Why the ‘Messy Middle’ for Office of Revenue Needs AI and Salesforce

I attended Salesforce’s Dreamforce event in San Francisco to learn more about the company’s expanding vision for artificial intelligence (AI) and its newest capability, Agentforce Revenue Management. Salesforce has steadily evolved from being a CRM leader to positioning itself as an AI-first business platform. The introduction of Agentforce marks the company’s most ambitious step yet in connecting AI with the financial heartbeat of the enterprise, the revenue lifecycle. Salesforce’s evolution from Revenue Cloud to Revenue Management represents a strategic expansion beyond quote-to-cash processes toward a more intelligent, end-to-end revenue operations platform. By integrating AI and automation, Salesforce is now enabling businesses to optimize pricing, forecasting, and revenue recognition in real time, unifying every aspect of the revenue lifecycle under its AI-first strategy.

The longer-term opportunity is cultural, designing revenue processes where artificial intelligence (AI) and people share accountability for growth, accuracy and customer trust. I recommend that Salesforce customers anticipate and evaluate how agentic capabilities can be embedded into existing revenue workflows now, not later, to avoid falling behind. This should include a readiness assessment of current systems, processes and organizational structures to identify where complexity may arise from siloed applications, inconsistent data models or manual approvals. Dreamforce made it clear: The era of agentic systems is no longer theoretical. The “messy middle” between quote and cash is being actively redesigned, and Salesforce intends to lead that transformation from within its platform, not around it.

ISG research indicates that enterprises are making major investments to transform revenue operations. Over the next two years, organizations plan to double spending on AI-enabled systems designed to accelerate quote-to-cash processes and optimize pricing and compliance. Nearly 80 percent of those that have implemented some form of AI across sales or finance functions have seen measurable benefits, including improved forecast accuracy, shorter quote cycles and stronger audit controls. Yet for many organizations, the operational challenge remains the same. Disconnected systems, siloed applications, spreadsheets and manual workflows create what Salesforce executives described as the “messy middle.” It is the point where information breaks down between sales, finance and fulfillment and where customer experience, revenue recognition and margin performance all start to suffer.

Agentforce Revenue Management is Salesforce’s answer to that problem. The company describes Agentforce as an AI teammate that works alongside sellers, finance leaders and operations teams to automate tasks that traditionally span multiple systems. Rather than introducing yet another standalone tool, Salesforce has embedded Agentforce directly into its existing platform. It connects across Sales Cloud, Service, Commerce and Billing to manage pricing, quoting, contracts, orders and compliance. The company’s theme, “Agents automate. Humans lead. Revenue flows,” captures its intent to augment people rather than replace them. Agentforce supports end-to-end revenue operations by providing intelligent recommendations for product configurations, quote generation, pricing approvals and billing accuracy. The advantage for customers is a unified data model that enables every team involved in revenue to work from the same information set, something many organizations have struggled to achieve. ISG Research asserts that through 2027, providers will develop agentic AI capabilities to automate much of the revenue lifecycle for renewals and expansions offers, improving the customer experience and resulting in increased lifetime value.

One of the most interesting design elements of the platform is its composability. Salesforce allows customers to start wherever the pain point is greatest—product catalog, CPQ, contract lifecycle or billing—and add capabilities over time. Each component can function independently, yet everything connects through shared data. At Dreamforce, Salesforce demonstrated how the system could generate a discount recommendation based on historical deal performance, create and approve a quote within minutes and handle contract amendments and renewals without breaking audit integrity. Tableau dashboards brought together key indicators such as annual recurring revenue (ARR), monthly recurring revenue (MRR), churn and margin in real time, allowing executives to monitor performance without waiting for finance cycles to close. The overall impression was that Salesforce is no longer positioning AI as an overlay but as an integrated function that drives business rhythm.

Several enterprise customers joined the stage to discuss early results. Kaseya, Logitech and CoreWeave each shared examples of how automation and AI-driven insights are changing the approach to revenue management. Kaseya reported a substantial reduction in quote cycle times. Others pointed to measurable improvements in deal standardization, compliance and transparency. One comment stood out: A revenue operations leader noted that his organization decided to build with AI in parallel with transformation rather than as a follow-up project. “We didn’t want to wait six or 12 months to add AI later,” he said. “We’re building with it now.” It is a sentiment ISG is increasingly hearing across industries that waiting for the “perfect” state before introducing AI may result in being permanently behind.

For enterprises, the launch of Agentforce Revenue Management raises practical questions about readiness. Early adopters with clean product catalogs, unified customer records and established data governance will likely benefit first. Those organizations will use agentic automation to reduce cycle times and accelerate decision-making. Enterprises that are later in the maturity curve can learn from these early efforts, but may face growing pressure as competitors use AI to speed up quoting, billing and renewals. The risk of delay is not technological obsolescence but cultural inertia. When peers are closing deals in hours instead of days, legacy approval processes quickly become bottlenecks.

What Salesforce presented at Dreamforce also speaks to a larger shift in enterprise thinking about automation itself. Many organizations are realizing that AI adoption is no longer just a technology decision; it is a governance and organizational design decision. Agentforce offers a glimpse of how this next phase might unfold. By embedding intelligence at every step of the revenue process, Salesforce is not only automating work but redefining who does the work—human, machine or both. This introduces new conversations about accountability, auditability and the ethics of AI-assisted pricing and contract execution. Enterprises will need to ensure the use of agentic systems remains transparent, traceable and compliant with both corporate policies and emerging regulatory expectations around AI usage.

Salesforce’s approach highlights the next stage of automation maturity—what ISG refers to as the shift from efficiency to decision velocity. In this new phase, success is defined less by how quickly a task is executed and more by how intelligently a decision is made. Agentforce moves Salesforce closer to becoming a full lifecycle revenue platform that bridges CRM, revenue performance management, and finance. Its emphasis on agents as trusted teammates represents a subtle but important philosophical shift. Instead of humans training models in isolation, models are now being trained to work alongside humans in context, learning from the same data and processes that drive the business.

Regards,

Barika Pace