Pricing AI: What CFOs and FP&A Leaders Need to Know

As artificial intelligence becomes a core component of software strategy, many SaaS companies are charging ahead with AI-driven features, but fewer have a clear plan for how to price them.

In his recent global webinar, “The Transition from SaaS to AI Pricing,” Marcos Rivera (Founder of Pricing I/O and one of the leading experts in software pricing strategy) offered a compelling and pragmatic take on how pricing needs to evolve in the AI era.

You can watch the full webinar here:
🎥 Watch: The Transition from SaaS to AI Pricing

For CFOs, FP&A leaders, and PE operators, this isn’t just a product conversation, it’s a strategic one. Below, we summarize the key themes and implications for finance and investment leaders managing SaaS and AI-powered businesses.

1. AI Is Not a Pricing Strategy, Value Is

Many companies are launching AI features with the hope of commanding a premium. But Rivera’s advice is clear: “AI” doesn’t automatically mean “more revenue.”

Customers need to see and feel the value, not just hear about the machine learning happening behind the curtain. As a result, finance and pricing leaders need to work closely with product and GTM teams to ensure AI capabilities are:

• Clearly differentiated from the core offering

• Mapped to a tangible business outcome

• Justifiable in terms of margin, cost-to-serve, and value delivered

2. Usage-Based Pricing: Powerful, But Problematic

AI features often lend themselves to usage-based models, charging per query, token, transaction, or minute of processing.

The challenge? Predictability. Usage-based pricing introduces variability that makes forecasting harder and revenue less stable. For FP&A teams, that means:

• A greater need for sophisticated scenario modeling

• Improved sensitivity analysis across demand curves

• Closer integration with RevOps and GTM teams to model expected usage patterns

This isn’t just about modeling volatility, it’s about managing investor expectations in a world where ARR may start behaving like variable revenue.

3. Packaging > Pricing

One of the most compelling takeaways from Rivera’s session was this: how you package AI features may matter more than how you price them.

Poor packaging can lead to buyer confusion, limited adoption, or worse, a sense that customers are being nickel-and-dimed for features they don’t understand.

Rivera suggests bundling AI into existing tiers in a way that enhances perceived value without cannibalizing your core offering or adding complexity to sales motions. CFOs and product leaders need to align early on:

• What gets included by default?

• What should be an upsell?

• How do we protect gross margin while still accelerating adoption?

4. The Investor Lens: Quality of Revenue Matters More Than Ever

For PE firms and CFOs in high-growth environments, one of the subtler risks in AI monetization is revenue quality.

It’s easy to mistake “AI revenue” for strategic progress. But if that revenue is usage-spiky, poorly retained, or coming from customers with limited understanding of what they’re buying, it’s not durable, and your next diligence cycle will surface that. AI features must pass the same tests as traditional SaaS metrics: Retention, Expansion, Attach rates, Predictability

Final Thought: FP&A Should Be at the Table Early

Perhaps the most important message for finance leaders is this: AI pricing can’t be an afterthought.

This is not a one-time SKU adjustment. It’s a cross-functional effort that will impact:

• Forecasting accuracy

• Customer LTV

• GTM velocity

• Investor perception

• And ultimately, valuation

We’re no longer in the era of annual price reviews. As AI accelerates, so too must our ability to model, test, and adapt our pricing strategy.

If you're planning 2025 budgets, navigating an AI roadmap, or preparing for your next board meeting, take the time to watch Marcos Rivera’s session. It’s 45 minutes well spent:

🎥 Watch Now: The Transition from SaaS to AI Pricing

About Thamesbridge Consulting
We help FP&A teams, CFOs, and PE-backed businesses turn complexity into clarity—through better models, sharper insights, and smarter planning. If you’re navigating SaaS, AI, or both, we’re here to help.