Before building, founders in complex industries must deeply understand the operational rigor and nuances of their target vertical. This 'operator market fit' ensures the solution addresses real-world workflows, as a one-size-fits-all approach is doomed to fail.
Frame your go-to-market strategy as an engineering problem. Create a dedicated 'GTM engineering team,' including actual engineers, to build a programmatic stack and apply a rigorous test-and-learn mindset to every GTM motion, from outbound campaigns to event strategy.
A core principle for developing successful AI products is to focus on amplifying human capabilities, not just replacing them. The vision should be to empower human teams to perform the most demanding cognitive tasks and increase their impact, which leads to better product design and user adoption.
The most significant value from AI is not in automating existing tasks, but in performing work that was previously too costly or complex for an organization to attempt. This creates entirely new capabilities, like analyzing every single purchase order for hidden patterns, thereby unlocking new enterprise value.
When building core AI technology, prioritize hiring 'AI-native' recent graduates over seasoned veterans. These individuals often possess a fearless execution mindset and a foundational understanding of new paradigms that is critical for building from the ground up, countering the traditional wisdom of hiring for experience.
Effective enterprise AI needs a contextual layer—an 'InstaBrain'—that codifies tribal knowledge. Critically, this memory must be editable, allowing the system to prune old context and prioritize new directives, just as a human team would shift focus from revenue growth one quarter to margin protection the next.
Go beyond simple ROI to measure pilot success. Focus on: 1) Time to Value: delivering measurable outcomes within weeks. 2) Expansion Velocity: enabling the customer to achieve new business growth. 3) Engagement Depth: the customer actively pulling your product into new functions and creating a wishlist of use cases.