A significant implementation roadblock is the ownership battle between IT and business functions. IT wants to control infrastructure and moves slowly, taking years. In response, business units run their own unsanctioned initiatives to move quickly, leading to a disconnected and unscalable approach to AI.

The next major competitive threat isn't a rival company, but your customer's own AI agent. These agents will silently and autonomously replace underperforming software by building alternatives or switching to a better API. This churn happens without any sales cycle or warning.

The primary threat from AI disruptors isn't immediate customer churn. Instead, incumbents get "maimed"—they keep their existing customer base but lose new deals and expansion revenue to AI-native tools, causing growth to stagnate over time.

Incumbent companies are slowed by the need to retrofit AI into existing processes and tribal knowledge. AI-native startups, however, can build their entire operational model around agent-based, prompt-driven workflows from day one, creating a structural advantage that is difficult for larger companies to copy.

Despite the power of new AI agents, the primary barrier to adoption is human resistance to changing established workflows. People are comfortable with existing processes, even inefficient ones, making it incredibly difficult for even technologically superior systems to gain traction.

An established customer base is both an asset and a liability. The endless demands for features and support for the core product can consume over 98% of engineering resources. This "trap" leaves little capacity for the focused work needed to create a competitive AI product, causing companies to fall behind.

While AI accelerates development, it risks eliminating the healthy friction between departments where valuable insights are born. The iterative debates between functions like sales and engineering uncover crucial nuances. Over-reliance on AI can lead to siloed, less robust innovation by minimizing these collaborative learning moments.

Even with capital and data, incumbents struggle to compete with focused AI startups because of cultural inertia. Existing go-to-market strategies, sales compensation, org structures, and obligations to a large customer base are fundamental laws of physics that prevent large companies from moving at startup speed.

According to Box CEO Aaron Levy, the biggest barrier to deploying AI agents isn't technology but corporate structure. Agents deliver the most value on processes that cross departments, but data fragmentation and a lack of central ownership across these silos prevent effective implementation.