Footnotes
1. Tacton, “The State of Manufacturing: 2026,” June 2026.
2. Nucleus Research, “CPQ benefit areas driving the most value,” July 19, 2024.
How integrated CPQ turns sales promises into profitable growth
How integrated CPQ connects sales commitments to pricing, fulfillment, billing, and margin performance across quote-to-cash.
Configure, price, quote (CPQ) technologies are often designed around a critical business objective: Help sales teams quote faster.
Speed only solves part of the problem or Speed can actually cause other problems. A quote isn’t just a price on a page. It’s a customer promise. It tells the customer what they can buy, what it will cost, and when they can expect it. If that promise is built on disconnected data and processes, manual workarounds, or incomplete operational visibility, a faster quote may simply move the problem downstream—faster.
That’s where a modern CPQ application’s intended value can break down. The quoting screen may look new and improved, but the harder work still depends on whether product, pricing, availability, fulfillment, contract, and billing data are connected behind it. When that data isn’t drawn from an integrated picture across CPQ and enterprise resource planning (ERP) systems, the quote still goes out in minutes, but the errors surface after it’s approved—when they’re costlier to fix. Operations, fulfillment, invoicing, customer service, and support teams may then have to chase answers across functions and systems to keep the promise created when the customer quote was approved.
That’s why speed is often a mirage for CPQ upgrades: The front end moves faster, but the quote-to-cash process still strains under the weight of broader business complexity. A quote may look complete but still create order errors, margin leakage, billing disputes, fulfillment issues, or customer frustration.
CPQ creates more meaningful value for the business when it moves faster and connects what sales teams promise with what the enterprise can profitably make or procure, deliver, bill, and support. For leaders, the opportunity is to design CPQ around quote-to-cash outcomes so the whole business can improve accuracy, protect margin, reduce manual work, and scale growth with greater confidence.
What the data says:
In a survey of 280 manufacturing leaders, 62 percent reported at least moderate margin loss between quote and delivery, and 81 percent said maintaining CPQ configuration models required moderate to extremely high effort.1
When does CPQ become an enterprise problem?
CPQ becomes an enterprise problem in two ways: when it isn’t fed accurate, actionable data; and when the quote can’t carry cleanly into the business processes that have to deliver it. And both challenges are growing as products become more configurable, pricing becomes more dynamic, and customers expect easier ordering experiences and faster deliveries. Even modern CPQ platforms with flexible pricing engines can produce inaccurate quotes when they rely on static, locally maintained copies of cost or product data that actually live in the ERP—copies that drift out of date the moment the source changes. Disconnected quoting workflows compound the problem: Static data copies and manual handoffs simply can’t handle today’s complexity.
Consider manufacturing, for example. Quoting requirements can vary widely by product, customer, channel, and fulfillment model. A make-to-stock business doesn’t quote like a configure-to-order, make-to-order, engineer-to-order, or service-based business. For complex, configurable products, such as HVAC systems, the quote may need to account for product compatibility, configuration rules, availability, delivery constraints, installation requirements, and service commitments. Those rules also have to stay synchronized with the master data that engineering and operations maintain—otherwise the organization is governing two diverging rule sets, and the gaps surface downstream as errors.
When the CPQ process isn’t connected to those rules and constraints, the breakdowns tend to fall into a few predictable patterns:
- Pricing and margin gaps: Stale local CPQ copies of ERP-maintained cost or price data, inconsistent discounting, or disconnected margin thresholds can create leakage before the order is even booked.
- Configuration errors: Incomplete or inaccurate product logic can lead to quotes for products that can’t be built, delivered, installed, or supported as promised.
- Promise-date misses: Missing inventory, capacity, or delivery data can create commitments the business can’t meet.
- Operational rework: Quotes built on inaccurate or disconnected data may force operations, procurement, shipping, invoicing/accounts receivable, customer service, or support teams to reconcile details, correct orders, adjust fulfillment plans, or resolve customer issues after approval—delaying fulfillment and undercutting the speed CPQ promised.
- Order-to-cash friction: Manual re-keying or disconnected handoffs can trigger order errors, billing disputes, fulfillment issues, cash delays, or customer frustration.
These issues may look like sales operations problems at first. But the impact often lands across the enterprise: lower margin, slower execution, more rework, delayed cash, and a customer promise that becomes harder to keep.
How can leaders design CPQ around quote-to-cash outcomes?
Strong CPQ programs start with the desired business outcome, then align the process, data, governance, and technology needed to support it.
That means leaders should first get clear on what CPQ needs to improve. Is the priority faster quote turnaround? Better quote quality? Stronger margin control? Fewer order errors? Better customer experience? Faster cash conversion? In most organizations, the answer is some combination—but the right design depends on knowing which outcomes matter most.
From there, leaders can map the quote-to-cash process from the customer request through quote, approval, contract, order, fulfillment, invoice, and cash. That exercise helps reveal where CPQ needs to connect with the systems and teams that shape the customer promise.
The most important connections often include:
- Product and configuration data, so sales can quote what’s eligible and buildable
- Customer and contract data, so the quote reflects the right terms, commitments, and requirements
- Pricing, cost, and margin data, so the business can protect profitability while supporting commercial flexibility
- Inventory, capacity, and availability data, so promise dates reflect what operations can actually deliver
- Billing and order data, so the quote can move cleanly into fulfillment, invoicing, and cash collection
Governance matters as much as integration. Discounting rules, approval thresholds, exception handling, data ownership, and margin guardrails should be designed into the process, not managed through side channels after the quote is already in motion.
