Share This:

Accelerate revenue with

three real-world use cases for Agentforce and ARM. 

This article overviews the new-gen approach to Q2C and provides three high-impact use cases for Agentforce and Agentforce Revenue Management (ARM). You’ll get a sense of why this is so vital to B2B manufacturers and a high-level roadmap for adopting Agentic Q2C. 

 

First some quick context.  

B2B manufacturers have invested heavily in CPQ and quote-to-cash (Q2C) software to manage complexity. But complexity hasn’t diminished. It’s only increased with more configurable products, more channels, more pricing variability, and ever-higher, B2C-like customer expectations—even when buying complex and highly configurable products. 

 

A better model is emerging: Agentic Q2C 

Agentic Q2C is what we at PW call today’s approach to quote-to-cash, where AI-powered agents proactively execute, optimize, and continuously improve revenue processes—from quoting and pricing to renewals. This is where it gets interesting.   

One of the most compelling aspects of Agentic Q2C is how it feels to the business. Processes that once required dozens of clicks, multiple systems, and days of effort now happen almost instantly, driven by agents working behind the scenes. It can feel “automagical.” But the reality is that Agentic Q2C is built on strong data, thoughtful architecture, and clear process design. So, it’s all highly intentional. 

 

Agentic Q2C is based on Salesforce Agentforce and Agentforce Revenue Management (ARM). 

It’s the combination of Salesforce’s technology—not as separate tools, but as a unified, agent-enabled platform for revenue operations—that creates Agentic Q2C. Together, Agentforce and Agentforce Revenue Management (ARM) represent the next-generation, AI-powered quote-to-cash platform designed to replace traditional CPQ.  

 

To clarify, this isn’t just “AI on top” of CPQ. 

One of the most important things to understand is that Agentforce and ARM are not bolt-ons; they’re foundational. With ARM, Salesforce has moved core revenue processes onto a platform that natively supports agentic execution—meaning agents can act directly within the system, not just analyze data from the outside. Processes can be optimized continuously, and value can be delivered incrementally. That changes everything. 

Now, instead of static workflows, manual handoffs, and spreadsheet-driven processes, you have agents that execute tasks, collaborate across the Q2C lifecycle, and improve over time as data and models evolve. 

Agents can have a significant impact when successfully implemented and tailored to each business. Three real-world examples follow. 

 

Use Case #1: PO Intake Agent 

Eliminate manual quote creation, at scale. 

For many manufacturers—especially those selling through distributors—quote creation still starts with a purchase order, and that process is often painfully manual. Teams/people read PDFs and spreadsheets, extract product and pricing details, and rebuild quotes line-by-line in Salesforce. Thankfully, that labor-intensive, mistake-prone approach is quickly becoming obsolete.  

Now, the PO Intake Agent ingests purchase-order documents, extracts product, pricing, and deal data, and automatically creates highly accurate quotes—all within the system and within minutes.  

Impact: In one real-world Agentic Q2C scenario, manual accuracy hovered around 70–75% pre-implementation. The PO Intake Agent elevated accuracy to 95%+ and dropped processing time from days to minutes. 

Value: 

  • Revenue protection—pricing errors are reduced dramatically  
  • Cycle time compression—quotes generated in minutes instead of days  
  • Operational efficiency—entire manual teams can be redeployed  

 

 

Use Case #2: Intelligent Price Setting & Margin Optimization Agents 

Go from guesswork to guided pricing. 

Pricing is one of the most powerful—but also one of the most inconsistent and challenging—levers in manufacturing. Many organizations still rely on rep-driven pricing decisions, fragmented spreadsheets, and inconsistent margin targets. The result is dreaded margin leakage. 

This use case typically involves two coordinated agents: 

An Intelligent Price Setting Agent recommends pricing based on account segment, product, and historical deals. This establishes a strong starting point for sellers. 

A Price Analyst Agent evaluates final deal pricing, scores margin performance, and flags risk or missed opportunity. 

Impact: In one example, target margins were 40–45% but actual margins had dropped to approximately 32% due to inconsistency. With agent-driven pricing guidance, margins can be restored to target levels and incrementally increased toward 48–49%, unlocking millions in additional revenue.  

Value: 

  • Margin recovery—immediate improvement back to target ranges  
  • Consistency—standardized pricing across reps and regions  
  • Scalability—agents enforce best practices automatically  

 

Over time, these agents can become even more powerful. As more data is captured, models become more granular, predictions improve, and margins can increase incrementally. (Even 1–2% can equal millions in impact.)  

This is great news because industry research, including McKinsey pricing AI studies, shows that pricing optimization is one of the highest-impact AI use cases in B2B revenue operations. 

 

Use Case #3: Renewal & Amendment Agents 

Fix the most broken part of Q2C. 

If quoting is complex, renewals are often chaotic. Many organizations struggle with mid-contract changes, renewal cycles, disconnected systems, and manual data manipulation. In some cases, teams literally export data to spreadsheets, make changes offline, re-import the data—and hope it works.  

Renewal & Amendment Agents identify contracts and assets due for renewal, generate renewal quotes automatically, handle mid-lifecycle changes, and summarize account status. 

Impact: Transforms one of the most manual and error-prone parts of Q2C into a streamlined, reliable process.  

Agents enable faster renewals, ensure customers are consistently renewed and updated with accuracy and confidence, reduce missed revenue opportunities, and free up internal teams from weeks of manual effort. 

Value: 

  • Revenue retention—fewer missed or incorrect renewals   
  • Efficiency gains—massive reduction in manual effort  
  • Employee productivity—reduced burnout during renewal cycles  

 

There’s also a hidden benefit. When renewals are clean and consistent, customer experience improves, leading to higher retention and expansion.  

 

 

How to get started with Agentforce and ARM 

First, what not to do. Don’t try to do too much, too fast. It’s a formula for failure. Depending on the source, industry estimates suggest that approximately 75% of AI initiatives fail. Many AI initiatives stall because organizations lack clean data, don’t define clear outcomes, and/or attempt overly complex use cases too early. 

 

Successful agentic Q2C adoption looks like this.  

The best practice/best advice we find is simple: Start with one process. Prove the value. Then scale. This slow-but-steady phased approach wins the agentic race, reducing risk and accelerating ROI. 

1. Define the outcome. Start with a specific business problem, such as slow quote turnaround, margin inconsistency, or renewal inefficiency.   

2. Map the process. Understand how the process works today, where breakdowns occur, and what “good” looks like. 

3. Validate the data. Ask hard questions. Do we have the data to support this? Is it clean and accessible? Because bad data equals bad agents.  

4. Start small. Focus on one high-impact use case, one agent, one measurable outcome.  

5. Build the foundation. A strong ARM implementation ensures clean data models, scalable architecture, and seamless agent integration. 

6. Expand over time. As data improves, add more agents, increase sophistication, and layer in predictive models. 

 

Final Thoughts and Tips 

This isn’t just automation, it’s transformation.  

Individually, each of these use cases delivers value. But together, they represent something bigger In fact, the opportunity with Agentforce and Agentforce Revenue Management (ARM) isn’t just incremental improvement; it’s a fundamentally new way to operate revenue. Companies that move first from system-driven workflows to agent-driven execution won’t just be faster; they’ll be structurally better at capturing revenue. 

So, let your Agentic Q2C imagination run free.  

When business leaders see agents take real-world processes like those outlined above and execute them end-to-end, it opens the door to endless possibilities, and a new way of thinking about revenue operations.  

Contact Pierce Washington for a smart conversation about how Agentic Q2C will transform your business and accelerate revenue.