If you've ever bought a "best-in-class" SaaS platform and then spent a year implementing it, only to realize you still didn't get the result you wanted, you already understand the problem RaaS is trying to solve.
In 2026, we're going to see more vendors make a simple, uncomfortable promise: don't pay us for software. Pay us for outcomes. Not hours. Not seats. Not tokens. Not features. Results.
I've been digging into this because it's showing up everywhere: pricing pages, product roadmaps, procurement conversations, and the way teams are deploying AI agents. The pattern is clear. AI and agents make outcome delivery feasible in ways it wasn't two years ago, and economic pressure makes it inevitable.
This is Part 1 of a two-part series. Here I'll explain what Results-as-a-Service actually is, show real examples where early versions are already working, and set up the architectural question that Part 2 will answer: what has to be true for RaaS to be real?
(Also: yes, "RaaS" is an overloaded acronym. This is Results-as-a-Service, not the other RaaS you're thinking of.)
A Beginner-Friendly Definition
Results-as-a-Service (RaaS) is a commercial and operating model where a provider is paid for a measurable outcome, not for access to a tool.
SaaS sells capability. RaaS sells achievement.
A simple frame: SaaS says "here's the software, good luck." Services say "here are smart people, we'll try." RaaS says "here's the outcome, we own the delivery."
That last line is the whole point. "We own the delivery."
If you want a crisp mental model: RaaS is SaaS with skin in the game.
Why RaaS Becomes Attractive in 2026
Outcome-based models exist today. Performance marketing, fraud guarantees, contingency fees. What's new is that AI agents let software providers take responsibility for end-to-end execution at scale, not just provide tooling.
Three forces converge in 2026.
The Economics of AI Break Seat-Based Pricing
Seat-based pricing assumed value scales with human usage. Agents break that assumption.
If a product automates what 50 people used to do, the "right" customer outcome is fewer seats, which is catastrophic for the vendor's revenue model. So pricing evolves: per seat becomes per usage, per usage becomes per work unit, per work unit becomes per outcome.
Procurement Is Shifting From Features to Risk
In most enterprises, the AI conversation is no longer "can it do it?" It's: who is accountable when it fails? How do we measure value? How do we stop paying for shelfware?
Outcome-based pricing pushes risk onto the provider. Vendors only get paid when they deliver value, and customers effectively renew with their usage.
Agents Make Execution a Product Primitive
If AI becomes the front end and software becomes a capability graph, the natural UX shifts from "open an app" to "state an intent." If you've read my AI-Native Computer series, you'll recognize this shift. The interface layer changes fundamentally when AI mediates between users and capabilities.
And once users think in intents, the question becomes: why am I buying tools at all? Why not buy the outcome?
Yesterday: "Which app should I open?" Tomorrow: "What outcome do I want?"
The RaaS Ladder
Not everything that looks like RaaS is actually RaaS. There's a middle step that matters.
The progression looks like a ladder:
SaaS (pay for access): You pay whether you win or lose. The vendor sells you a platform, maybe some training, maybe some professional services. Implementation, adoption, and outcomes are your problem.
Work-as-a-Service (pay for work done): You pay when the system does something measurable. A conversation handled, a record updated, a document processed. The vendor takes on execution risk for discrete tasks.
RaaS (pay for business outcome): You pay when impact is realized. A case resolved, a chargeback prevented, a qualified meeting booked. The vendor owns the delivery end-to-end.
The closer pricing gets to outcomes, the more the provider carries risk, and the more the provider needs architecture to manage that risk. That's the subject of Part 2.
Early Successes: Where RaaS Is Already Real
The clearest proto-RaaS today is customer support, because the volume is high, the outcome is observable, and the ROI math is brutal and obvious.
Customer Support: Pricing Per Resolution Is Mainstream
Intercom's Fin AI Agent is priced at $0.99 per resolution, explicitly "pay only for resolved customer conversations." Since Fin 2, their average resolution rate climbed to 66% across 6,000+ customers, with 20%+ of customers above 80%.
A concrete example: Lightspeed reports Fin resolving 45–65% of support volume, involved in 99% of conversations, and able to answer 95% of them.
Zendesk defines and bills for "automated resolutions," customer requests resolved without human escalation. They note that conversations flagged as resolved are verified by an LLM to ensure accuracy.
Notice what's happening here. Outcome-based pricing is dragging verification, measurement, and auditability into the product. The receipt isn't a nice-to-have. It's becoming the product.
CRM Platforms: Pay Per Conversation and Pay Per Action
Salesforce's Agentforce pricing has included $2 per conversation. In 2025, Salesforce introduced Flex Credits, a pay-per-action model, and noted that thousands of organizations had leveraged the per-conversation approach.
Is "per conversation" a true business outcome? Not always. But it's still the direction: away from seats and toward measurable units procurement can reason about.
Fraud Protection: Pay Only for Approved Orders
Riskified's Chargeback Guarantee is explicit: "Pay only for approved orders that generate revenue," and they cover chargebacks. A case example: a U.S.-based ticketing merchant increased approval rates by approximately 7% and decreased chargeback rates by approximately 30% after switching.
This is RaaS in its cleanest form. Measurable outcome, provider carries financial risk, provider is incentivized to continuously improve.
Enterprise Results Clouds
In December 2025, Bairong announced a Results-as-a-Service strategy and launched a "Results Cloud" platform for deploying and managing AI agents at scale.
What stands out is the architecture language: "measurable delivery," "audit-traceable accountability," and closed-loop tracking of operational processes to quantify business outcomes.
Whether you buy their claims or not, the direction is unmistakable. Agent platforms are being built to operationalize outcomes, not chat.
The Hard Truth
If you take nothing else from this post, take this: RaaS is not a pricing model. It's a control plane.
To sell outcomes, you must reliably define the outcome, execute work toward it, verify it happened, measure cost and risk, and repeat at scale. This is why RaaS is possible now: agents can execute. It's also why RaaS is hard: outcomes require governance.
In my writing on agent architecture, I've said: you don't trust the agent, you bound it. RaaS is the same idea applied to business models. The provider doesn't just deploy an agent and hope. They deploy an agent within a control plane that ensures outcomes are defined, verified, and billable.
RaaS is what happens when "agentic" meets accountability.
What Comes Next
Part 2 goes deep on architecture. I'll show you what a "Result Contract" looks like, how the Outcome Control Loop works, and what has to be true for both providers and consumers to make RaaS real.
If you're evaluating RaaS offerings, you'll learn the questions to ask. If you're building them, you'll see the architectural patterns that separate real outcome delivery from outcome theater.
The shift from "pay for software" to "pay for results" isn't just a pricing change. It's a fundamental restructuring of how software value is created, measured, and captured.
The companies that figure this out will own the next era of enterprise software. The ones that don't will be selling seats while their competitors are selling outcomes.
Continue to Part 2: RaaS Architecture →