Pricing your product in the agentic era

​Software has worked on a simple “users x monthly rate” pricing model for decades. But what’s the best pricing model for a world where AI agents are doing the work instead of humans?

​Facilitated by Jay Nathan, CEO and Co-founder at Balboa, and featuring Maree McMinn, Product Director at Infor, this session looks at how enterprise software companies are learning to monetize agentic features when the old per-seat math no longer works.

​Maree leads commercialization for Infor's AI and automation suite. She tracks token usage account by account, month over month, trying to understand why two customers with the same hundred named users can generate wildly different AI costs. She has learned that tokens and API calls, while the common language of AI cost right now, mean almost nothing to a customer trying to understand their bill.

​Together, we'll explore:

• ​Why per-user pricing was always a weak proxy for value, and why it breaks down once agents are the ones using the product

• ​Why tokens and API usage are the current language of AI cost, and why neither one helps a customer understand their invoice

• ​How to work backward from a customer outcome (cost saved or revenue generated) to a value-based price, instead of starting from infrastructure cost

• ​The hidden cost of building agents nobody has figured out how to monetize yet

• ​Why commercialization needs a seat at the roadmap meeting BEFORE product decides what to build

​This session is built for product leaders, pricing and RevOps leaders, and finance partners inside product orgs who need a framework for pricing agentic AI features, and not just a token counter bolted onto the old seat-based model.