Every B2B company today needs AI-driven pricing, though not all recognize it, and only a few excel at it. Based on experience with over 50 projects in this space, here are the key aspects you must master for success:

1) Build a high-quality, automated data pipeline
It’s a fundamental requirement but often underestimated. Without accurate and comprehensive data on products, customers, and contracts, AI-driven pricing lacks a strong foundation. Establish an automated data flow that continuously updates the latest observations so your AI model can refresh price recommendations periodically—whether almost real-time, daily, weekly, or monthly, depending on your business needs. Once the basics are in place, enhance the model by incorporating advanced data like macroeconomic trends or demand forecasts to refine price recommendations further.

2) Prioritize process over tool
Many clients initially seek an AI-pricing tool, but success depends on the process, not just the technology. Define clear responsibilities and mandates early on. Who owns the AI pricing tool? How will it improve pricing decisions? How will human expertise be integrated? What leeway will sales teams have during negotiations? Without aligning the process, even the best tools risk poor adoption.

3) Balance science with art
No AI pricing tool delivers perfect recommendations due to market unpredictability and limited data points. Accept that AI models provide a strong starting point and structured approach but require human intelligence to refine outcomes. Your AI pricing tool should support this interplay by offering a user-friendly interface that allows pricing teams to adjust model outputs before sharing them with sales.

4) Experiment with diverse model types
Test a variety of machine learning models rather than defaulting to specific ones like neural networks, as some vendors do. Evaluate which model best fits your data. In simple terms, this means identifying the model that most accurately replicates historical pricing agreements.

5) Empower sales in final price setting
Provide sales teams with a recommended price range that includes a target price, a floor price, and an aspirational price. This approach empowers them to set the final price while accounting for customer-specific nuances and contextual details that the AI model or pricing team may not fully capture.

---

Subscribe to stay updated
I regularly shareinsights on B2B pricing, selling, negotiating, and the application of AI in commercial processes. subscribe here below to stay informed.