The New Economics of Revenue | Sam Chung, Chief Customer Officer, Salesforce
Sam Chung, Salesforce's Chief Customer Officer, discusses how CFOs must adapt to fundamental shifts in business economics driven by consumption-based revenue models, AI adoption, and increased market complexity. He argues that CFOs must evolve from back-office financial stewards to technologically-savvy strategic partners who deeply understand business operations and lead enterprise-wide initiatives like AI implementation.
Summary
The transcript captures an in-depth conversation with Sam Chung about the evolving role of CFOs in response to significant business transformations. According to Salesforce research cited in the discussion, 65% of finance leaders manage multiple revenue models, 71% sell through more channels than a year ago, and 87% expect to add consumption-based products—statistics that underscore dramatic shifts in how companies monetize their offerings.
Chung identifies three major drivers of this change: accelerated innovation cycles requiring constant reassessment of go-to-market strategies, rapidly evolving buyer expectations influenced by consumer experiences, and persistent economic volatility forcing companies to rethink their positioning. He emphasizes that consumption-based models, while lacking the predictability that subscription models offered, provide the highest correlation between value delivered and payment made. This shift has profound implications for finance: consumption models create unpredictable revenue streams, require real-time transparency to customers on usage and billing, and impact cash flow through increased accounts receivable disputes and DSO challenges.
A central concern Chung raises is the "messy middle"—the complex, fragmented technology landscape between CRM and ERP systems where companies stitch together dozens or hundreds of applications. This creates data lineage problems and operational complexity that obscures visibility into critical revenue and cash flow processes. He advocates for unified revenue infrastructure that harmonizes data models from prospect to invoice collection.
Regarding AI adoption, Chung draws a critical distinction between probabilistic and deterministic processes. While frontier AI models are "highly probabilistic" and "kind of right most of the time," finance requires determinism—particularly for revenue recognition, which "needs to be right 100% of the time." He argues that enterprise software's value lies in codifying rules, policies, and workflows to complement AI's probabilistic capabilities, creating a marriage of human-intelligible logic and AI reasoning engines.
Chung emphasizes that the CFO role has expanded significantly—74% of finance leaders report expanded scope in the past year alone. CFOs are increasingly being asked to lead AI strategy, understand product development and go-to-market decisions, and participate in enterprise-wide transformation initiatives. This requires CFOs to move beyond the back office, develop deep curiosity about how the business operates, and become technologists themselves. He notes that the days of CFOs relying solely on ERP systems and a relationship with the CIO are over; CFOs must now understand diverse technology ecosystems and trends.
Chung also stresses the importance of starting AI implementations small, with use cases where strong baseline metrics exist, rather than attempting organization-wide deployment immediately. He cites Salesforce's own experience of initially "boiling the ocean" with AI before pivoting to focused use cases. Operational finance processes—accounts payable, expense reimbursement, order management, billing, and collections—represent ideal starting points because they have high volume, clear metrics, and measurable ROI.
Furthermore, Chung clarifies that accountability doesn't change with AI deployment. Whether a decision is made by a human or an agent, the responsible executive remains accountable. This requires new internal controls and visibility mechanisms to oversee both human and agent activity, making vendor selection critical—companies should choose technology providers that believe in human-AI collaboration rather than full automation.
Finally, addressing long-term preparation, Chung advises CFOs to develop deep operational understanding beyond financial performance metrics, become technology-literate, and engage with evolving technology trends. He references the "thin GL strategy" where ERPs focus narrowly on external financial reporting while other systems handle operational agility, a shift reflecting how rigidity in traditional ERP systems conflicts with business velocity.
About this episode
<h3><strong>How consumption models, AI, and rising complexity are reshaping the CFO mandate</strong></h3><p>Revenue 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.</p><p>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.</p><blockquote><strong><em>“You can’t have probably right revenue.” — Sam Chung</em></strong></blockquote><h2><strong>Key Takeaways</strong></h2><ul><li><strong>Consumption changes the finance equation.</strong> 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.</li><li><strong>The CFO is moving upstream.</strong> 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.</li><li><strong>AI needs an economic baseline.</strong> 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.</li><li><strong>“Probably right” isn’t good enough for revenue.</strong> 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.</li><li><strong>Agents don’t transfer accountability.</strong> 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.</li></ul><br /><p></p>
Key Insights
- Chung identifies that 87% of finance leaders expect to add consumption-based products, representing a fundamental shift from predictable subscription models to variable revenue streams with lower visibility
- He argues that consumption models create the highest correlation between customer value and payment but introduce significant cash flow complexity through increased billing disputes and DSO aging issues
- Chung claims that frontier AI models are 'highly probabilistic' and 'kind of right most of the time,' but finance processes like revenue recognition require 100% determinism, making pure AI automation insufficient without enterprise software safeguards
- He contends that the 'messy middle' of fragmented technology between CRM and ERP creates data lineage problems and opacity that prevents finance leaders from seeing end-to-end revenue processes
- Chung observes that CFOs must evolve from financial stewards confined to the back office to strategic participants in product development, go-to-market strategy, and innovation cycles
- He asserts that accountability for business outcomes doesn't transfer to AI agents—executives remain liable for decisions made by agents just as they do for human decisions, requiring new control mechanisms
- Chung emphasizes that successful AI implementation requires starting with high-volume operational processes (AP, expense management, billing) where baseline metrics exist rather than attempting organization-wide deployment
- He argues that CFOs can no longer rely primarily on their ERP and CIO relationships but must become technology-literate themselves to understand diverse vendor ecosystems and emerging trends like AI and consumption models
Topics
Transcript
This episode of CFO Thought Leader is made possible by Planful and Avalara. Bonus episode. These frontier models, these reasoning engines are super, super powerful. But I think one of the things that we have tried to explain to our customers is that they're highly probabilistic, which means like they're kind of right most of the time. But you know, if you're a finance leader, there are very few places where kind of right most of the time is okay. And so when I talk to customers about, you know, the best places to use AI, there is certainly a quant component to that conversation. Again, getting back to the ROI calculation. But I mentioned earlier, there is certainly a…
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