The State of AI in CPQ: Hype, Reality, and What’s Next

Artificial Intelligence (AI) is dominating conversations across industries—but what does it really mean for Configure, Price, Quote (CPQ) solutions? As someone who has been focused on CPQ for almost 30 years, I wanted to take some time to unpack the current state of AI in CPQ—what’s hype, what’s reality, and what you should actually pay attention to.

What Customers Are Asking

Let’s start with what I see and hear from customers. There is definitely high interest in AI, but it’s often driven more by curiosity than immediate purchasing intent. Large enterprises are exploring AI capabilities more than smaller companies, but adoption varies greatly based on region, industry, and specific business needs.

Interestingly, analysts and vendors often appear more enthusiastic about AI than the customers themselves. The hype is loud, but in reality, many organizations are still prioritizing foundational CPQ tasks over advanced AI integrations.

Where AI Is Actually Used in CPQ

Despite being in early stages, AI is already playing a role in several key CPQ areas. Here are some of the more common use cases today:

  • Automated content generation: Tools like Copilot or ChatGPT-style models help create quote introductions or respond to customer feedback.

  • Approval automation: Based on predefined rules, AI streamlines what used to be complex, manual approval workflows.

  • Guided selling: Whether through filter-based navigation or AI-driven chatbots, users are being steered more effectively to the right products or configurations.

  • Email-based configuration: Some solutions parse customer requests from emails and auto-fill configuration inputs—a huge time-saver, especially for large quotes or RFPs.

  • Translation and documentation: AI helps translate technical specs and draft user-facing documents faster than ever.

  • Cross-sell/upsell suggestions: Personalized recommendations are becoming more precise thanks to AI pattern recognition.

  • Rule setup assistance: The dream of AI-supported admin tools that set up pricing or configuration rules is gaining traction, though it’s far from perfect today.

These are just a few examples. Many vendors are also experimenting with generative agents, AI for automated testing, and predictive analytics for customer behavior.

What’s Still Missing?

Despite progress, we’re not in fully autonomous CPQ territory yet. One key limitation is AI transparency—users often see suggestions or pricing changes without understanding why they’re happening. This “black box” problem can reduce trust and hinder adoption.

Another big concern is data security. Companies want assurance that sensitive customer and product data won’t be mishandled, especially when working with third-party models.

Lastly, hallucinations—AI making things up—remain a risk. While not inherently bad in creative contexts, hallucinations are unacceptable in CPQ, where accuracy is critical.

Is It Worth the Investment?

Many customers ask: “Is now the right time to invest in AI for CPQ?”

My answer: It depends on your specific needs. Some vendors are investing in small proof-of-concept projects (under $25k), which allow businesses to experiment with minimal risk. This can be a smart way to test AI use cases without making a large commitment.

But keep in mind: standard CPQ functionality can still solve many challenges effectively. You don’t need AI to get significant ROI. Prioritize features that bring the most value with the least complexity.

Looking Ahead

It’s clear that AI will become more deeply embedded in CPQ over time. Vendors are hiring AI experts, global system integrators are building custom AI solutions, and interest in complex quote automation is rising. While AI may not be the main selling point yet, it’s increasingly becoming a differentiator.

What matters most is how well vendors address the real concerns: explainability, security, and integration with existing processes. CPQ buyers should focus on practical use cases, not buzzwords.

Final Thoughts

AI in CPQ is not about chasing the latest trend—it’s about understanding where it can truly enhance your sales processes. As this technology matures, we’ll likely see a shift from “nice to have” to “must have.” Until then, use your judgment, run experiments where it makes sense, and always keep ROI front and center.

If you’re evaluating CPQ solutions or want help understanding how AI fits into your strategy, feel free to reach out to us at info@novuscpq.com. We’re here to help you navigate these changes and find the right fit for your business.

You can also listen to a CPQ Podcast episode we recorded on this topic here