testing ideas with short feedback loops instead of waiting for engineering sprints.
Production code
Not just mockups — real React components, interaction patterns, engineer-ready handoffs. UX copy, research plans, feasibility testing.
Workflow automation
AI-powered code reviews, automated documentation, testing infrastructure. Systems that help the whole team, not just me.
A lot of people tried AI, hit a limitation or saw a mistake, and wrote it off as not useful — a binary judgment. I didn't. I kept playing with it, watching it get better and better, learning when to use which model and how to structure problems. The skill compounds — and so do the results.
Changing the Game
Arguments don't change minds. Results do. I found an ally in the CTO and proposed something simple: "What if we just did a hackathon next week?"
The Proposal
The CTO was enthusiastic about AI's potential. I proposed something low-risk: "What if we just did a hackathon for a couple days?" As part of leadership, I had the standing to make it happen.
We carved out a few days. My goal: port the mobile app from Xamarin to React Native — a technology choice with stronger ecosystem support and more available talent.
Days 1-3: Hackathon
A decent number of features were already working. Core navigation, key screens, state management — not just scaffolding, but functional pieces of the app.
Weeks 2-4: Part-time continuation
Kept building in spare hours. Features, polish, edge cases. Still doing my actual job as Head of Design.
Week 4: The reveal
Showed leadership a nearly complete app. Some aspects were better than the original. Their reaction: "Holy crap."
What Changed
The mobile port got attention. But what mattered more was what it unlocked — permission to build things differently.
Labs became the future
We started building a new web portal as a "labs" area — a place to test ideas and prototypes. But the team saw what was possible and wanted to move to it immediately. It's now becoming the foundation for the entire future product, with the majority of new feature value going into the new web app.
Production-grade foundation
Working with Nick (who handled most of the CI/CD), we built a solid foundation: modern frontend stack, light/dark mode, design system with Storybook, analytics, localization, frontend linting. AI assistance accelerated everything.
Team adoption
AI code reviews. AI agents for routine tasks. Claude Skills for specialized workflows. Context documents for project understanding. The whole team started working differently.
What It Demonstrated
The CTO later wrote an internal proposal for broader AI adoption across the organization, using my work as an example of what's possible when someone invests in building the skill. He called it "The Britton Effect" — what happens when someone invests in AI as a real skill, not a toy.
It's not that I'm uniquely talented at coding. It's that I understood the tool deeply enough to apply my actual expertise — design thinking, product sense, user understanding — at a pace that wasn't possible before.
AI doesn't replace expertise — it amplifies it. But only if you build the skill.
What I Learned
Show, don't argue
Skeptics don't change their minds from presentations. They change from seeing results they can't explain away. Build something undeniable.
Find your allies
The CTO was already curious. I didn't try to convince the skeptic directly — I built coalition with someone who wanted to see what was possible.
The skill compounds
Early investment in understanding AI deeply pays dividends. Each project teaches you something that makes the next one faster.
Technology choices matter
The contractors chose Xamarin → MAUI. I chose React Native. Picking the right foundation matters more than how hard you work on the wrong one.