3. Redesign processes to reach escape velocity To move beyond isolated wins, organizations must redesign pricing processes from the ground up, treating AI agents as core rather than add-ons to human-centric workflows. The focus should be on how to redesign workflows that build on the advantages of both humans and AI agents. In practical terms, that means designing workflows based on the tasks that AI agents do best and integrating humans where and when needed, such as to approve actions and to resolve ambiguity or conflicting objectives. Achieving this shift requires a pricing command center to achieve tight coordination across pricing, IT, finance, sales, and data science, anchored in shared objectives and agile delivery models. Some of the highest-value opportunities sit at the intersection of pricing and adjacent commercial workflows. For example, a homebuilder with a captive-lending arm created an affordability agent to create a smoother handoff in the sales-to-mortgage journey, reducing the time from purchase agreement to underwriting for buyers by an estimated 40 percent. This integrated approach helps organizations move beyond isolated wins to reach “escape velocity” that scales impact. 4. Build the human–agentic AI system As pricing organizations evolve, companies need to redefine roles and upgrade skills. Future pricing teams will combine existing capabilities (such as driving adoption and interpreting insights) with new ones, including supervising AI agents, setting guardrails, and governing autonomous decisions. A practical way to guide this transition is to shift the conversation from where AI can be used to where decisions can be safely delegated . 4 When decisions can be executed with clear rules, auditability, and reversibility, agentic AI can operate with greater automation at scale. Where those conditions do not yet hold, gen AI still plays a powerful role by supporting humans with faster analysis, better explanations, and improved confidence to act. This graduated approach to autonomy helps organizations build trust, capability, and confidence over time. Humans remain firmly at the center and oversee strategy, ethics, and exceptions, while AI agents manage analytics and transaction flow within defined guardrails. A case example Consider a $15 billion B2B distributor that embarked on a multiyear pricing transformation: In the first phase, the company spent roughly 18 months reimagining pricing processes and deploying AI tools, including a price adviser and discount manager. These changes replaced largely manual pricing across more than 1.5 million SKUs with data-driven guidance and governance, delivering more than 200 basis points of margin improvement. The company then layered agentic AI on top of this foundation. Within just ten weeks, a pricing copilot identified an additional approximately 50 basis points of margin opportunity by defining a new price architecture and generating real-time recommendations and explanations across 4 “ The automation curve in agentic commerce ,” McKinsey, January 28, 2026. B2B pricing: Navigating the next phase of the AI revolution 11