A common trap is starting with the assumption that AI must be used, leading to a search for a place to tack it on. This results in superfluous features like a generic "AI assistant," rather than solving a real user need. The correct approach begins with the user's pain.

Don't feel pressured to label every AI-powered enhancement as an "AI feature." For example, using AI to generate CSS for a new dark mode is simply a better way to build. The focus should be on the user benefit (dark mode), not the underlying technology, making AI an invisible, powerful tool.

Founders often mistakenly market "AI" as the core offering. Customers don't buy AI; they buy solutions to their long-standing problems (e.g., more leads, better service). Frame your product around the problem it solves, using AI as the powerful new tool in your solution space that makes it possible.

Most users don't want abstract tools like 'agents' or 'connectors.' Successful AI products for the mainstream must solve specific, acute pain points and provide a 'golden path' to a solution. Selling a general platform to non-technical users often fails because it requires them to imagine the use case.

While marketing a product as "AI-Free" might appeal to a niche audience, similar to "handcrafted" goods, it's unlikely to be a successful strategy for mass-market brands. Ultimately, consumer behavior at scale is driven by price and quality, not a company's internal AI use.

In a market where every vendor claims to be "AI-powered," differentiation comes from focusing on outcomes. AI should be messaged as a force multiplier that improves existing workflows, enhances efficiency, and provides intelligence, not as a standalone product.

Vendors fail to connect with SMBs on AI because their messaging is either too technical and intimidating or too aspirational and fluffy. SMB partners and customers want clarity, not hype. They need simple, concrete use cases demonstrating tangible business value like productivity gains or automation, not visions of futuristic robots.

With AI development becoming accessible, having an "AI product" is not a sustainable advantage. True defensibility comes from solving a specific customer problem better than anyone else, using AI as a tool, not the core value proposition. The challenge is no longer building, but deciding what to build.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.