Is SaaS dying? It’s probably too early to say. But artificial intelligence is putting pressure on one of the premises that held the model together for years: paying for a tool and handling internally all the work needed to get results.
Every time ChatGPT, Claude, or another model adds new capabilities, products that had built their edge around a specific feature can lose value fast.
The question for many tech companies is then fairly direct: how do you build a competitive advantage when the underlying model improves so quickly?
One of the answers is starting to emerge around services.
Sequoia Capital argued in 2026 that the next trillion-dollar company could be a software company that, in practice, behaves like a services company.
The thesis rests on a telling data point: for every dollar companies spend on software, they spend roughly six on services.
That market opens space for a new model: Results as a Service, or RaaS.
What Is Results as a Service (RaaS)
In a SaaS model, the customer pays to use a tool. In RaaS, the customer pays for a result.
The difference changes far more than the pricing scheme. The provider is no longer limited to delivering technology and begins to take on part of the execution.
In customer service, the outcome might be a resolved ticket. In sales, a qualified opportunity or a conversion. In collections, a recovered debt. In professional services, a processed document or a completed task.
Software has value when it produces something the business needs.
And if the provider can take direct ownership of that result, it can also start to capture a share of the budget that used to be reserved for services.
Why AI Is Accelerating the Shift from SaaS to RaaS
The pace at which models evolve introduces a particular risk for products built on easily replicable features.
A tool may take months to develop a capability that then shows up natively integrated into a new version of ChatGPT, Claude, or another model.
A results-oriented company faces the problem from a different angle. If the model improves, it can use that improvement to deliver the service faster, at a lower cost, or with better quality.
Sequoia adds another dimension: the difference between intelligence and judgment.
AI can progressively absorb tasks based on reasoning, information, and rules.
But the judgment developed over years inside an industry (how a process is actually executed, which exceptions matter, when to step in, and what doing good work really means) can become an advantage that’s much harder to copy.
Bairong: From Software to Results as a Service
A particularly interesting example comes from China. Bairong Inc. launched a strategy called Results as a Service in late 2025, along with its Results Cloud platform.
According to PR Newswire, the offering uses AI agents to execute business functions and ties pricing to verifiable results.
Bairong proposes different schemes: charging per completed task, per role taken on by agents, or per value generated for the business.
The shift is significant because it moves the conversation from “what features the software has” toward “what result it delivers.”
For the customer, it also changes how risk is distributed: a larger share of the cost becomes tied to the outcome achieved.
Outcome-Based Pricing Is Already Showing Up in Major Platforms
Bairong represents an explicit version of RaaS, but the move toward results-based pricing is already appearing within the SaaS market itself.
Intercom charges from USD 0.99 per outcome generated by Fin, its AI agent. Those results can include a resolved query or certain completed workflows.
Zendesk has also introduced models where the price of its AI agents is tied to completed resolutions.
HubSpot moved in the same direction in 2026: Breeze Customer Agent charges per resolved conversation, and Prospecting Agent charges per lead recommended for outreach.
These companies remain software platforms. But the signal is clear: even SaaS is starting to experiment with an economy based less on access and more on execution.
A Services Company’s Next Competitor Could Be a Software Company
This trend has an especially important consequence for agencies, accounting firms, law firms, consultancies, and other knowledge-intensive businesses.
Their next competitor may not be another firm. It may be a tech company able to use AI agents to directly do the work their clients already pay for.
An agency sells marketing results. An accounting firm delivers financial processes. A law firm resolves legal work. A contact center sells support, sales, or recovery.
Companies have always paid for results. What’s beginning to change is who can charge for them.
Expertise Is Still a Competitive Advantage
This scenario doesn’t mean services companies are automatically displaced, either.
In fact, they may have an advantage many tech companies still need to build: they know the work deeply.
A creative agency may have spent years developing a particular way of interpreting a brand, building campaigns, and evaluating an idea.
A law firm knows exceptions and criteria that rarely appear fully documented.
A customer experience company accumulates millions of interactions that make it possible to understand what works and what fails with a real customer.
That knowledge can be turned into agents, workflows, software, and proprietary evaluation systems.
Here, concepts like taste and judgment gain importance.
The technical ability to generate a response can be commoditized. Knowing which is the right response for a given company, customer, or situation is much harder.
Firms that manage to codify their expertise around AI agents can use precisely what they know best as a competitive barrier.
From Charging for Activity to Charging for Performance
AI also makes it possible to measure more precisely what result each interaction produced.
That enables commercial models such as:
- Revenue share on sales
- Commission per conversion
- Price per qualified lead
- Pay per resolved ticket
- Fee per recovery
- Pricing tied to KPIs
The provider takes on more responsibility, but can also capture more value when its operation performs.
For the customer, the discussion starts to shift from how much the license costs toward how much impact it generates.
At ChatCenter, this evolution is already part of our operating model.
We work with AI voice and chat agents focused on results in sales, customer service, customer recovery, demand generation, and collections, with commercial structures that can include revenue share, commission per sale, CPL, and pay per resolved ticket.
Is SaaS dead? No. But the line between software and services is moving.
SaaS will still make sense when a company wants to directly control a tool and run its own processes.
What’s new is that AI agents let tech companies also compete for the budget allocated to the work itself.
RaaS, outcome-based pricing, and AI-Native Services are distinct signals of that same transformation.
For software companies, the challenge will be to build value that goes beyond an easily replicable feature.
For services companies, the opportunity may lie in turning years of expertise, judgment, and operational knowledge into agents and systems capable of executing that work at scale.
Learn how to apply Performance AI to sales, customer service, collections, and demand generation with measurable results.
