Artificial intelligence is unlocking one of the greatest value shifts in modern software history, yet many companies are losing pricing power before they ever reach production. At the Usage Economy Summit 2025, Marcos Rivera, CEO of Pricing I/O, delivered a practical and candid session on how to avoid this mistake. Drawing from more than 500 pricing and packaging projects across leading B2B SaaS companies, he outlined clear frameworks that help teams capture value confidently while navigating the messy economic realities of AI.
Marcos opened by highlighting a simple truth. Pricing is still pricing. The objective remains capturing value in a fair exchange. What has changed are the mechanics that surround value. The industry has moved from selling access in the on prem era, to selling activity in the cloud era, to selling action in the AI era. Systems now perform work on behalf of users, which increases the percentage of economic value that vendors can capture. The opportunity is larger, but the path is far more complex.
Unit economics in AI do not behave like traditional SaaS. Recurring revenue is blended with reoccurring usage. Services now operate as high margin premium layers. Gross margins swing dramatically based on model choice, frequency, and task type. Even more challenging, most AI pilots never reach paid production. The outcome is clear. Companies must approach AI monetization with intention, discipline, and a willingness to experiment with structure while avoiding chaos.
Many companies default to a narrow time savings narrative when articulating AI value. Marcos warns that this weakens pricing power immediately. Buyers do not pay for AI. They pay for meaningful business outcomes, and studies show that the AI label does not increase willingness to pay or trust on its own.
To strengthen value articulation, Marcos introduced the REAL framework.
Revenue: Show how AI helps customers win, grow, or keep more business.
Expense: Move beyond hours saved. Demonstrate cost advantages and competitive speed.
Avoidance: Highlight risks or future expenses the customer avoids by adopting your solution.
Lift: Subtract the friction required to adopt and reach time to value.
This framework forces teams to quantify a complete value story. It also reinforces a key point. If your implementation time drops from months to weeks, your pricing power has improved. Revisit it.
Packaging AI capabilities is where companies often fall into confusion. Some teams bundle AI at no charge. Others create add ons without rationale. Many simply imitate competitors and hope it works. Marcos argues that packaging must follow the experience, not the hype.
Underscore the core: If AI makes the core product faster or smarter, include it in base plans and adjust prices accordingly. Slack adopted this path by folding AI into its Business Plus plan and raising the price by twenty five percent.
Upgrade the more: If AI solves a more advanced version of an existing job, offer it as a premium tier or paid add on. ClickUp uses this structure successfully with its AI Brain offering.
Unlock the new: If AI creates an entirely new product or TAM, package it as a standalone capability. Intercom’s Finn agent is a prime example.
The lesson is straightforward. AI should be packaged where the customer experiences the value, not where it is most convenient for the vendor.
AI introduces a fundamental shift in how work is performed. It is no longer only humans driving value. Systems perform tasks independently, which makes usage and outcomes natural pricing candidates. Marcos believes hybrids will dominate this transition.
Every hybrid model contains three layers.
Base: The platform or foundational service fee.
Allowance: Entitlements, thresholds, or credits that create predictability.
Meter: The unit of value to charge for. This must align to customer value and protected cost.
From these layers, five common models emerge, ranging from simple access tiers to flat plus usage combinations, all the way to credit based systems and pure pay as you go. Marcos cautions that the meter is where the most risk lives. Choose the wrong metric and you distort adoption. Choose the right metric and you unlock scalable value capture.
As usage increases, customers become more anxious about variability. Marcos presented a simple mental model called the 3P framework that reduces fear and promotes adoption.
Predict: Provide real time visibility into usage and burn.
Prevent: Align usage periods with reset periods so spending patterns feel intuitive.
Protect: Use caps, guardrails, and true forward adjustments to avoid surprising bills.
Psychological safety is essential. If customers do not trust the model, they will not scale the product.
Marcos closed by urging leaders to sit with their teams for thirty minutes and test each framework against their own product. Determine where AI fits in your customer experience, identify the right hybrid model, and validate whether your value story is complete. The fundamental principle is simple. You should be paid more as your product does more work. Customers should feel safe adopting and scaling that work over time.
AI has created a rare moment in pricing history. The opportunity to capture value is significantly higher than previous eras. The companies that win will be those that price the experience, quantify the real outcome, and meter the right value with clarity and confidence.
To dive deeper into how companies are monetizing AI today, make sure to watch Marco’s full recording of his keynote presentation.