How we define AI in pricing Analytical AI, gen AI, and agentic AI each play a unique role in pricing. Analytical AI In pricing, this is the application of machine learning, dynamic deal scoring, econometrics, and other AI to deliver pricing insights, including forecasts, benchmarks, and willingness to pay. Examples include producing list pricing guidance, sales discount guidance, negotiated deal scores for exception approval, and promotional price optimization. Gen AI In pricing, this is the use of large language models and other gen AI technologies (including small language and domain-specific models) to explain pricing guidance in natural language by drawing on structured and unstructured data as well as AI models. Examples include generating summaries of market and competitor intelligence, approving list price changes, drafting emails to clarify customer requests, and suggesting key talking points versus competitive offers to assist sales in value selling. Agentic AI In pricing, this is the application of autonomous AI “agents” to reason and act on behalf of humans within defined guardrails, calling on automation, gen AI, and traditional analytics and AI to complete larger process steps and to know when to pull in humans for exceptions management. Examples include adjusting list prices based on robust governance protocols and human oversight, supporting real-time discount approvals, planning promotions or deal desk workflows via a copilot, and streamlining renewals. Human oversight is retained for exceptions and decisions. Organizations with established data models and analytical AI capabilities can focus on accelerating quote turnaround or improving deal guidance where incremental integration is limited. Those with weaker data foundations may start with market intelligence or competitive analytics, which can often be deployed faster using existing platforms. In all cases, early pilots should prioritize limited integration complexity, clear ownership, and tangible business outcomes. 2. Build solid foundations that enable scaling The lesson of a successful pilot is not to continue to build pilots—that’s a recipe for dissipated resources and a dead end for scale. Shift the focus to strengthening foundations, including investment not only in data quality, integration, IT platforms, and governance but also in the continued advancement of pricing models themselves. B2B pricing: Navigating the next phase of the AI revolution 9