When CPQ is designed around business outcomes, it becomes a connected control point in quote-to-cash performance. Sales can move faster, while operations, finance, and customer teams get a cleaner path to fulfill what was promised.
Schedule a CPQ assessment with KPMG
Reach out to assess where CPQ and quote-to-cash performance may be limiting your sales productivity, margin protection, order accuracy, or customer experience.
How can AI make CPQ more effective?
AI is helping improve CPQ by better orchestrating the expanding matrix of quote-to-cash decisions. The strongest use cases help sellers make better recommendations, reduce manual analysis, automate defined tasks, and route the right exceptions to the right teams.
Guided selling is one example. If a customer’s need doesn’t produce an exact product match, AI can help recommend eligible alternatives based on product rules, configuration logic, availability, margin guardrails, and delivery constraints. That can help sales teams move faster without forcing them to search across product catalogs, engineering inputs, or informal institutional knowledge.
Pricing is another high-value use case. AI can help analyze historical quotes, win/loss patterns, customer segments, discount behavior, and margin thresholds to recommend a pricing range that balances win probability with profitability. Instead of asking sellers to calculate the “right” price on their own, the system can provide a more informed starting point while still routing exceptions for review.
Routine quote automation can also create value. For lower-risk quotes that fit predefined product, pricing, margin, and approval rules, AI-enabled workflows can help generate quotes more quickly while reserving human attention for complex, strategic, or high-risk opportunities.
The common thread is data discipline. AI can only help CPQ become faster and more intelligent when the underlying product, pricing, customer, availability, contract, and billing data are reliable. If those inputs are fragmented, AI may simply accelerate the quoting problems leaders are trying to solve.
What steps can leaders take to start improving CPQ outcomes?
Improving CPQ starts with understanding how the current quote-to-cash process performs today, where it creates friction, and what the business needs it to do next.
Leaders can begin with four practical moves:
- Benchmark quote-to-cash performance: Compare current quote speed, accuracy, approval cycles, win rates, margin performance, and error rates against business goals and relevant industry benchmarks.
- Identify where value is leaking: Look for points where disconnected data, manual workarounds, approval delays, pricing errors, configuration issues, or fulfillment gaps are slowing execution or weakening margins.
- Define an AI-enabled future state: Determine where AI and automation can improve guided selling, pricing recommendations, routine quote generation, exception routing, and margin protection.
- Build the value case: Quantify the benefits of improving CPQ across sales productivity, quote accuracy, order quality, margin performance, customer experience, and cash conversion.
Answering these questions gives leaders a clearer view of the problem—and whether the business is getting full value from the CPQ, ERP, and commercial platforms it already has. In some cases, the needed technology capabilities may already exist, but they may be misconfigured, underused, or disconnected from the workflows and data needed to improve quote-to-cash performance.
That’s where deeper CPQ and quote-to-cash expertise can help. The next step is to pressure-test whether the current environment is configured, connected, and governed in a way that supports the desired business outcome. From there, leaders can prioritize the improvements most likely to protect margin, reduce manual work, improve order accuracy, and help the enterprise keep the promises the whole organization is making to its customers.
What the data says:
When CPQ is designed and adopted effectively, the value case can be significant. A research analysis that studied well-designed CPQ deployments over two years found they delivered an average 121 percent return on investment, with payback within 16 months. The same analysis found a 56 percent increase in quoting efficiency and a 46 percent decrease in time spent fixing quoting mistakes and repricing.2
How KPMG can help improve CPQ and quote-to-cash performance
KPMG helps organizations assess CPQ and quote-to-cash performance, identify where value is leaking, and build practical roadmaps across process, data, technology, governance, and operating model. The work can help leaders understand whether their current CPQ environment is designed to support the outcomes they need—from faster quote turnaround and stronger margin control to cleaner order handoffs and better customer experience.
Depending on the organization’s needs, KPMG professionals can support targeted CPQ improvements, ERP integration, pricing and margin governance, AI-enabled quote automation, or broader commercial transformation.
KPMG professionals can help you:
- Assess current CPQ and quote-to-cash performance.
- Identify sources of value leakage across quoting, approval, contracting, ordering, fulfillment, billing, and cash collection.
- Evaluate whether existing CPQ, ERP, and commercial platform capabilities are being used effectively.
- Improve quote-to-order processes, data flows, approval rules, and exception handling.
- Strengthen pricing, margin, discount, and governance controls.
- Build the business case for targeted CPQ improvements or broader transformation.
- Identify AI-enabled CPQ opportunities, including guided selling, pricing recommendations, routine quote automation, and exception routing.
- Develop a practical transformation roadmap tied to business value, customer impact, and implementation complexity.
How KPMG can help
Move from disruption response to intelligent execution — with a supply chain designed to adapt, decide, and deliver in real time.
Meet our team
Vivek Sudhakar
Principal, Advisory, Platform, Oracle, KPMG LLP
Sean M Cassidy
Advisory Managing Director, Supply Chain, KPMG LLP
Joshua S Jujjavarapu
Director Advisory, Procurement & Supply Chain, KPMG US
Vivek Sudhakar
Principal, Advisory, Platform, Oracle, KPMG LLP
Sean M Cassidy
Advisory Managing Director, Supply Chain, KPMG LLP
Joshua S Jujjavarapu
Director Advisory, Procurement & Supply Chain, KPMG US