Out-of-the-box AI impresses novices but fails to meet expert standards. The product goal should be to capture an expert's knowledge through iterative feedback, so that by the third interaction, the AI's output is exceptional and personalized.
When enterprises hire external firms, they outsource not just costs but also institutional knowledge. AI platforms can reverse this by capturing learnings from external engagements, building a proprietary 'brain' for the company and keeping knowledge in-house.
Founders should focus on how AI can replace or augment human labor and services, which constitute the vast majority of enterprise budgets, rather than just layering AI onto existing software.
Instead of selling AI tools to incumbents (e.g., law firms who bill by the hour), build an AI-first service that delivers the end result directly to the customer. This avoids conflicts of interest and captures more value.
The key to valuable enterprise AI is solving the underlying data problem first. Knowledge is fragmented across systems and employee heads. Build a platform to unify this data before applying AI, which becomes the final, easier step.
When engaging C-suite design partners, frame the partnership around a massive, long-term value goal (e.g., $100M). This filters for strategic partners and aligns the conversation on transformative impact, not incremental features.
In deep enterprise plays, early ARR can be a misleading metric. The founder focused on a small number of customers with massive expansion potential ($10M+ ARR), prioritizing deep integration and value creation over premature scaling and surface-level growth.
Instead of a traditional slide deck, the founder raised a $6M seed round using an 80-page transcript of C-suite interviews. This powerfully demonstrated deep market understanding and buyer desperation, de-risking the investment based on problem validation.
The founder secured 80 interviews and five C-suite design partners, including at MasterCard, by sending cold emails focused on a compelling thesis about AI's impact on labor, not a product. This high-level validation came before writing significant code.
The ultimate signal of product-market fit is when your go-to-market strategy simplifies to 'get a customer in a room with a prospect.' When customers become your most effective sales channel, you have found it, and your team can 'walk away'.