In the AI era, marketing and growth roles are splitting into two distinct archetypes: the 'tastemaker' who has exceptional creative taste and intuition, and the 'engineer' who can technically analyze and orchestrate complex systems. Being average at both is no longer a viable path to success.
Gamma scaled to a $2B valuation with only 50 people by innovating on org design, not just product. They prioritize hiring generalists over specialists and use a 'player-coach' model instead of a traditional management layer. This keeps the team lean, agile, and close to the actual work.
When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.
To get mainstream users to adopt AI, you can't ask them to learn a new workflow. The key is to integrate AI capabilities directly into the tools and processes they already use. AI should augment their current job, not feel like a separate, new task they have to perform.
A major hurdle in AI adoption is not the technology's capability but the user's inability to prompt effectively. When presented with a natural language interface, many users don't know how to ask for what they want, leading to poor results and abandonment, highlighting the need for prompt guidance.
Successful AI products follow a three-stage evolution. Version 1.0 attracts 'AI tourists' who play with the tool. Version 2.0 serves early adopters who provide crucial feedback. Only version 3.0 is ready to target the mass market, which hates change and requires a truly polished, valuable product.
Because AI products improve so rapidly, it's crucial to proactively bring lapsed users back. A user who tried the product a year ago has no idea how much better it is today. Marketing pushes around major version launches (e.g., v3.0) can create a step-change in weekly active users.