How AI is Transforming Software Monetization: Key Pricing Strategies for Success

Artificial Intelligence (AI) is revolutionizing the software industry, with 94% of tech companies planning to introduce new AI-driven solutions. However, innovation alone is not enough—companies must implement effective monetization strategies to generate sustainable revenue. This article explores the best approaches to AI monetization and real-world examples of their implementation.

A recent Global Software Study, which surveyed over 500 software executives, confirms the rapid expansion of AI adoption. As AI becomes mainstream, developing the right pricing strategies is essential for unlocking growth opportunities. Gartner’s Hype Cycle indicates that Generative AI (GenAI) has moved past the phase of exaggerated expectations and is heading toward widespread adoption.

At Bespoke Business Development, a key principle we follow is: “How you charge is more important than how much you charge.” This is particularly relevant for AI monetization, where usage-based and outcome-based pricing models are becoming dominant. In this article, we delve into these pricing models and how they serve as key revenue drivers for tech companies.

The Evolution of Software Pricing: From Perpetual Licenses to Usage-Based Models

Software pricing has evolved alongside shifts in customer purchasing behaviors, progressing through three primary stages:

  1. Perpetual Licensing – Traditionally, software was sold through upfront purchases with additional maintenance fees. While this model provided initial revenue, it was costly and limited long-term monetization.
  2. Subscription-Based Pricing – This model transitioned software from ownership to access, enabling predictable revenue streams, simplified budgeting, and enhanced customer retention.
  3. Usage-Based Pricing – The rise of AI, particularly GenAI, has accelerated the adoption of this model, which better aligns costs with customer-perceived value.

Companies implementing usage-based pricing must prioritize scalability and customer retention. While it introduces some revenue variability, it lowers barriers to entry and enhances adoption. Businesses can mitigate unpredictability by offering prepaid credit bundles or commitment-based models.

Usage- and Outcome-Based Pricing: The Future of AI Monetization

Traditional user-based pricing models offer simplicity, transparency, and predictable revenue growth. However, they may not be optimal for AI-driven solutions. Customers may limit the number of users to control costs, and in some cases, AI solutions reduce human labor needs, shrinking the potential monetization base.

As a result, usage-based pricing is gaining traction for Generative AI applications. While it can lead to fluctuating revenue, this model more accurately reflects customer value, aligns with AI cost structures, and provides greater flexibility.

Tech companies can adopt one of two approaches when implementing a usage-based model:

  • Cost-Centric Pricing – Charges are based on resource consumption (e.g., compute power, storage). Metrics such as API calls, processed data, or messages sent serve as pricing indicators.
  • Value-Centric Pricing – Pricing is based on measurable success, such as resolved queries or revenue impact. Companies like Zendesk and Chargeflow utilize this model.

Various forms of usage-based pricing exist, from fully flexible pay-as-you-go models to structured usage-based subscriptions that provide greater revenue predictability. Selecting the right model requires careful consideration of scalability and business objectives.

Outcome-based pricing is an advanced variation of usage-based pricing, where charges are directly tied to delivered value. However, accurately defining and tracking outcomes demands robust data analytics and close collaboration between service providers and customers.

Case Study: Fin’s Outcome-Based Pricing in Action

Intercom’s GenAI-powered customer service agent, Fin, integrates seamlessly with company platforms to automate customer support via emails, live chat, SMS, and social media. As it resolves more queries, Fin continuously improves its efficiency and effectiveness.

Customers pay based on successfully resolved support tickets. For example, in August, Fin saved customers approximately 142 hours, charging $1 per resolved issue compared to $10 per resolution with a human agent. As more customers adopt Fin, Intercom benefits from higher revenue and broader market penetration—17% of its customers have already opted for outcome-based pricing.

Selecting the Right Pricing Model for GenAI: Key Considerations

Choosing an effective monetization model for GenAI depends on several factors:

  • Does the AI solution replace human labor?
  • Can the impact of the AI solution be clearly measured?

The monetization strategy matrix helps visualize these factors, positioning AI solutions based on their effect on human labor and the clarity of their value proposition.

For example, Salesforce’s AI tool Einstein enhances user decision-making, but its value is difficult to quantify, making user-based pricing more viable. In contrast, Agentforce, another Salesforce AI solution, fully replaces human support agents, making a per-conversation pricing model more appropriate.

Disruptors like Intercom’s Fin offer an even clearer case for outcome-based pricing, as customers only pay for successfully resolved issues. This approach ensures customers derive tangible value, making it an attractive and scalable model.

For established companies, a gradual transition approach is ideal—starting with user-based pricing and progressively shifting toward outcome-based models as AI solutions mature and deliver more measurable value.

Next Steps for AI Monetization

Selecting the right pricing model involves more than just defining charges. Companies must also consider:

  • Billing infrastructure to support dynamic pricing models
  • Financial forecasting tools to monitor revenue streams
  • Marketing strategies that highlight the value of AI solutions
  • Clear customer communication to explain pricing benefits and expected outcomes

Successful AI-driven companies focus on conveying the results of their solutions rather than the underlying technology. By adopting the right pricing strategy, tech companies can unlock sustainable revenue streams and drive long-term growth in the evolving AI landscape.

The views and opinions in these articles are solely of the authors and do not necessarily reflect those of Bespoke Business Development. They are offered to stimulate thought and discussion and not as legal, financial, accounting, tax or other professional advice or counsel.