AI companies often struggle to define the value they generate, leading to uncertain pricing strategies as they experiment with different models.

AI companies should avoid charging as a tool, as it limits their budget and potential for growth; instead, they should focus on the value delivered by their agents.

To succeed, AI companies need to understand their unit economics and how to price their services effectively based on the value they provide.

Charging by activity and changing pricing based on workflows are effective strategies for AI companies, moving towards value-based pricing rather than cost-based pricing.

AI can facilitate bespoke contracts that align with customer value, moving away from rigid pricing models and allowing for more tailored agreements, which are essential for AI companies to effectively manage pricing and value delivery.

AI companies need better tools to understand their unit economics and capture more value from their offerings.

Value-based pricing is common in AI applications, where vendors charge based on the perceived savings or value they provide to customers, rather than just margins.

Experimentation is key in pricing strategy, especially for AI products. Testing different pricing models helps determine what resonates with customers and drives value.

Pricing strategy should focus on delivering value to customers, pushing the company to improve continuously rather than adopting a low-cost model that may compromise long-term viability.