For decades, enterprise software pricing was built around a familiar unit: the human being.
Count the employees, identify the users, negotiate the per-seat price and then spend the next three years discovering that 17 percent of those users never logged in. It was not perfect, but at least everyone generally understood what was being purchased.
AI agents are beginning to dismantle that model.
Unlike a traditional user, an AI agent may complete thousands of actions, interact with multiple systems and operate continuously without taking lunch, vacation or an inexplicable three-hour block marked “focus time.” Vendors are now trying to determine how to monetize that activity, and the emerging answer is increasingly based on consumption rather than headcount.
Salesforce, for example, offers Agentforce pricing through conversations and Flex Credits. Its Flex Credit model charges based on specific actions performed by an agent, positioning the model as a way to align pricing with the work completed. Microsoft’s Copilot Studio uses Copilot Credits, with different agent actions and responses consuming varying numbers of credits.
Oracle has taken a different approach in parts of its portfolio, making its AI Agent Studio available to Fusion Applications customers at no additional cost. That may encourage adoption, but it also demonstrates how inconsistent the emerging market remains. One supplier charges by action, another by credits, another may embed the capability into an existing subscription, and all will explain that their model is the simplest.
Per-user pricing is relatively predictable. Consumption-based AI pricing is not.
The cost may depend on how often an agent runs, how many systems it accesses, how many steps it takes to complete a task and whether the vendor changes the credit value assigned to those steps.
A small pilot can therefore look inexpensive while enterprise deployment creates a very different cost profile.
The purchasing challenge is not simply estimating current usage. CIOs and CPOs must forecast how quickly business teams will create new agents, how widely those agents will be deployed and how usage will change as the technology improves.
That is difficult when many organizations are still determining what their agents should actually do.
AI agents are frequently justified through productivity gains. An agent may resolve service requests, update records, qualify opportunities or complete administrative tasks more quickly than a person.
Those benefits may be real. However, a productivity claim is not the same as a financial return.
If an agent saves employees five minutes per task, does the company reduce labor expense, increase output or simply create five more minutes for meetings? The financial value depends on the outcome, not just the activity.
This makes outcome-based pricing attractive in theory and complicated in practice. Buyers must understand who defines a successful outcome, whether failed or repeated actions are billable and how the vendor measures value when an agent participates in only part of a larger business process.
Organizations should not approach AI agent agreements as ordinary software subscriptions with a new product name.
Contracts should address:
The objective is not to avoid consumption pricing. In many situations, paying for productive activity may be more logical than buying thousands of licenses “just in case.”
The risk is committing to an economic model before the organization understands its usage.
The seat may not disappear entirely, but it is losing its position as the default unit of enterprise software. In its place, buyers are being offered credits, actions, conversations and outcomes.
That may ultimately produce better alignment between price and value. For now, it also produces something enterprise technology vendors have always been remarkably good at creating: a new billing unit that requires a spreadsheet, a glossary and several calls with the account team.
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