Gartner: $234 Billion in Enterprise Application Software Spend Is at Risk from Agentic AI

Agentic AI is set to disrupt enterprise software revenue models, with up to $234 billion of enterprise application spending exposed to agentic arbitrage between now and 2030, according to Gartner. By 2030, this will account for roughly 20% of enterprise application software-as-a-service (SaaS) spending, according to the market researcher.

Agentic arbitrage happens when AI agents complete tasks across multiple systems, reducing the need for users to interact with multiple traditional software interfaces.

“Agentic AI changes the economics of software,” said George Brocklehurst, Managing Vice President at Gartner. “Agentic systems deliver outcomes directly, bypassing traditional user experience (UX)-heavy applications and making the software invisible. This breaks the link between user growth and revenue growth for many enterprise software vendors.”

“The shift to agentic AI will also lead to a redefinition of ‘Saaspocalypse’, the disaggregation of the legacy SaaS market as we know it today,” said Brocklehurst. “This is less an apocalypse and more of a metamorphosis. SaaS will not be destroyed; it will emerge in a different form. This metamorphosis represents threats and opportunities for both incumbents and new challengers.

“Enterprise buyers will deemphasize buying more new tools or dashboards,” added Brocklehurst. “They want better outcomes and adding more AI features often creates more cost, not better outcomes. Better outcomes from AI require systems that can retain deep institutional memory and customer context over time.”

To remain competitive and achieve growth opportunities, incumbent software vendors must move from interface-based value to outcome-based value, embed agentic capabilities at the point of execution into their offerings to defend their position in the value chain, capture and retain customer-specific knowledge, not just data.

Channel Impact^®
AI-native startups and service providers can act as the agentic layer across enterprise systems, deliver measurable outcomes instead of features and assist organizations redesign workflows around AI.