While the tech world obsesses over AI, positioning a product as 'AI-first' can be a liability in mainstream markets. Many non-tech users are skeptical or actively hostile towards AI, making it a poor marketing message. Focus on the problem solved, not the underlying technology which can create backlash.

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.

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.

AI cannot magically create demand. According to Monaco's CEO, AI outbound platforms are amplifiers, not creators, of message-market fit. If your message doesn't resonate with a market problem, the AI will fail, regardless of its sophistication.

The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.

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.

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.

Early on, Templify balanced customer feedback with their own deep domain expertise. They intentionally focused more on demonstrating a new, better way of working rather than simply asking what customers wanted, thus defining the future state of their market instead of just iterating on the past.