Agentic AI Economics: Business Models and Pricing Strategies

Uncover the evolving landscape of agentic AI and its impact on traditional software business models. In this course, delve into the shift from software-as-a-service to service-as-software paradigms, and explore innovative pricing models like task-based and outcome-based frameworks. Learn how to identify the factors influencing AI economics, such as autonomy and nondeterminism, and hidden runtime costs, which challenge conventional unit economics. By examining real-world case studies, this course gives you an opportunity to learn how companies like Intercom and ChatGPT are testing pricing strategies in practice. Tailored for nontechnical business professionals and decision-makers eager to bridge the gap between AI technology and financial strategy, by the end of this course, you’ll be equipped with the skills you need to evaluate and apply agentic AI pricing models—whether you’re building or buying them.

Learning objectives
Describe the shift from traditional software-as-a-service models to agentic service-as-software models.
Compare and evaluate various pricing models for AI agents, including outcome-based and usage-based models.
Identify the limitations of flat-rate pricing in agentic AI systems with unpredictable usage patterns.
Recognize how AI agents' dynamic decision-making can lead to unpredictable execution paths and inflate costs significantly.
Examine hybrid revenue models for AI businesses and identify how they provide flexibility and scalability in high-usage environments.