Intelligent Quoting: How AI is Transforming CPQ Software

Artificial intelligence is already one of (if not the) hottest topics in the world of business tech. 83% of companies already say it’s a top priority in their business plans.

McKinsey estimates AI can bring a potential $13 trillion to the manufacturing sector by 2030. And Accenture predicts a 40% productivity boost by 2035 in industries that adopt AI.

There are dozens of ways to integrate AI into your workflow. Generative AI apps like ChatGPT probably come to mind first. But that’s just the tip of the iceberg.

Your mind might not jump to the quote-to-cash process when you think about where to apply AI to your business. But it’s the most important aspect of your sales operations — everything from quoting to contracting to the final transaction falls under this umbrella.

The good news: There’s a new breed of AI-powered CPQ (configure, price, quote) software transforming the modern sales process.

And it’s transforming it for the better.

How AI and machine learning work within CPQ software

CPQ software provides a platform for product selection/configuration, pricing calculations, and quote generation. These are tasks your sales team would normally have to do manually. They’d need to reference multiple systems, deal with complicated pricing structures, and put everything into a quote template themselves.

With the intuitive interface CPQ provides, that’s no longer an issue. It automatically applies your product rules and pricing logic to create an accurate branded quote.

AI supercharges this process.

There are a few places where AI can be particularly useful when delivering quotes:

• Dynamic product recommendations
• Automated pricing adjustments based on real-time data
• Data entry when building quotes
• Analyzing pipeline data from leads and sales transactions
• Forecasting and predictive analytics for accurate pricing strategies

AI can also assist with contract negotiations, identify upsell/cross-sell opportunities, and even help with lead prioritization. And when you integrate CPQ and CRM, the amount of data and workflow automation at your disposal multiplies exponentially.

AI and ML facilitate smarter quoting.

It’s simply too risky to use spreadsheet- or document-based quoting. Your rep might miscalculate pricing. They could quote a customer for a product that doesn’t fit your current delivery capacity. Or, they might simply forget to include a line item.

With AI and ML in play, you’re getting an optimized quote every time. No more guesswork or manual inputting errors. Your reps can focus on selling, and nobody will have to go back and fix things later.

Let’s dive into how AI-powered quoting works.

Intelligent configuration

Traditionally, configuring complex products requires you to define and apply a bunch of different rules and constraints, like feature dependencies (if one option is selected, another must be excluded), pricing adjustments, and manufacturing limitations.

AI enhances this process by continuously learning from past configurations and interactions. It applies these insights to predict the best product combinations based on a customer’s specific needs. 

Through guided selling, the end user gets a personalized experience based on their unique requirements. This makes for a fast, error-free process.

For manufacturers, when you integrate CPQ with your ERP system, the AI-driven platform will also dynamically adjust available options based on your current production capacity. That way, your sales reps always see viable configuration options, and nothing else.

Predictive quoting

ML algorithms analyze vast amounts of historical sales data — past quotes, customer purchase histories, transaction outcomes, you name it. They can also segment customers based on behavior, purchasing power, and price sensitivity.

By learning from these trends, your system can predict what price points will most likely result in a successful sale.

Imagine a business selling custom manufacturing equipment. A repeat customer requests a quote for 100 units of a product.

Here’s how predictive quoting works:

1. Upon analyzing the customer’s past orders and negotiations, it appears the customer previously requested a 15% discount on bulk orders.
2. The system looks at your input costs and determines the price of raw materials has risen by 5% since the customer’s last order.
3. The system adjusts the base price to account for increased costs — a 12% discount is sufficient to win the deal while maintaining a healthy margin.
4. Based on this analysis, the CPQ system generates a quote with a 12% discount, forecasting that the customer will likely accept it, given their historical negotiation patterns.

This intelligent configuration leads to higher customer satisfaction, as the system accounts for real-world constraints and preferences automatically.

Dynamic pricing and margin optimization

AI can dynamically adjust pricing based on real-time factors like market demand, competitor pricing, customer profiles, and historical data. Of course, it considers your profit margins as well.

If you’re operating in an industry where prices change quickly and frequently, dynamic pricing gives you the power to react just as quickly.

And if you use custom pricing (e.g., for an enterprise SaaS product or engineer-to-order manufacturing deal), you’ll need CPQ’s AI features to account for profitability when quoting each customer.

When your rep quotes someone someone below the minimum profitability threshold, the CPQ will flag it and prevent them from going through with the deal.

Personalized customer experiences

Since AI-powered CPQ systems analyze your customers’ purchase history, it’s easy for them to make targeted upselling/cross-selling recommendations for your sales reps.

This streamlines quote delivery and increases your average deal size, sure. But it also makes the buying experience more personalized.

In a world where 86% of B2B buyers expect companies to be well-versed in their preferences during sales interactions, this is the difference between long-term customer satisfaction and losing the deal altogether.

Churn risk analysis

CPQ with AI revolutionizes financial forecasting. Since it has a wealth of customer data, it can also assess customers’ likelihood of churning.

For subscription-based businesses, this means the CPQ system can analyze how likely a customer is to renew or upgrade. It considers factors like contract length, product usage trends, and customer preferences.

For manufacturers and consumer goods companies, PROS Smart CPQ is a solid example of this. Its churn forecasting algorithms consider all the factors that influence customer churn. You can use this data to plan out retention strategies or change your sales approaches based on the chances of success with each customer.

