TubeBuddy / BENlabs AI adoption
A four-week proof that
helped change how we build.
I built a working React Native proof alongside my design leadership role. The CTO used that work as an example in a broader mandate to adopt AI across the organization.
See the working proof- My role
- Head of Product Design & Senior Director
- The proof · 2025
- Four weeks, part-time
- What followed
- A mandate, shared tools, new team practices
The context
The tools had changed.
Expectations hadn’t.
Early coding-assistant trials had disappointed a head of engineering. Those results still shaped expectations as the tools improved.
Meanwhile, the production app’s contractor-led port from Xamarin to MAUI was eighteen months into a six-month plan.
My decision
Make the new capability
tangible.
I kept testing new models across research, prototyping, and code, learning which tools fit which problems.
I partnered with the CTO and proposed a hackathon lasting a few days to give the team something it could see and inspect.
AI adoption / The working proof · 2025
A proof people
could inspect.
Four weeks of part-time work changed the conversation.
I built an independent React Native proof alongside my work as Head of Design, using the existing app’s functionality and APIs as a reference.
It was functional.
I would not have called it design-ready.
What I built
An independent React Native proof.
What it demonstrated
Working mobile functionality.
Interface reconstructions, visually refined for this case study. Sample content shown.
What changed
From something I built
to how we build.
A working demonstration helped change what the team believed it could build.
Leadership commitment
From demonstration
to mandate.
The CTO used my work as an example in a broader mandate to adopt AI across the organization.
Labs became the main environment for building new customer features.
A shared foundation
Nick and I built the shared portal foundation: a design system with Storybook, light and dark themes, analytics, and localization.
Nick led most CI/CD.
New team practices
AI code reviews, agents for routine tasks, specialized skills, and shared context became part of how the team worked.
Explore the shared foundationWhat It Demonstrated
The CTO 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
Prove the inflection point
Early disappointment can outlast the technology that caused it. A result people can inspect makes new capability concrete.
Build with believers
The CTO already understood AI’s potential. Partnering with someone who saw the opportunity created room to turn curiosity into organizational proof.
The skill compounds
Early investment in understanding AI deeply pays dividends. Each project teaches you something that makes the next one faster.
Keep the scope clear
My independent proof and the production rewrite had different scopes. The demonstration showed what I could get working with AI assistance; design refinement and production readiness remained separate work.