Unlocking scalable AI revenue: Challenges and strategies for 2026

Amidst unprecedented energy, funding, and innovation in AI, companies are facing significant challenges in monetizing AI products compared to traditional SaaS models, as highlighted by Orb CEO Alvaro Morales at Web Summit 2025. Unlike SaaS, which benefits from high fixed costs and low marginal costs, AI incurs real marginal costs with each query, complicating pricing strategies. Usage-based pricing, which aligns revenue with costs, and hybrid pricing models, combining subscription fees with usage-based components, are emerging as solutions, though they come with their own challenges of unpredictability and customer dissatisfaction. Additionally, outcome-based pricing, where customers pay based on results, is gaining interest but is complex to implement due to the need for shared definitions of outcomes and real-time measurement. The ability to experiment with pricing quickly is becoming a competitive advantage, as companies like Replit lead with innovative approaches like effort-based pricing, where costs align with the complexity and perceived value of AI requests. As AI products mature, companies that treat pricing as a dynamic and integral part of their strategy, backed by the right infrastructure for rapid experimentation and data-driven decisions, will likely gain an edge in the evolving landscape of AI monetization by 2026.