Cues' initial product was a specialized AI design agent. However, they observed that users were more frequently uploading files to use it as a knowledge base. Recognizing this emergent behavior, they pivoted to a more horizontal product, which was key to their rapid growth and product-market fit.

To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.

Spreading efforts across startups, SMBs, and enterprises created confusing signals. A deep dive into metrics revealed enterprises, despite being a smaller revenue portion, showed the highest expansion potential, prompting a decisive focus that unlocked growth.

Instead of inventing new features, Prepared identified its most lucrative expansion opportunity by seeing users' painful workarounds. They noticed 911 dispatchers manually copy-pasting foreign language texts into Google Translate—a clear signal of a high-value problem they could solve directly.

Founders can waste time trying to force an initial idea. The key is to remain open-minded and identify where the market is surprisingly easy to sell into. Mercor found hypergrowth by pivoting from general hiring to serving the intense, specific needs of AI labs.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Unlike traditional software where PMF is a stable milestone, in the rapidly evolving AI space, it's a "treadmill." Customer expectations and technological capabilities shift weekly, forcing even nine-figure revenue companies to constantly re-validate and recapture their market fit to survive.

The sweet spot for their transformational AI platform wasn't the largest corporations, which are too rigid to adopt new tech. Instead, it was mid-market companies (100-1,000 employees) that had budget and pain but were agile enough to implement new workflows successfully.

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.