Instead of hiring a 'Chief AI Officer' or an agency, the most successful GTM AI deployments empower existing top performers. Pair your best SDR, marketer, or RevOps person with AI tools, and let them learn and innovate together. This internal expertise is more valuable than any external consultant.

Athena discovered that providing a world-class assistant wasn't enough; they also had to educate clients on the skill of delegation. This highlights that for complex services, investing in client training is as crucial as the service itself for ensuring customer success.

The turning point came when a simple OpenAI API call solved a customer's problem more effectively than their complex, slow data science script. This stark contrast revealed the massive opportunity in leveraging modern AI and triggered their pivot.

Beyond automating 80% of customer inquiries with AI, Sea leverages these tools as trainers for its human agents. They created an AI "custom service trainer" to improve the performance and consistency of their human support team, creating a powerful symbiotic system rather than just replacing people.

To create scalable offers that deliver results without you, shift from asking 'What do I know?' to 'What must my people do?'. Transformation comes from implementation, not just information. You must surface the hidden, instinctual actions and decisions that experts make to provide customers a clear path to results.

Enterprises struggle to get value from AI due to a lack of iterative, data-science expertise. The winning model for AI companies isn't just selling APIs, but embedding "forward deployment" teams of engineers and scientists to co-create solutions, closing the gap between prototype and production value.

Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.

The surprising success of Dia's custom "Skills" feature revealed a huge user demand for personalized tools. This suggests a key value of AI is enabling non-technical users to build "handmade software" for their specific, just-in-time needs, moving beyond one-size-fits-all applications.

Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.