The integration of AI into various applications is still evolving, with many companies struggling to effectively implement AI features despite having the technology available.

When building AI tools, companies often find themselves on a long path to feature parity with their existing frontend, which can delay the implementation of essential features.

Many AI products start in a lab or beta phase, and only after stabilizing and reducing costs do they become core offerings.

Conversely, using existing AI-integrated apps often feels like a chore, creating more work instead of simplifying tasks.

The integration of AI into existing workflows requires thoughtful UI design and seamless tool integration to be effective for companies.

Many startups are focusing on selling tools for AI applications, but the reality is that many potential customers are still figuring out how to effectively use AI, leading to a disconnect between product offerings and actual market needs.

Integrating AI into existing businesses can be advantageous, especially for companies with strong distribution channels, allowing for efficient product enhancements.

The disconnect between a well-developed frontend experience and the slower development of AI tools leads to user confusion about which features are supported.

Using AI prototyping tools for production apps can lead to maintenance issues, as they require ongoing attention to bugs and regressions, making them less reliable than dedicated AI agent platforms.