Becoming AI-first: Lessons from 100s of conversations on building AI products (www.ashpreetbedi.com)

After hundreds of calls with builders, CTOs, and CEOs, the author distills what “AI-first” really means and three high-leverage paths companies repeatedly use: internal tools, workflow automation, and user-facing AI features. Rather than re-architecting everything around ChatGPT, AI-first is about finding where intelligence creates real leverage—boosting team productivity, automating repeatable processes, or making products noticeably smarter for users. This framing helps teams prioritize high-ROI initiatives instead of chasing broad, unfocused AI projects. Practically, teams should start by giving employees access to general-purpose agents and coding assistants and connecting them to company knowledge to build intuition. Next, identify repeatable, team-specific workflows to automate (invoice classification, support summarization, sales prep)—expect most robust solutions to require engineering and frameworks (no-code can help early, but serious setups often use code and orchestrators). For user-facing features, begin with off-the-shelf integrations, then add small, reliable “magic” interactions (buttons or focused APIs) served via REST APIs rather than open-ended chatbots, prioritizing reliability and UX. The author recommends Agno as a pragmatic stack: it provides primitives and a FastAPI application you can deploy for privacy/security to accelerate building these features.