Shift focus from the physical object to the process it enables. Whether for surgery, labs, or logistics, successful product development requires deeply understanding and improving the underlying workflow. The specific technology is secondary to a system design that correctly supports the process.

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 distinction between a software product and its human-led delivery is disappearing. Value is no longer in the application alone but in how it empowers human experts to deliver better outcomes. Product teams must design for this human-in-the-loop symbiosis, not just for the end user.

When building complex AI systems that mediate human interactions, like an AI proctor, start by creating a service map for the ideal human-to-human experience. Define what a great real-world proctor would do and say, then use that blueprint to design the AI's behavior, ensuring it's grounded in human needs.

Before writing code, manually perform the customer's workflow as a service. This unsexy approach ensures you deeply understand the process, enabling you to build a superior automated solution later. It's about fulfilling the task first, then building the software.

Shift the AI development process by starting with workshops for the people who will live with the system, not just those who pay for it. The primary goal is to translate their stories and needs into tangible checks for fairness and feedback before focusing on technical metrics like accuracy and speed.

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.

With AI, designers are no longer just guessing user intent to build static interfaces. Their new primary role is to facilitate the interaction between a user and the AI model, helping users communicate their intent, understand the model's response, and build a trusted relationship with the system.

A core design philosophy for B2B SaaS is to shorten the time it takes for a design to face the realities of a production-like environment. Prototyping directly in the browser, powered by AI coding assistants, reveals issues like loading states and responsiveness that static design tools completely miss.