In the early days of enterprise software, pricing was based on access. You bought licenses, whether for a desktop application from a store like CompUSA in the 1990s or a SaaS seat from a cloud provider in the 2010s, and you paid whether you used the tool or not. The result? Buying software meant making significant upfront investments in complex, installed solutions. Companies purchased expensive licenses, often with hefty maintenance or upgrade fees.

Then came the cloud. Companies like Salesforce pioneered SaaS, and infrastructure players like AWS and Twilio introduced consumption-based pricing. You paid only for compute or bandwidth used, and you got consistent software updates and flexibility without large upfront costs. Yet, despite these improvements, companies still faced challenges with "shelfware," paying annually for licenses or modules that remained underutilized. A fixed fee unlocked features, users, or usage tiers regardless of whether those tools delivered results. In high-throughput operational environments, this model created a persistent gap between software cost and business value. 

For decades, enterprise software was priced by access, not by impact. But today autonomous AI agents powering supply chain, logistics, transportation, and field service operations make an even more innovative pricing model possible: outcome-based pricing. Unlike traditional software, AI agents don’t just accelerate tasks, they assume full workflows. They triage customer emails, answer calls, reconcile invoices, update shipment records, and extract structured data from unstructured documents. These agents behave less like tools and more like digital workers. Pricing them like static software may miss the point.

To put it simply, the nature of agentic AI enables you to pay only when a meaningful business outcome is delivered.

From Inputs and Interfaces to Measurable Output

The limitations of seat-based or SaaS pricing are well understood. They charge for presence, not performance. In practice, that means companies pay whether a tool performs well or not. Typical examples include your CRM, applicant tracking systems, and in most cases your ERP. 

Consumption based pricing on the other hand, like infrastructure-as-a-service, can be variable based on the service, and while it provides better value than SaaS based pricing, it relies on the users to be valuable.

In contrast, outcome-based pricing aligns AI agent incentives with your operational outcomes. Instead of charging per login or per API call, agents are priced by what they produce: orders booked, calls completed successfully, resolved conversations, documents submitted, hours saved, revenue accelerated.

This structure does two things:

• It creates transparency - Each completed task is measurable.
• It creates accountability - You don’t pay until the agent delivers.

Why AI Agents Make This Possible

Traditional software rarely lends itself to outcome-based pricing because it depends on human users. It’s hard to isolate the tool’s contribution from the operator’s effort. But AI agents operate autonomously. Once deployed, they extract fields, enter data, trigger workflows, and resolve exceptions, often without a human in the loop. Agents don’t just assist, they take ownership of tasks. They extract details from documents, file them into systems, reconcile financials, and route information to the right place. They’re not tools in the traditional sense. They behave more like digital teammates.

So we started asking: if AI agents are completing the work, shouldn’t pricing reflect that? What if cost were directly tied to what they produced? 

This led us to outcome-based pricing, a model where customers only pay when the agent accomplishes a meaningful task. A quote sent, a call answered, or a field extracted with precision.

That autonomy makes outcomes attributable. You can measure precision at the field level. You can benchmark time saved against a historical baseline. You can tie throughput to business KPIs like increased revenue, reduced rework, faster processing, or lower costs per outcome.

This level of granularity turns AI from a fixed IT cost into a variable cost of operations, more like a subcontractor than a license.

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Navigating the Nuance of Outcome-Based Pricing

While the appeal of outcome-based pricing is clear, pay for what works, not just what’s offered, it also raises fair and important questions. What qualifies as an outcome? How predictable is the cost? What happens when things don’t go as planned?

These concerns aren’t theoretical, they’re practical. No one wants to be surprised by an unexpected invoice or spend time debating whether a task counts as “complete.” And not every workflow fits neatly into a single pricing framework.

We take those realities seriously. That’s why we work closely with each partner to define what an outcome means in their specific context. Some are straightforward, a shipment entry logged correctly, a quote booked, a call successfully answered. Others are more nuanced, like resolving a customer inquiry that would normally escalate to a second-line support call. In those cases, we ensure clarity upfront and, when escalation is required, there’s typically no charge.

We also understand that outcome-based pricing isn’t always the right model across the board. Some interactions, like routing, initial triage, or status updates, may be better served by a simpler, usage-based structure. That’s why we offer blended pricing models, so the approach fits the task, not the other way around.

Ultimately, the goal is alignment. Our pricing should reflect not just what our agents do, but how your business defines success.

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Designing for Outcomes, Not Just Automation

Designing AI systems around outcomes, not just automation, requires more than smart workflows; it demands transparency, traceability, and trust. At Boon, we build agents that not only complete tasks but also report exactly what was done, how accurately, and where exceptions occurred. This level of visibility allows operators to tune performance directly, without relying on engineering cycles. In that sense, pricing becomes a forcing function: if we’re tying cost to performance, we need to prove the work. And that sets a higher bar for what qualifies as enterprise-ready AI. It’s no longer enough for software to be functional, it has to be accountable. 

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A More Durable Commercial Model

This approach creates a more durable, scalable model for AI in operations. Outcome-based pricing doesn’t mean surprise invoices or vague performance guarantees, it means tying costs to tangible, repeatable business results that are forecastable, measurable, and defensible. Unlike legacy vendors whose business models rely on seat-based revenue, even when AI reduces seat demand, we have no such conflict. Our incentives are fully aligned with yours: we only succeed when our agents deliver real impact. For AI to move from experimental to essential, it must speak the language of outcomes, and earn its place not as a sunk cost, but as a working part of your operation.

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