Contrary to the vision of free-wheeling autonomous agents, most business automation relies on strict Standard Operating Procedures (SOPs). Products like OpenAI's Agent Builder succeed by providing deterministic, node-based workflows that enforce business logic, which is more valuable than pure autonomy.

Before launch, product leaders must ask if their AI offering is a true product or just a feature. Slapping an AI label on a tool that automates a minor part of a larger workflow is a gimmick. It will fail unless it solves a core, high-friction problem for the customer in its entirety.

Customers are hesitant to trust a black-box AI with critical operations. The winning business model is to sell a complete outcome or service, using AI internally for a massive efficiency advantage while keeping humans in the loop for quality and trust.

Enterprise buyers are drawn to the vision of full automation ("the sizzle"), but their immediate need is improving existing human workflows ("the steak"). A startup must offer both. The visionary product gets them in the door, while the practical agent-assist tool delivers immediate value and gathers necessary data for future automation.

For founders, AI tools are excellent for quickly building an MVP to validate an idea and acquire the first few customers—the hardest step. However, these tools are not yet equipped for the large-scale, big-picture thinking and edge-case handling required to scale a product from 100 to a million users. That stage still requires human expertise.

The "last mile" difficulty of implementing AI agents makes them economically viable for huge enterprise deals (justifying custom engineering) or mass-market apps. The traditional SaaS sweet spot—the $30k-$50k mid-market contract—is currently a "missing middle" because the cost to deliver the service is too high for the price point.

Early versions of AI-driven products often rely heavily on human intervention. The founder sold an AI solution, but in the beginning, his entire 15-person team manually processed videos behind the scenes, acting as the "AI" to deliver results to the first customer.

Traditionally, service businesses lack scalability for VC. But AI startups are adopting a 'manual first, automate later' approach. They deliver high-touch services to gain traction, while simultaneously building AI to automate 90%+ of the work, eventually achieving software-like margins and growth.

To build an effective AI product, founders should first perform the service manually. This direct interaction reveals nuanced user needs, providing an essential blueprint for designing AI that successfully replaces the human process and avoids building a tool that misses the mark.