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.

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 the industry buzzes about sophisticated "agentic AI," the most common real-world applications in e-commerce are far more basic. Retailers are primarily using AI for task-oriented work like optimizing SKU description pages, highlighting a significant gap between current capabilities and future hype.

Despite significant promotion from major vendors, AI agents are largely failing in practical enterprise settings. Companies are struggling to structure them properly or find valuable use cases, creating a wide chasm between marketing promises and real-world utility, making it the disappointment of the year.

The market is rejecting 'lame co-pilots' that provide minor workflow improvements for an extra fee. Successful AI products create entirely new, powerful use cases and deliver substantial, tangible value on day one, justifying their place in the budget.

The primary hurdle for potential AI agent users isn't the technical setup; it's the inability to imagine what to do with the tool. Even technically proficient individuals get stuck on the "what can I do with this?" question, indicating that mainstream adoption requires clear, relatable examples and blueprints, not just easier installation.

A free trial for an AI agent hosting service revealed an unexpected user behavior: spinning up powerful AI agents for specific, time-bound tasks (like a coding project or planning a trip) and then letting them self-destruct. This concept of temporary agents opens up new possibilities beyond persistent personal assistants.

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.

Companies racing to add AI features while ignoring core product principles—like solving a real problem for a defined market—are creating a wave of failed products, dubbed "AI slop" by product coach Teresa Torres.