Founders and product leaders in the AI space face unique challenges when establishing pricing strategies. Unlike traditional software, AI products come with significant costs tied to compute, inference, and customer support. Each query incurs expenses that must be factored into pricing models. Companies often see gross margins of 50-60% for AI, compared to 80-90% for traditional SaaS. This reality highlights the need for a pricing strategy that not only covers costs but also reflects the value delivered to customers. Three emerging AI business models are influencing how companies set their prices: Copilots, Agents, and AI-enabled Services. Copilots, which enhance human productivity, typically follow a per-seat or consumption model. Agents operate autonomously and are often priced based on tangible ROI, moving away from per-seat fees. AI-enabled Services blend automation with human oversight, allowing for flexible pricing that can adapt to customer needs. Pricing models vary from consumption-based to outcome-based, with a clear trend toward aligning costs with customer value. To find the right pricing strategy, companies need to understand their market and customers deeply. Establishing a cross-functional team to explore key questions about go-to-market motions and customer willingness to pay is essential. The article outlines guiding principles such as focusing on value delivered rather than access and using incremental price adjustments to gauge customer response. This approach helps avoid common pitfalls and aligns pricing with the actual outcomes customers experience.