Real-world ways AI is transforming CPQ and creating measurable revenue gains for B2B companies.
AI is rapidly reshaping how companies enable configuration, pricing, and quoting of complex products and services. Read on for useful insights and examples of use cases you can implement right now to start realizing significant AI business benefits.
AI isn’t scary, it’s strategic.
Yes, AI is new. Yes, it’s evolving. And yes, there’s a lot of hype around AI. But don’t let all the noise and unknowns stop you from leveraging the game-changing benefits of AI in the quote-to-cash (Q2C) process. And don’t think of AI as just another efficiency tool. New AI-driven capabilities like automated quote generation and SDR-level prospecting are generating real revenue and impacting bottom lines every day. With the right partner, the right data strategy, and the right use case, your B2B can soon enable more intelligent—and profitable—product configuration and pricing.
The juice is worth the squeeze.
From deal size to win rates to customer satisfaction, AI isn’t just improving quoting—it’s improving outcomes.
The effort and expense of adopting AI into your Q2C system and process is far outweighed by a long list of benefits. These include faster quotes, smarter pricing, upselling and cross selling, fewer errors, predictive insights, accelerated deal cycles, improved win rates, happier customers, and futureproofing your business. For manufacturers in highly competitive markets, the message is clear: if you’re not exploring AI in your quote-to-cash processes, your competitors probably are. The goal with AI is to get and stay a step ahead of competitors; or at very least to not fall a step behind.
AI powered quoting enables B2B companies to:
- Guide reps with real-time product and pricing insights
- Price dynamically and competitively based on market signals
- Tailor quotes to customers based on past performance
- Sell more accurately and avoid costly change orders
- Reduce delays and errors by automating repetitive quoting tasks
- Target more of the right people at the right time with the right products
4 Use-case Examples of AI improving CPQ Today:
Intelligent Quote Configuration and Pricing
Scenario: Picture a sales rep leaving a meeting. They say to their phone, “Create a quote for customer X for products A, B, and C. Make it aggressive on pricing.” This is possible now with voice-prompted agentic AI models (trained on historical quote and win-rate data) that can trigger intelligent configuration. These agents understand customers’ buying history, product compatibility, and previous discounts. So, they can rapidly generate data-informed, winning quotes that are strategically tailored to close deals. Forget keyword-based quoting bots that only drop items into carts. The new AI agents are context-aware and capable of predictive quoting that eliminates guesswork and delays.
Impact: Faster, smarter quotes based on what’s actually working, increasing win rates, and reducing time to value.
Dynamic Pricing Based on Market Indexes
Scenario: Consider quoting in volatile commodity industries like oil and gas where prices change multiple times a day. Traditionally, quoting in these markets required manually updating tables, pulling in new rates, recalculating everything, and always chasing a moving target. Accurate quoting and pricing in this space is difficult to consistently get right. With AI-driven dynamic pricing, quoting engines can plug directly into market indexes and calculate pricing in real time. No more manual refreshes or lagging tables; just fast, accurate quotes reflective of live market conditions.
Impact: Enables pricing agility and risk mitigation in commodity-based sectors while improving operational efficiency and customer trust.
Real-time, Inventory-based Quoting
Scenario: Imagine quoting a build-to-order product—without knowing whether you have the parts to fulfill it. That’s a recipe for disappointment. AI changes the game by providing real-time visibility into inventory and manufacturing capacity during the quoting process. It can also factor in distributor timelines and supply constraints. This extends to quoting against sales agreements. If a customer committed to buying a specific quantity over time, AI can ensure the quote aligns with what’s been purchased, what’s in production, and what’s already promised elsewhere. In other words, AI enables smarter quotes based on what and when you can actually deliver.
Impact: Reduces overcommitment, improves delivery accuracy, and gives reps the confidence to quote with precision to close more deals more quickly.
Smarter Services Quoting
Scenario: AI brings new superpowers to companies that quote services for things like maintenance. By analyzing past statements of work (SOWs), project outcomes, timelines, and change orders, AI can forecast what similar projects will truly require in terms of hours, resources, and roles. That means more accurate quotes for services with better staffing plans and fewer change orders mid-project. Think of AI as turning tribal knowledge into institutional intelligence.
Impact: Accelerates sales cycles, improves services margins, and enhances customer satisfaction by aligning expectations with reality.
Take a crawl-walk-run approach to AI adoption.
The sooner you begin introducing AI into your Q2C, the smarter your AI will get—and the further ahead you’ll put your business. Like most things, is no “easy button.” But there is a smart, proven path forward. Following is a good starting point and sensible steps for infusing your quote-to-cash and revenue-cycle process with the power of AI.
Step 1: Understand your data.
This may be the most critical step because AI isn’t magic. It’s based on data. And, of course, bad data in means bad data out. Clean and well-architected product, pricing, and customer data is the solid foundation AI must be built on to deliver on the dream. So, step-one is understanding your data (without personal bias). This takes due diligence upfront to peel back the layers and really understand what data there is, where it’s coming from, what it means and is trying to tell you, and what you want that data to do. Companies that are succeeding with AI, despite the early adoption phase that we’re in, are simply cleaning up their data and properly preparing it for AI use.
Step 2: Identify predictive patterns.
Analyze your data to identify patterns within it that can be used for predictive AI. For example, which SKUs always show up together and which pricing models win in which regions? This helps define your AI model. The patterns and predictions within your data and your business are the specific areas you can leverage AI to begin automating some of those sales-supporting tasks.
Step 3: Define your use cases.
Based on your new data insights, choose one or a few highimpact AI use cases to test first. Examples include speeding up quote creation by automatically configuring quotes based on past successful deals; or layering in dynamic pricing tied to realtime market data. By identifying a few targeted use cases like these, you not only build confidence in your data and your processes, you also create initial wins that demonstrate the tangible value of AI in CPQ before going further down the path.
Step 4: Start small and get wins.
Don’t boil the ocean, as in don’t try to solve everything at once. Look first to the agentic side of AI. Salesforce Agentforce, for example, can do a lot right off the shelf to help you identify those less obvious data patterns. Simple tasks—like automatically adding commonly bundled products to a quote, suggesting discounts based on historical deal data, or autopopulating approval workflows—can make a surprisingly big difference in your revenue cycle. Customizable agents deliver confidenceinspiring results you can build on. This starts your AI journey on solid footing and puts you on a good path towards a more robust AI strategy and implementation.
Step 5: Train and iterate.
Just like onboarding a new hire, AI models need to be trained, tested, and continually developed. Don’t set it and forget it. Iterate and improve your AI agents as new data and lessons emerge. Don’t worry, you remain in control throughout the process. You guide and direct the way the AI model is applied. Also, because people control the AI, train them on maximizing these new agents and tools. Employees will feel more secure in their jobs and add in-demand skills to their resumes.
Why Pierce Washington for AI-powered quoting?
We know what it takes to make AI work in quote-to-cash because we know the data that drives it. We’re also Salesforce and Oracle Q2C partners with 20+ years of experience transforming revenue cycles for B2B companies. We specialize in turning highly configurable products and complex pricing environments into true bottom-line impact.
We don’t plug in a tool and wish you luck.
We approach every project as a business-critical, strategic partnership. We learn your financial goals and guide you on a tailored journey from strategic planning to demystifying your data to identifying the most impactful use cases to implementation and iteration of intelligent quoting systems that drive more dollars—not just new efficiency.
Let’s talk.
Whether you’re just AI-curious or ready to adopt AI, Pierce Washington is ready to help.