The New Economics of Revenue | Sam Chung, Chief Customer Officer, Salesforce

How consumption models, AI, and rising complexity are reshaping the CFO mandateRevenue models are becoming more complex—and for finance leaders, the consequences extend well beyond how a company bills its customers. As businesses combine subscriptions, consumption-based pricing, and other models, CFOs face new questions around predictability, visibility, cash flow, and the infrastructure needed to manage revenue from beginning to end.Salesforce Chief Customer Officer Sam Chung brings an unusually broad perspective to these challenges. Over more than two decades at Salesforce, his roles have spanned revenue operations, finance and strategy, CFO of Salesforce.org, enterprise transformation, and customer leadership. In this conversation, Chung explores why CFOs must move further into operations, product, and technology; how AI and agents are changing the economics of finance; and why greater automation does not diminish the need for financial controls, precision, and human accountability.“You can’t have probably right revenue.” — Sam ChungKey TakeawaysConsumption changes the finance equation. Usage-based revenue can strengthen the connection between what customers pay and the value they receive—but it also reduces predictability. Finance needs greater visibility into usage, billing, collections, and ultimately cash flow.The CFO is moving upstream. Today’s finance leaders cannot simply report the financial consequences of decisions made elsewhere. Increasingly, they need to participate in product, pricing, innovation, and operating conversations as those decisions are being made.AI needs an economic baseline. Rather than beginning with sweeping transformation, Chung advocates starting with use cases where companies already understand their operating metrics. Establishing a baseline makes it possible to determine whether AI is actually improving productivity, quality, and ROI.“Probably right” isn’t good enough for revenue. AI models can be probabilistic, while critical finance processes demand precision. The challenge is determining where AI can create value while maintaining the controls and deterministic outcomes that finance requires.Agents don’t transfer accountability. AI agents may increasingly perform work once handled by people, but responsibility remains with finance leadership. Controls, visibility, and oversight must evolve to encompass work performed by humans and agents alike.