"AI first" doesn't mean automating everything or replacing people. It means asking a different question at the start of every project: "How can AI amplify what we're building?" Before designing workflows, before scoping features, before writing a single line of code — consider what AI makes possible that wasn't possible before.
Most teams bolt AI onto existing products as a feature. AI-first thinking inverts this: the AI capability shapes the product, not the other way around. It's the difference between "let's add a chatbot" and "what if the entire onboarding experience was conversational?" The first is incremental. The second is transformative.
"Don't add AI to your product. Reimagine your product with AI."
This philosophy extends to how I work, not just what I build. AI assists in research, prototyping, writing, analysis, and code. Not as a replacement for thinking, but as a multiplier. A rapid prototype that would take a week now takes a day. User research synthesis that took hours now takes minutes. The time saved goes into the work that only humans can do: judgment, empathy, and creative problem-solving.
The risk of AI-first thinking is cargo-culting — using AI because it's trendy, not because it's useful. Every AI integration needs the same test as any feature: does it solve a real problem better than the alternative? If a simple rule-based system works, use that. AI-first is a mindset, not a mandate.
In Practice
- — Every new project starts with an AI opportunity scan — what can AI do here that humans can't do at scale?
- — Prototypes leverage AI-assisted development to validate ideas in days, not weeks.
- — AI tools are part of the daily workflow — not experiments in a sandbox, but integrated into real work.