The AI Pricing Shift

Artificial intelligence is forcing software companies to rethink nearly every aspect of their business. Product development cycles are accelerating, cost structures are changing, and traditional SaaS pricing models are being challenged.

One company at the center of this transformation is Fin, the AI customer service platform formerly known as Intercom.

In a recent episode of the LogiSense podcast, Tim Neil sat down with Nakul Sharma, Pricing Product Manager at Fin, to discuss how the company approached one of the most difficult questions facing AI businesses today:

How do you price AI in a way that aligns cost, value, and customer trust?

From Seats to Outcomes

For many years, SaaS companies relied heavily on seat-based pricing. The model was simple, predictable, and easy for customers to understand.

AI changes that equation.

As AI agents become capable of handling work traditionally performed by humans, charging per seat becomes increasingly disconnected from the value being delivered. If AI can resolve customer issues without human intervention, why should customers continue paying based on the number of support agents they employ?

Fin's answer was to move toward an outcome-based pricing model.

Rather than charging customers for conversations or AI interactions, Fin charges only when a customer issue is successfully resolved. This creates a direct connection between the value delivered and the price paid.

The Challenge of Defining an Outcome

While outcome-based pricing sounds simple in theory, implementing it is far more complex.

• How do you determine whether an AI interaction actually solved a customer's problem?

• How do customers verify those outcomes?

• How do you create trust in a model where pricing is tied to performance?

During the discussion, Nakul shares how Fin approaches these questions through transparent reporting, customer auditability, and clear definitions of successful resolutions.

These operational details are often overlooked when discussing AI pricing, but they are critical to making outcome-based models work at scale.

Balancing Predictability and Fairness

One of the biggest concerns surrounding usage-based and outcome-based pricing is budget predictability.

Finance teams need confidence in future spend. At the same time, customers don't want to commit to usage levels they cannot accurately forecast months in advance.

The podcast explores how Fin balances these competing priorities through annual drawdowns, rollover allowances, flexible overage structures, and contract mechanisms that help customers adapt as AI adoption grows.

For organizations considering usage-based or outcome-based pricing models, these lessons are particularly valuable.

Why Token-Based Pricing May Not Be the Future

Many AI providers today price based on tokens, model consumption, or infrastructure costs.

Nakul offers a different perspective.

While token-based pricing may make sense at the foundation model layer, he believes business buyers ultimately want pricing tied to business value, not technical consumption metrics.

As AI matures and infrastructure costs continue to evolve, outcome-based and value-based pricing models may become increasingly attractive for enterprise software providers seeking stronger alignment with customer success.

Listen to the Full Conversation

The transition from SaaS pricing to AI monetization is creating new challenges for product leaders, finance teams, and pricing professionals.

In this episode, Nakul Sharma shares practical lessons from Fin's journey, including:

• Why seat-based pricing no longer fits many AI products
• How outcome-based pricing was designed and implemented
• Managing predictability and fairness in AI monetization
• The impact of AI costs on pricing strategy
• Why token-based pricing may not survive at the application layer
• Where AI pricing models are headed next

Listen to the full podcast to hear the complete discussion and gain firsthand insights from one of the leaders shaping the future of AI pricing.