Instead of focusing on the 'how' (chat vs. voice), DoorDash's AI strategy starts with the 'what': the customer's complete, end-to-end job. For DoorDash, that's getting a physical item delivered. This grounds AI development in solving a real problem, preventing teams from chasing shiny tech without purpose.

The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.

Don't assume AI can effectively perform a task that doesn't already have a well-defined standard operating procedure (SOP). The best use of AI is to infuse efficiency into individual steps of an existing, successful manual process, rather than expecting it to complete the entire process on its own.

AI tooling accelerates the implementation phase of software development but doesn't shortcut foundational business tasks like understanding customer needs or iterating on feedback. The fundamentals of identifying a problem, finding customers, and retaining them remain the most time-consuming part of building a SaaS.

Before any AI is built, deep workflow discovery is critical. This involves partnering with subject matter experts to map cross-functional processes, data flows, and user needs. AI currently cannot uncover these essential nuances on its own, making this human-centric step non-negotiable for success.

Early versions of AI-driven products often rely heavily on human intervention. The founder sold an AI solution, but in the beginning, his entire 15-person team manually processed videos behind the scenes, acting as the "AI" to deliver results to the first customer.

To bridge the AI skill gap, avoid building a perfect, complex system. Instead, pick a single, core business workflow (e.g., pre-call guest research) and build a simple automation. Iterating on this small, practical application is the most effective way to learn, even if the initial output is underwhelming.

Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.

To build an effective AI product, founders should first perform the service manually. This direct interaction reveals nuanced user needs, providing an essential blueprint for designing AI that successfully replaces the human process and avoids building a tool that misses the mark.