While 75% of partners see AI as essential, adoption is low. The primary barriers are not just talent shortages, but also managing customer expectations, translating AI into specific business value, and overcoming end-customer concerns about trust, transparency, and control over AI-driven outcomes.

An AI like ChatGPT struggles to provide tech support for its own features because the product changes too rapidly. The web content and documentation it's trained on lag significantly behind the current software version, creating a knowledge gap that doesn't exist for more stable products.

When a customer opens a support case, all marketing pretense vanishes. They are frustrated, something is broken, and they need a real solution. This "moment of truth" is where most systems fail due to chaos and complexity, presenting a prime opportunity for AI to streamline and improve the experience.

A major unsolved problem for MCP server providers is the lack of a feedback mechanism. When an AI agent uses a tool, the provider often doesn't know if the outcome was successful for the end-user. This "black box" makes iterating and improving the tools nearly impossible.

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.

SaaS companies face a new hurdle: customers using AI for deep research are often more knowledgeable than the company's own sales and support teams. This creates frustrating customer experiences and exposes a critical need for internal AI literacy across all customer-facing roles.

A major hurdle in AI adoption is not the technology's capability but the user's inability to prompt effectively. When presented with a natural language interface, many users don't know how to ask for what they want, leading to poor results and abandonment, highlighting the need for prompt guidance.

A large customer support organization signals that a product is too complex, hard to onboard, or buggy. Instead of optimizing the support function, companies should focus on improving the product to the point where extensive human support becomes unnecessary.

Even sophisticated users of cutting-edge AI tools like Claude and Perplexity frequently encounter bugs and clunky user experiences. This highlights that reliability and ease of use, not just raw capability, are critical hurdles that AI companies must overcome to achieve widespread adoption.