Why AI and Machine Learning Are the Future of CPQ

AI in CPQ isn’t just some future promise—it’s happening right now. The old way of handling quotes, relying on historical data and gut feeling, is becoming obsolete. With AI and machine learning (ML), manufacturers can predict outcomes, optimize quotes, and even identify which deals are worth pursuing. In fact, if you’re not leveraging AI in your CPQ system, you’re leaving money on the table.

Magnus Fasth and Patrik Skjelfoss shared a real-world example in the recent podcast, where one of their clients used machine learning to analyze four years of sales data. By feeding the system every quote—both won and lost—they created a predictive model that could tell them which future quotes were likely to succeed. The results were astonishing. Sales teams no longer had to guess whether a deal was worth chasing; they had data-driven insights that improved both the speed and accuracy of their decisions.

AI and ML aren’t just fancy buzzwords—they have practical, immediate benefits for manufacturers using CPQ systems.

Here’s why:

1. Predictive Insights
   Machine learning can analyze historical data and identify patterns that the human eye would miss. For example, by reviewing past quotes, an AI system can predict the likelihood of winning a new deal. This lets your sales team focus their efforts where they’ll have the most impact. As Patrik mentioned, it’s about more than just knowing which quotes to prioritize—it’s also about understanding whether a discount is necessary to close the deal, or if the customer is ready to sign without one.

2. Faster Quoting
   AI doesn’t need time to think. While humans might take hours—or even days—analyzing a complex quote, AI can deliver results in seconds. By streamlining the quoting process, manufacturers can respond to customers faster, reducing the risk of losing a sale to a more responsive competitor.

3. Smarter Discounting
   Sales reps often feel the need to discount heavily to secure a deal. But with machine learning, you can use data to determine whether a discount is really necessary. Machine learning models analyze similar deals to recommend the optimal pricing strategy, helping you maintain margins while still winning the business.

4. Customized Experiences
   AI allows for personalized customer experiences. By analyzing customer preferences, purchasing history, and product data, AI-driven CPQ systems can recommend the best products or configurations for each individual client. This creates a smoother, more customized experience for the customer and increases the likelihood of a sale.

What Does the Future Hold for AI in CPQ?

We’re already seeing AI transform CPQ, but the real potential is just starting to be tapped. Here’s what’s coming next:

• Real-Time Configuration Adjustments: Imagine a CPQ system that adjusts configurations in real-time, based on changes in supply chain, customer preferences, or even market conditions. Good old fashioned AI makes this possible, automatically tweaking configurations to optimize for availability, cost, and customer demand. This has been around for years and years.

• Natural Language Processing (NLP): AI-driven CPQ systems are getting better at understanding and responding to natural language. This could mean sales teams interacting with their CPQ systems by simply speaking to them—asking for quotes, configurations, or updates using voice commands.

• Enhanced Forecasting: Beyond predicting whether a quote will turn into a sale, ML will help companies forecast demand, optimize production schedules, and manage inventory. The result? Leaner, more efficient operations that directly impact the bottom line.

In fact, cpq.se is already helping clients explore some of these AI-driven solutions. One example from the podcast: using a specialized language model to automatically generate executive summaries for quotes. These summaries are fine-tuned to the specific market, ensuring the right sentiment is conveyed and the customer’s attention is grabbed immediately. This kind of automation not only saves time but also improves the quality of the sales process, tailoring each interaction to the customer’s needs.

Why You Need AI in Your CPQ Now

If you’re still on the fence about AI, think about this: companies that embrace AI and machine learning will have a significant competitive advantage. They’ll be faster, more accurate, and better at closing deals. Companies that don’t? They’ll get left behind.

AI in CPQ is no longer an experimental technology. It’s already delivering real, measurable results. From smarter quotes to better customer experiences, manufacturers that leverage AI in their CPQ systems will be able to respond faster to market demands, improve their sales processes, and ultimately drive more revenue.