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Building a Pricing and Monetization Strategy for Agentic AI
Agentic AI can represent a transformative revenue opportunity for enterprise software companies. That is, for the enterprise software companies that figure out how to monetize it.
Achieving this upside is a challenging, iterative process, and even the most advanced companies are still working through it. But six months out from the supposed “SaaSpocalypse,” we believe that those who get this right are positioned for significant growth.
We’re seeing it firsthand across Vista’s portfolio, where many companies are transitioning to new pricing models as they bring agentic capabilities to market.
Turning a new AI product into meaningful revenue takes time. In our experience, companies should expect at least six months — and often twelve or more — of testing, learning and refinement before achieving sustained value capture.
We’ve distilled that process into four key steps, which are being implemented across our portfolio today.
Step One: Validate Before You Build
Every AI monetization strategy should begin with research. The goal is to understand whether customers value the proposed capability, how it creates value for them and whether they are willing to pay for it.
Start with customer interviews and advisory board sessions. Demo use cases for the GenAI evolution of your software product, and ask questions like:
- How would you describe the value this product creates for your company?
- How much better is this product than its non-Agentic version and the next-best alternative?
- What price would you consider acceptable? Expensive? Too expensive?
Companies should come away with segment-specific demand information, an initial view of willingness to pay and a clear understanding of the product’s competitive differentiation. Even 10 to 15 customer conversations can provide a strong foundation for the decisions to follow.
Step Two: Launch, Learn and Iterate
Step two is an intentional pilot. This is not the moment to perfect the monetization model. It’s the moment to drive early adoption and start collecting evidence.
At the customer level, that means three things:
- Keep initial pricing simple. At this stage, the priority is adoption. You want customers using the product so you can learn from them.
- Test value-aligned price metrics with customers. This both gathers valuable information and gives customers time to understand where the model is headed and why.
- Include time, usage or feature limits to protect future pricing potential. Be transparent that pricing and packaging may change so you do not box yourself into a beta model.
Behind the scenes, companies should use the pilot period to build the capabilities required for the next phase. That includes tracking product usage, identifying which behaviors correlate with customer value and giving customers visibility into their consumption. Where future pricing may depend on usage, companies should also begin assessing the telemetry, billing and commercial infrastructure required to support it.
At the same time, Sales and Customer Success should begin learning how customers respond to the emerging value proposition and pricing model. Those insights should feed back into both product and monetization decisions before a broader release.
Ultimately, the objective is a bi-directional feedback loop: customers tell you what they value; product data shows you what they actually use. Companies should leave this phase with a clearer view of who values the product, how they use it, what drives that value and which monetization approaches are most likely to work.
Step Three: Differentiate Packaging and Pricing
With data and feedback behind you, you’re ready for wider release. Your focus should be on capturing differentiated value across your customer base.
Start with packaging. Different customer segments will use different features, at different levels, and derive different amounts of value from them. Companies should identify those differences and build packages that reflect them, rather than bundling every AI capability into a single upsell.
Then, refine the pricing model. Many companies will combine historical price meters, such as users or licenses, with new variable meters tied to usage, work performed or outcomes delivered. Others may move further toward consumption- or outcome-based models. The goal is to ensure that your ability to monetize value reflects the different ways customers consume it.
Just remember: how you charge is more important than how much you charge. For more on this, read our work on evaluating and evolving your current price metric.
Step Four: Transformative Monetization
This is the point at which the product, customer value proposition and monetization model are mature enough to support full, sustained value capture.
Use the data gathered through earlier stages to refine the pricing model based on actual customer behavior, willingness to pay and value realization. This may mean adjusting the price metric, scaling logic, packaging, price levels or commercial policies to better reflect the value the product delivers.
Then operationalize the model across the customer base. Update Sales and Customer Success enablement, strengthen value articulation and migrate remaining customers to the target-state pricing approach.
From there, the work becomes continuous. Maintain a feedback loop with customers and closely monitor KPIs to ensure pricing remains aligned with value and supports healthy year-over-year expansion. As the product evolves and delivers more value, the monetization model should evolve with it, whether through organic usage growth, packaging changes or deliberate price increases.
The Work Is Worth It
We’ve boiled this process down to its essential elements here, but our intention isn’t to gloss over the dozens of processes and months of work behind a successful monetization strategy. We know this is a daunting transition for any company.
What keeps us motivated is the success we’ve already seen across the Vista portfolio. Done right, AI monetization represents a genuine inflection in both the value enterprise software can deliver and the market opportunity it can address.
A Guide to Rethinking Software Pricing for the Agentic AI Era
