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.

Generative AI primarily changes an app's user interface, but agentic AI can bypass UIs entirely to complete tasks. This makes transaction-fulfillment apps, which constitute a huge portion of the market, vulnerable to being replaced by agents that act directly on a user's behalf.

Enterprises no longer need to buy expensive SaaS products for tasks like customer feedback. They can now spin up custom AI agents internally, making it harder for SaaS companies to acquire new customers and leading to higher-than-modeled churn. This poses a fundamental threat to the SaaS business model.

While users building their own tools is a risk, the more profound disruption comes from AI agents performing knowledge work autonomously. This could eliminate the need for human-centric software like project management tools entirely, as agents handle tasks, tracking, and completion without manual input.

Productivity tools have survived due to high user switching costs. Agentic AI presents the first major disruptive threat by creating an abstraction layer that can access data and perform actions across any tool, making the underlying application itself far less important.

Incumbent SaaS companies are starting to block API access for AI agents. They fear agents will bypass their user interfaces to perform the same functions, devaluing their core product and eroding the traditional per-seat revenue model.

Companies are churning from established SaaS products not due to dissatisfaction, but because custom-built AI agents are replacing their core functionality. Notion was canceled after an internal agent took over its role as the source of truth for team meetings.

When AI agents are connected to legacy software like Marketo, they hit API limits and performance issues. The agents themselves then effectively recommend leaving that vendor for more modern platforms, becoming a driving force in tech stack decisions.

The friction of learning a new user interface often prevents customers from switching vendors. This 'UX moat' disappears when an AI agent is the primary user. The agent can instantly master any new system, making migration decisions purely about API quality and cost, not human usability.