The recent tech stock selloff has fueled the “SaaSpocalypse,” with some SaaS companies losing 15–20% of their market value amid market reactions to the Agentic AI-driven threat. Over the past few weeks, I have advised several dozen private equity and institutional investors and discussed the impact of Agentic AI on tech stocks with industry analysts. Grounded in 25 years of leadership across the enterprise and AI software space, I see the recent market reaction being far ahead of reality.
One of the biggest misunderstandings in the “SaaSpocalypse” conversation is the focus on the products’ future while ignoring the vendors’ business models. But the more important question is this: if AI reduces the importance of users and seats, how will software companies make money next? That is the issue sitting underneath the noise. Enterprise SaaS is not simply facing a product transition, but also the question of how vendors will monetize it.
What Comes After User-Based Metrics When Agents Do The Work
For years, SaaS companies scaled around a simple commercial model: charging for access to theirsoftware. That usually meant a seat, a license, or a subscription based on the number of human users who needed to use the product. The model worked extremely well because the software’s value was closely tied to the people actively using it, and the incremental cost per additional user was close to zero.
AI changes that model. When agents begin drafting, analyzing, routing, completing, or resolving work on behalf of people, fewer humans may need to spend time inside the interface. That does not mean the software becomes less valuable. In many cases, it even becomes more valuable. But it does mean the unit of pricing becomes less obvious.
That is why the biggest revenue question in enterprise SaaS right now is not whether vendors can still monetize, but rather what they will monetize. And history says they will find an answer, quickly. Software companies have always been good at evolving pricing when the market changes. They moved from on-premises licensing and maintenance fees to recurring cloud subscriptions, and they will move again.
The biggest challenge vendors face is that the next model must be easy for buyers to understand while protecting the vendors’ margins. That is not easy when AI cost structures, such as token cost and token volume, fluctuate underneath the surface.
Outcome-Based Pricing Ties Software to Business Value
One of the most discussed models is outcome-based pricing. Instead of charging for access, vendors charge for a business result. That might be a successfully resolved service ticket, a completed workflow, a processed document, a matched invoice, or another measurable operational outcome. Companies like Salesforce, Zendesk, and Intercom have been piloting this model for their Agentic AI-enabled products.
There is a clear appeal here as it brings the commercial conversation closer to the language the buyer already uses. A customer service leader understands ticket resolution. A finance leader understands invoice matching or payment reconciliation. An HR leader understands job descriptions, candidate screening, or onboarding tasks. When software pricing aligns with a recognizable, measurable business object or outcome, the value discussion gets much easier. It is also a major shift!
In a seat-based model, buyers often pay for capacity they may not fully use. In an outcome-based model, the promise is stronger, and you pay for what gets done. That can make the vendor feel more accountable and more like a partner in business performance than a product sitting on top of the process.
But outcome-based pricing only works cleanly if both sides agree on what success means and how to measure it. If a vendor says a service ticket was resolved and the customer says it wasn't, friction starts immediately. So the commercial simplicity on the surface depends on a very careful definition underneath. What counts as success, what counts as failure, and who decides all have to be clarified upfront. The information also needs to be traceable and verifiable through logs, reporting, and cost estimators.
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Alongside outcome-based pricing, other models are gaining traction. Usage-based pricing is the most obvious at the infrastructure and platform layers. That includes tokens, credits, API calls, and other forms of measurable consumption. It is simple from a technical perspective, but it is often less intuitive for business buyers. The customer may understand they are paying for usage, but not always what that usage means in real business terms or how to estimate it over time.
That is one reason business-object pricing may end up being a particularly important middle ground. Instead of charging for abstract model activity, the vendor charges for units of business work. That could mean documents processed, contracts analyzed, bills of lading extracted, forecasts generated, reports created, or job descriptions completed. Some of these metrics are already common for Generative AI product features, and vendors will expand them to AI agents doing the work.
This matters because the closer pricing gets to a tangible business object, the easier it becomes for the buyer to compare the new model against the existing process. What does it cost us today to do this with people, legacy software, or outsourcing? What does it cost under the AI-enabled model? That is a conversation buyers are equipped to have. From the vendor side, this also creates room to monetize where they actually add value: not in raw model access, but in applying models to enterprise workflows, with context, governance, and repeatability.
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This is where the conversation becomes more strategic. Beyond preserving revenue as seat counts potentially weaken, tech vendors will also identify new sources of value (read: revenue) within their systems. One of the biggest sources of value for vendors and customers alike is the customer data that SaaS applications already hold, along with the context and semantics that make it useful.
Raw data alone is not much of a moat. The stronger moat comes from understanding what the data means, how it connects across workflows, and how it can be used to drive a result. In an enterprise system, a record is not just a record. It sits inside a structure and connects to roles, approvals, histories, exceptions, dependencies, and business rules. That semantic layer is where the real monetization opportunity lies.
A plain model can generate generic output. Enterprise software vendors can write output grounded in a customer’s own operational context. That difference is commercially meaningful and valuable. It allows vendors to charge not only for the software layer but also for governed access to business objects, context-rich orchestration, AI-assisted decisioning, and the ability to work across connected systems, with the right safeguards in place. In other words, vendors continue to do the heavy lifting; their customers participate through economies of scale.
That is why it would be a mistake to assume that AI naturally lowers what enterprises spend on software. In some cases, it may actually raise spend because the vendor can tie pricing more directly to business outcomes and make the value more visible than under a flat seat model.
Margin Management Becomes Harder for Vendors
The challenge for vendors is that even if pricing becomes more intelligent, the underlying cost structure will become more complex. A vendor may charge a fixed amount for an outcome or a business object, but their own costs can fluctuate based on token usage, model choice, task length, orchestration complexity, and the number of agent interactions required to complete the work.
That means the transition will not be clean, and vendors will experiment. They will set early prices, learn from customer usage, adjust the boundaries of what is included, and refine the metric over time. Some will place caps or thresholds around what counts as one unit of work. Others will bundle usage into packages. Many will look for a structure that gives customers confidence while protecting themselves from cost overruns.
So when people worry that changing revenue models make SaaS less attractive, they are noticing that predictability becomes harder in the short term. But that does not mean vendors stop monetizing effectively. Instead, the industry is searching for the next accepted commercial language.
Summary
The next chapter of enterprise SaaS will not be defined only by new AI features, but also by a new pricing logic. As AI agents reduce the centrality of the human seat, vendors will move toward outcomes, usage, business objects, and access to the contextual data inside their systems. The most important shift is that monetization moves closer to measurable business value. Finally.
Software companies will continue to make money, but the accepted unit of value the market will settle on is the next unknown. Once that becomes clear, the shape of the next generation of enterprise software economics will become much easier to see.
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