Historically, we trusted technology for its capability—its competence and reliability to *do* a task. Generative AI forces a shift, as we now trust it to *decide* and *create*. This requires us to evaluate its character, including human-like qualities such as integrity, empathy, and humility, fundamentally changing how we design and interact with tech.

Consumers can easily re-prompt a chatbot, but enterprises cannot afford mistakes like shutting down the wrong server. This high-stakes environment means AI agents won't be given autonomy for critical tasks until they can guarantee near-perfect precision and accuracy, creating a major barrier to adoption.

Unlike traditional deterministic products, AI models are probabilistic; the same query can yield different results. This uncertainty requires designers, PMs, and engineers to align on flexible expectations rather than fixed workflows, fundamentally changing the nature of collaboration.

The true challenge of AI for many businesses isn't mastering the technology. It's shifting the entire organization from a predictable "delivery" mindset to an "innovation" one that is capable of managing rapid experimentation and uncertainty—a muscle many established companies haven't yet built.

Competing in the AI era requires a fundamental cultural shift towards experimentation and scientific rigor. According to Intercom's CEO, older companies can't just decide to build an AI feature; they need a complete operational reset to match the speed and learning cycles of AI-native disruptors.

The key challenge in building a multi-context AI assistant isn't hitting a technical wall with LLMs. Instead, it's the immense risk associated with a single error. An AI turning off the wrong light is an inconvenience; locking the wrong door is a catastrophic failure that destroys user trust instantly.

Unlike the dot-com or mobile eras where businesses eagerly adapted, AI faces a unique psychological barrier. The technology triggers insecurity in leaders, causing them to avoid adoption out of fear rather than embrace it for its potential. This is a behavioral, not just technical, hurdle.

The key to leveraging AI in sales isn't just about learning new tools. It's about embedding AI into the company's culture, making it a natural part of every process from forecasting to customer success. This cultural integration is what unlocks its full potential, moving beyond simple technical usage.

The most significant hurdle for businesses adopting revenue-driving AI is often internal resistance from senior leaders. Their fear, lack of understanding, or refusal to experiment can hold the entire organization back from crucial innovation.