At HumanX in Amsterdam, I joined a conversation about whether this is the “end of SaaS” moment we have been building towards. My answer was straightforward: SaaS is not disappearing, but the model that defined it for the past two decades is changing quickly.
The shift is less about the death of software and more about a new expectation for what software should do. Tomorrow’s market leaders won’t be traditional SaaS companies, they’ll be AI platforms grounded in decades of real-world know-how. That means: moving from seat-based pricing to outcome-based models; from chatbots to cross-platform agents collaborating in autonomous workflows; and from systems of engagement tied to static records to intelligent systems of action that execute, resolve, and learn.
For years, enterprise software was built around seats. The more people using a product, the more value it was assumed to create, so companies optimised for adoption and engagement. That model made sense when software primarily helped people organise and manage work. It makes less sense now, when software can increasingly carry that work through to completion.
We are driving that same caliber of revolution. At Zendesk, we’ve turned decades of customer knowledge and industry expertise into an autonomous system of action built to deliver measurable outcomes. We’re not layering intelligence onto legacy tools. We’re redefining how service operates with AI driving resolution at scale and humans resolving complex issues.
We introduced outcome-based pricing (OBP) in 2024, becoming the first in the CX industry to do so, because traditional pricing models were no longer aligned with the way customers were using AI agents. Since then, Zendesk has doubled adoption of its OBP model within its customer base, with over 30,000 AI customers.
Customers should not pay simply because an interaction took place, they should pay when a problem is resolved, a transaction is completed or a customer gets what they need. This reflects a fundamental shift in how software creates value. Our model is designed around that principle: customers pay for automated resolutions, with clear measurement and visibility into how those outcomes are counted.
That transparency matters because OBP only works when the outcome is clear to both sides. Customers need to understand what qualifies as a resolution, see how their usage is developing and retain control over where AI is applied. The model also gives them a lower-risk way to adopt AI: they are not paying only for the promise of value, but for value that has actually been delivered.
The strongest evidence is in what customers are achieving: for example, Best Egg uses Zendesk AI to automate 80% of its chat enquiries, while TeamSystem automates up to 80% of repetitive queries. That does not remove the human role, it gives service teams more time to focus on the issues that require judgement, expertise and empathy.
The conversation at HumanX went beyond pricing. AI agents will not operate in isolation – the next generation of enterprise AI will be made up of specialised agents working together across business systems and alongside human teams. That requires more than a powerful model, it requires context, secure integrations, permissions, governance and a clear understanding of what the system is accountable for.
The most effective organisations will not be the ones that choose between humans and AI, but the ones that understand where each is strongest and design the operating model around that combination. AI can handle scale, but people remain essential wherever context, judgement and trust are central to the outcome.
At Zendesk AI Summit on November 12, we will take this conversation further, looking at how service leaders can turn AI ambition into practical strategies and real, end-to-end resolution – alongside new product announcements.
SaaS is not ending; it is becoming more intelligent, more autonomous and more accountable for the outcomes it creates. The future of enterprise software will not be defined by how many seats it supports, it will be defined by how many meaningful outcomes it delivers.
