Artificial Intelligence (AI) is reshaping the software industry, with 94 percent of tech companies set to launch new AI solutions. To succeed, tech companies must not only innovate but also implement monetization strategies that drive solid earnings from their AI offerings. Read on to discover the best monetization strategies and real-world use cases.
Our latest Global Software Study with over 500 software executives shows that 94 percent of tech companies are planning to launch new AI solutions. As AI adoption grows, tech companies must prioritize developing the right monetization strategies to unlock new opportunities. According to Gartner’s Hype Cycle, GenAI has passed the peak of “inflated expectations” and is reaching mainstream adoption in the next years.
One of our common sayings at Simon-Kucher is: “How you charge is more important than how much you charge”. This is particularly relevant for AI monetization, where usage-based and outcome-based pricing models are on the rise.
In this article, we focus on optimizing these pricing models as they are the primary drivers of AI monetization for tech companies.
Evolution of software pricing: From perpetual to usage-based pricing
Software pricing models mirror broader shifts in customer buying preferences, evolving through three distinct stages:
Perpetual ownership: Once the dominant model, it required upfront payments plus maintenance fees but had excessive costs and limited value monetization.
Subscription models: This shifted focus to access over ownership, enabling predictable revenue, easier budgeting, and stronger customer retention and acquisition.
Usage-based pricing: AI, especially Generative AI (GenAI), has accelerated its rise. This model aligns more closely with customer perceived value.
Companies adopting the usage-based pricing approach must focus on scalability and customer retention. While it introduces some revenue unpredictability for customers, it lowers entry barriers and boosts adoption. Unpredictability can be overcome through prepaid credit bundles or commitment models.
Usage- and outcome-based pricing: The next frontier in AI monetization
User-based pricing models are simple, transparent, and provide predictable revenue growth. Yet, they can have limitations when applied to AI solutions. For instance, customers may restrict the number of users to control costs, and it can be difficult to differentiate between truly "active" and "less active" users in terms of value delivery. Or worse, new AI solutions may replace human labor, reducing the number of users (and monetization basis for user-based pricing).
Therefore, usage-based pricing is fast gaining traction for Generative AI applications. Although it may result in less predictable revenue streams, usage-based pricing better reflects customer value, aligns with AI cost structures, and offers customers more flexibility.
When implementing a usage-based pricing model, companies can choose between cost-centric or value-centric approaches:
Cost-centric models, such as those used by providers of hosting infrastructure, are based on resource consumption (e.g., compute time, storage). Activity-based metrics capture the derivative of resource use, like tokens or emails sent.
Value-driven pricing prioritizes success, such as resolved queries or revenue impact. Platforms like Zendesk and Chargeflow use this approach.
There are many different forms of usage-based pricing models from fully flexible pay-as-you-go structures to usage-based subscriptions that offer much more revenue predictability. Choosing the right model that is scalable and aligns with your company’s goals requires careful consideration.
Outcome-based pricing models can be considered an advanced form of usage-based pricing, which directly reflects value delivered to customers. However, defining, tracking, and forecasting outcomes requires precise data and close collaboration between providers and clients.
Fin case study: Outcome-based pricing in action
Fin, Intercom’s GenAI-powered customer service agent, integrates with company platforms to provide AI-first customer service across emails, live chat, SMS, and social channels. The more issues Fin resolves, the smarter it becomes—continuously improving service quality and outcomes for customers.
Customers pay based on successful support ticket resolutions. Fin’s growing success reflects increased customer value (~142 hours saved in August at $1 per resolution versus $10 per resolution with a human agent). As the service scales, Intercom sees both higher revenue and increased customer adoption, with 17 percent of customers opting for outcome-based pricing.
How to select the right pricing model for GenAI: Human impact vs. tangible value delivery
Choosing the right monetization model for your GenAI solution depends on several factors.
If your GenAI solution replaces human work, charging per user may be counterproductive. Additionally, the value your solution delivers could either be tangible (e.g., problems resolved) or more abstract, influencing your pricing approach. Before deciding on a monetization strategy, ask:
Does my AI solution replace human labor?
Can the impact of my AI solution be clearly measured?
This dilemma —can be visualized in a matrix of monetization metrics based on human impact versus tangible value delivery.
First, consider the incumbent Salesforce. Their AI tool Einstein enhances user decision-making, but its value is not easily measurable. Agentforce, their other AI solution, is different—it does not enhance but replaces the work of human support agents, making a per-conversation price metric more suitable.
Then, you have the disrupter Intercom, the company behind Fin. This tool not only replaces human labor but also does that in a way that makes the value more easily measurable. You only pay if you are happy with the proposed resolution to the customer’s question as suggested by Fin.
For incumbents, a "crawl-walk-run" approach works best. They may start with a user-based metric for their current software and gradually shift toward an outcome-based metric as AI features drive more automation and tangible value delivery. Companies may move from a user-based metric (the lower left quadrant) to a usage-based or hybrid model, eventually adopting outcome-based metrics to best reflect customer value.
Next steps for AI monetization
Finding the pricing model that is right for you also requires you to think about billing systems, financial infrastructure (e.g. forecasting and monitoring), marketing materials, and value propositions. Good customer communication is especially vital. Successful AI disruptors communicate the value of their offerings by explaining the outcomes of their tools, rather than the technology that drives it.
Whatever you decide, remember: How you charge is more important than how much you charge.
Ready to embark on your AI monetization journey? Reach out to us today!
The authors wish to thank Amber van Ginkel (Senior Consultant) for her contribution to this article.