AI is pushing software companies toward more variable monetization models, including credits, drawdowns, top-ups, and hybrids. New research from Michael Mansard, Principal Director, Subscription Strategy at Zuora, explores when credit-based monetization makes sense, where it creates risk, and what leaders need to operationalize these models successfully.
How to assess and design AI credit monetization for your business
AI is introducing a new kind of margin risk: not headcount, but compute. As usage and model complexity grow, guardrails around consumption and pricing become critical to protect margins. For some companies, credits help manage variable usage, unpredictable cost-to-serve, and customer flexibility. For others, they add complexity without solving the right problem.
In his leading-edge research, Michael helps revenue and finance leaders move past the trend cycle and think more clearly about when credits fit, how these models should be designed, and what it takes to make them work operationally.
Download the full report to get:
- A framework for deciding when credits are the right fit
- A deeper look at how credit-based models should be designed
- Examples of how leading vendors are approaching AI credit monetization
- Guidance for finance and revenue teams responsible for operationalizing these models
- Practical insights into the tradeoffs between flexibility, predictability, and trust
Key findings
This research highlights a larger shift in software monetization and the new pressures it creates for finance, revenue, and product teams.
- AI is accelerating the move toward more variable monetization models. Credits, drawdowns, top-ups, and hybrid structures are becoming more common as companies respond to usage volatility, margin pressure, and new AI delivery patterns.
- Credits are not a default best practice. They are a design choice that only works well under specific conditions. In the wrong context, they can create friction for customers and operational burden for internal teams.
- The challenge does not end with pricing. The real complexity shows up downstream in usage tracking, billing logic, invoicing, collections, forecasting, and revenue recognition.
- Trust is becoming a core monetization requirement. The strongest models help customers understand balances, burn rates, rollover rules, and top-up behavior clearly. Opaque designs may protect margin temporarily, but often weaken trust.
- Finance needs to be involved earlier. Credit design affects more than packaging. It shapes how revenue is recognized, how breakage is managed, how renewals are handled, and how predictable the business becomes.
Next steps for finance and revenue leaders
- Download the full report to read the complete research and findings
- Assess whether your offer truly needs credits before introducing a new abstraction layer.
- Design for clarity, predictability, and customer confidence, not just margin protection.
- Treat AI monetization as a revenue lifecycle challenge, not just a pricing decision.
- Bring product, pricing, finance, and revenue operations together earlier in the design process.
