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

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

The pricing model for AI services is evolving; companies might pay for successful outcomes, qualified leads, or resource utilization, reflecting a shift in how value is perceived in AI-driven businesses.

AI companies should align their pricing with what matters to their customers after demonstrating value, rather than relying solely on fixed or consumption-based pricing models.

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.

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

As AI models improve, the expectation is that they will converge in quality and pricing, making it essential for startups to build software that leverages this intelligence effectively.

In competitive markets, CEOs prioritize growth over cost savings, and successful AI implementations should focus on driving measurable topline outcomes.