From NYSE: Why pricing Is becoming a competitive advantage in the AI era

For most of the history of software, pricing was a configuration step.

You picked a model, negotiated contracts, and then spent the next year reconciling what actually happened. Revenue systems were built for that world. Stable usage. Predictable costs. Infrequent change.

That world is ending.

What we are seeing across AI-driven companies is that pricing is no longer something you “set.” It is something you operate. Daily. Sometimes continuously.

The reason is simple: AI introduces variability everywhere. Costs fluctuate based on inference volume and model choice. Usage changes as workflows automate. Margins move in real time. And increasingly, value is delivered through outcomes, not access.

This is especially visible with AI agents. When software moves from assisting users to doing work on their behalf, revenue can no longer be anchored to seats or static contracts. It has to reflect usage, outcomes, and changing economics.

Many teams start by focusing on billing and metering. Those are necessary foundations, but they are not where differentiation happens.

What sets leading companies apart is how quickly and safely they can evolve monetization alongside product innovation. How fast they can test pricing changes. How confidently they can align product decisions with revenue outcomes. How well they can bring product, engineering, finance, and go-to-market teams onto the same page.

In other words, pricing itself becomes a competitive advantage.

This requires a shift in how revenue systems are designed.

In a contract-led world, contracts were the source of truth. In an AI-driven world, usage is. That inversion changes everything. Pricing logic cannot be hard-coded. Revenue cannot be reconciled after the fact. Systems need to preserve detail, allow reinterpretation, and support iteration without breaking downstream workflows.

This is why we increasingly see teams treating monetization as a system rather than a setup step. Pricing is defined, simulated, executed, analyzed, and refined continuously. Revenue becomes something that is designed and operated, not just billed.

Looking ahead, I believe 2026 will be a turning point.

After years of experimentation, enterprises will focus less on whether AI is possible and more on whether it delivers real outcomes and ROI. The companies that win will be the ones that can adapt their business models as fast as their products evolve.

Revenue will no longer be a lagging indicator. It will be a design surface.

That is the shift we are building toward, and the one we see accelerating across the market.