Should AI companies only be charging customers when the tech works? The Information recently reported that Zendesk, an AI-powered customer support software, changed its pricing model, charging businesses only when the AI chatbot successfully resolves customer issues without human intervention.
This shift reflects a broader trend among software providers like Intercom and Forethought, who are also moving away from user-based subscription models and instead adopting outcome-based pricing, charging customers only when AI features perform effectively. This model aims to appeal to businesses that are cautious with their IT spending and skeptical of high-priced AI features. Although it’s still early days and outcome-based pricing remains a small part of their business, these companies believe the industry will eventually move in this direction as AI continues to automate tasks like customer support, sales, and recruiting.
While this model is relatively new for software and AI companies, outcome-based pricing has existed for many years and has been introduced in other industries like healthcare and manufacturing. A well-known example is Siemens, which uses Energy Performance Contracts (EPCs) in building automation and energy management. In this model, Siemens installs energy-efficient systems and guarantees a specific level of energy savings for their customers. Rather than charging for the installation or the system itself, Siemens charges based on the energy savings achieved.
For instance, Siemens may guarantee a 20% reduction in energy costs for a commercial building. If that target is met, the customer pays Siemens according to the savings realized. This outcome-based model ensures that the client’s payments are directly tied to the results delivered, aligning Siemens’ revenue with their customer’s energy efficiency goals.
The Shift Toward Value-Driven Models
For more than two decades, software-as-a-service (SaaS) pricing has relied on the “pay-per-seat” model, where companies pay based on the number of users who access the software. While effective in many cases, this model is starting to show its limitations in a world where AI enables automation at scale. As companies adopt AI-driven solutions, fewer employees are needed to perform many tasks, which leads to a paradox: businesses are being charged more for software that increasingly reduces their need for human users.
Outcome-based pricing flips the script. Instead of charging for access, companies like Zendesk, Intercom, and Forethought are pioneering models where customers only pay when the AI system successfully completes a task—whether resolving a customer support ticket or closing a sales lead. This represents a fundamental shift from selling usage to selling value, where the true impact of software is tied to the results it delivers.
This alignment of cost with benefit makes pricing more transparent and fair. Customers pay for the value they derive, which in theory, could foster deeper trust between software vendors and clients, particularly in uncertain economic environments.
Case Study: Gym Pricing
In one well-known case, a fitness center adopted a creative pricing model where members were charged only if they didn’t show up for their scheduled workout sessions. This reverse model, rooted in behavioral economics, used the principle of loss aversion to encourage regular attendance. By charging members for missed sessions rather than for attending, the gym created a financial incentive for people to stick to their fitness routines. This example highlights how outcome-based models can effectively align business revenue with customer behavior and desired outcomes.
Although this may seem like a contrast to outcome-based pricing, the underlying principle is similar: the business aligns its revenue model with the customer’s behavior and outcomes. In this case, the desired outcome is the member’s attendance, which is vital for achieving fitness goals. The model still connects payment to performance and outcomes—in this case, the act of showing up—just as AI-driven outcome-based models charge customers when the software performs a specific task.
Case Study: Rolls-Royce’s Power by the Hour
Another classic example of outcome-based pricing comes from Rolls-Royce, which introduced the Power by the Hour model in the aviation industry. Instead of selling aircraft engines outright and charging separately for maintenance, Rolls-Royce charges airlines based on the number of hours the engine is operational. This shifts the maintenance responsibility from the airline to Rolls-Royce, aligning their revenue with the engine’s performance and uptime.
Airlines only pay for the hours the engine is running effectively, meaning Rolls-Royce is incentivized to keep engines in top condition to maximize their operational hours. This model has since been adopted by other aerospace companies, including GE Aviation, and has become a standard in the industry.
The Challenges: Navigating Complexity and Uncertainty
Despite its potential, outcome-based pricing also comes with a set of unique risks. Perhaps the greatest concern for vendors is revenue volatility. Unlike traditional subscription models, which provide predictable recurring income, outcome-based pricing ties revenue directly to performance. If the AI underperforms or encounters technical issues, vendors could see sharp declines in revenue. For smaller companies, this unpredictability might pose significant financial risks.
Success in outcome-based pricing depends on the ability to manage performance risk. Companies need to ensure that their AI systems can deliver consistently across varied environments. Otherwise, they expose themselves to revenue instability.
Another challenge lies in defining outcomes. While automating customer support might be relatively straightforward, what about more complex tasks like strategic decision-making or creative problem-solving? How do you measure and price success in scenarios that are inherently ambiguous? The value of resolving a simple customer query is vastly different from resolving a high-impact technical issue, and businesses may struggle to find consensus on the “right” price for different outcomes.
Looking Forward: A Hybrid Approach?
The future of enterprise software pricing may lie in hybrid models that combine the predictability of traditional subscription pricing with the flexibility of outcome-based fees. In this model, customers pay a base fee for access to AI solutions but also agree to performance-based premiums when the software delivers specific, measurable outcomes. This structure could provide both vendors and buyers with the best of both worlds: predictable revenue streams for vendors and clear ROI for customers.
Ultimately, the shift to outcome-based pricing reflects a broader transformation in how we think about technology and the value it brings. AI is no longer just a tool—it’s a partner in the workplace. And like any good partner, its worth is measured by its contributions, not just its presence.
As AI continues to revolutionize business processes, companies that can successfully align their pricing models with the value they provide will be well-positioned to lead in this new era. Outcome-based pricing may not be a universal solution, but for many, it represents the next frontier in how software is sold and consumed.
About the Author
Assaf Melochna, President and CoFounder, Aquant
Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant.
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