Enhanced sales enablement

CPQ itself is a sales enablement tool. With AI, it becomes an even more powerful asset.

Through…

• Guided selling
• Automated configuration
• Predictive quoting

…your reps can focus on building relationships and selling. No more backtracking to fix mistakes or incorrect configurations after the fact. And they have all the information they need to act as trusted advisors when working with prospects.

Beyond this, your sales leaders can use CPQ data to look at sales cycle times and sales rep performance. From there, they can identify knowledge and skill gaps, and develop training programs tailored to specific needs.

The Strategic Shift: From Static Rules to Adaptive CPQ

Artificial intelligence is no longer a peripheral business topic; it is the core of modern business tech, with 83% of companies prioritizing it in their strategic planning. While generative AI like ChatGPT is the most visible application, the most profound impact is occurring in the quote-to-cash (QTC) process.

QTC is the engine of sales operations, encompassing everything from initial quoting to final transaction. Traditionally, CPQ (Configure, Price, Quote) software relied on rigid, manual “If/Then” logic. However, a new breed of AI-powered platforms is replacing these static rules with continuous learning loops.

How AI Supercharges the Quoting Process

By integrating machine learning (ML), CPQ transitions from a reactive tool to a proactive advisor. This shift provides several technical advantages:

• Dynamic Product Recommendations: Analyzing historical data to suggest the most viable product combinations.
• Contextual Pricing Adjustments: Automatically factoring in real-time variables like supply chain shifts or raw material costs.
• Predictive Analytics: Forecasting which price points are most likely to result in a successful close based on buyer behavior.
• Risk Mitigation: Reducing human error in complex configurations that could lead to unbuildable products or margin erosion.

Comparative Analysis of AI-Powered CPQ Implementations

To understand how these technologies function in the real world, we can examine how different industry leaders specialize their AI capabilities.

Salesforce: Agentic AI and Guided Selling

Salesforce integrates AI through its Einstein and Agentforce layers. Rather than just following static rules, the AI acts as a digital assistant that uses generative models to answer natural language queries from sales reps. By analyzing historical CRM data, it provides “Next Best Action” recommendations, helping reps identify high-propensity cross-sell opportunities within the quote interface.

Oracle: Scalable Self-Service Quoting

Oracle leverages AI to shift complex quoting from human-led to customer-directed. By embedding the CPQ engine into external-facing portals, Oracle enables customers to generate their own technical quotes. This transition allowed one major enterprise to increase self-service quotes from 2% to 79%, significantly reducing the sales cycle while maintaining 100% order accuracy.

DealHub: Unified Revenue Orchestration

DealHub positions CPQ within a broader unified revenue engine. Rather than treating quoting as an isolated task, the platform integrates it into a “quote-to-revenue” workflow that syncs data across Sales, Finance, and Customer Success. This orchestration ensures that when a deal is signed, the downstream financial and renewal data is automatically triggered without manual intervention.

Tacton: Industrial Configuration for Manufacturing

Tacton specializes in the “Engineer-to-Order” sector. Its AI handles the immense technical complexity of heavy machinery, ensuring every configured product is physically buildable. By integrating CPQ with CAD and ERP systems, the platform dynamically adjusts options based on real-time manufacturing capacity and global supply chain constraints.

Yagna iQ: Automated Contract Renewals

Yagna iQ focuses on the post-sale lifecycle. Its AI-powered “Renewal Cloud” proactively identifies upcoming contract expirations and generates renewal quotes based on previous consumption patterns. This predictive approach reduces churn by engaging customers with personalized offers before they seek alternatives.

Camos: Intelligence for Capital Goods

Camos focuses on mechanical and plant engineering. Its AI tools help users evaluate complex product models and simplify comparisons between vast product catalogs. This is particularly effective in high-customization industries where reducing the “search time” for parts is a major operational bottleneck.

CloudSense: Dynamic Pricing for Telecom & Media

CloudSense specializes in high-volume, service-based industries. It uses AI-driven insights to guide sales reps through complex service plan configurations, ensuring all localized pricing rules and add-ons are considered. This results in more accurate billing and higher average deal sizes through targeted, automated upsells.

Emerging Trends in the CPQ Space

Beyond current implementations, several trends are defining the next generation of sales tech:

• Explainable AI (XAI): As models become more complex, XAI provides transparency, helping users understand why a specific configuration or price was recommended.
• Visual Configuration (AR/VR): In industries like manufacturing, 3D and AR configurators allow buyers to visualize customized products in real-time, bridging the gap between sales and engineering.
• Lifecycle Personalization: Moving beyond the initial sale to offer personalized billing experiences, usage-based pricing models, and tailored loyalty incentives.

Final Assessment

The most effective AI-powered CPQ systems are those that fade into the background, providing a “quiet intelligence” that minimizes manual work while maximizing margin. When evaluating vendors, focus on those whose AI specialization aligns with your specific industry challenges—whether that is technical complexity, renewal retention, or self-service scalability.

When you’re selecting a new CPQ vendor, test the AI and automation features it offers. Ask about use cases and customer success stories, too. That way, you’ll find the solution that best fits your organization’s unique needs – and gives you a competitive edge. Ready to find the right CPQ for your needs? Take a look at our CPQ reviews and product comparisons to make an informed decision.