AI in CPQ – Challenges to Overcome and the Road Ahead

AI is reshaping CPQ, enabling faster, smarter, and more automated quoting. But as companies rush to implement AI-driven solutions, they face challenges ranging from data quality issues to user adoption barriers. While the potential of AI in CPQ is undeniable, businesses that overlook these hurdles may struggle to realize its full value.

Understanding and addressing these challenges is critical. In this article, we’ll explore the key obstacles companies face when adopting AI-powered CPQ and how to overcome them. We’ll also look ahead to what CPQ could look like in 2028, outlining the trends that will define the future of AI-driven sales automation.

Key Challenges in AI-Powered CPQ Adoption

1. Data Privacy and Security Risks

CPQ systems handle sensitive business data—pricing models, customer contracts, and product configurations. AI-driven CPQ tools often rely on cloud computing, raising concerns about data security and access control. Many companies hesitate to feed proprietary pricing and customer data into AI models without clear safeguards in place.

To mitigate these risks, businesses should:

• Implement strict data access policies and encryption measures.
• Use AI solutions that allow on-premise deployment or secure cloud environments.
• Regularly audit AI models to ensure they don’t expose confidential information.

Without proper security measures, AI-driven CPQ adoption can introduce compliance risks rather than streamline sales.

2. Data Quality and AI Bias

AI models learn from historical data, meaning any errors, inconsistencies, or biases in past quoting decisions will influence future recommendations. If pricing or discounting practices have been inconsistent, AI-generated quotes may reinforce those same patterns, leading to unintended consequences.

Companies can tackle this issue by:

• Conducting thorough data audits before deploying AI-powered CPQ.
• Establishing governance frameworks to continuously review and refine AI recommendations.
• Training AI models with diverse, well-structured datasets to minimize bias.

Ensuring clean, structured data is foundational to AI-driven CPQ success.

3. User Adoption and Trust Issues

AI can suggest optimal configurations, automate pricing, and generate quotes in seconds—but will sales teams trust it? Many sales professionals are accustomed to relying on experience and intuition. If AI-driven recommendations contradict their judgment, adoption can stall.

To drive adoption:

• AI suggestions should be transparent, showing the reasoning behind recommendations.
• Sales teams should be involved in training AI models to align them with real-world decision-making.
• Initial AI-driven CPQ implementations should allow manual overrides, giving users confidence in the system.

Without buy-in from sales teams, even the most advanced AI-powered CPQ system will struggle to gain traction.

4. Integration Challenges with Legacy Systems

Many manufacturers rely on legacy CPQ, ERP, and CRM systems. Implementing AI-powered CPQ often requires integrating these older systems with modern AI models, which can be complex. Compatibility issues, data silos, and outdated infrastructure can slow AI adoption.

To navigate these challenges:

• Businesses should invest in scalable CPQ solutions that offer flexible API integrations.
• IT teams should prioritize data centralization to create a unified foundation for AI insights.
• AI deployment should follow a phased approach, starting with specific CPQ functions before expanding.

Seamless integration between AI and existing sales technology is essential for long-term success.

5. Regulatory and Compliance Concerns

As AI-driven CPQ systems become more prevalent, regulatory scrutiny will increase. Privacy laws, AI transparency requirements, and industry-specific regulations could impact how companies use AI for quoting and pricing. Organizations must stay ahead of compliance risks by:

• Implementing explainable AI models that provide clear justifications for pricing and configuration decisions.
• Ensuring AI recommendations align with legal and corporate governance standards.
• Regularly reviewing AI-driven CPQ systems to ensure ongoing compliance with emerging regulations.

Regulatory uncertainty should not delay AI adoption, but businesses must proactively address compliance risks.

The Future of AI in CPQ – What to Expect by 2028

Despite these challenges, AI-driven CPQ is advancing rapidly. Here’s what we can expect over the next three years:

1. Conversational and Autonomous CPQ

By 2028, AI-powered CPQ systems will offer fully conversational interfaces. Instead of manually selecting options, sales teams and customers will interact with AI chat assistants to configure products and generate quotes in real-time. This shift will make CPQ more accessible and intuitive, reducing reliance on technical sales expertise.

2. AI-Driven Real-Time Pricing Adjustments

AI will enable dynamic pricing strategies that adapt in real-time based on market trends, competitor pricing, and supply chain fluctuations. Manufacturers will be able to adjust quotes instantly, optimizing both win rates and profit margins.

3. Hyper-Personalized Customer Interactions

CPQ systems will leverage AI to generate tailored proposals, interactive 3D product visualizations, and customized sales recommendations. AI-driven personalization will enhance the customer experience, increasing engagement and conversion rates.

4. AI-Enhanced Self-Service CPQ

AI-powered self-service quoting will become a standard feature in B2B sales. Customers will configure products, receive instant pricing, and finalize orders without needing direct sales rep involvement. This trend will accelerate sales cycles and improve efficiency.

5. Increased AI Regulation and Ethical AI Practices

As AI adoption grows, companies will need to ensure their CPQ solutions comply with stricter AI transparency and governance policies. AI-driven decision-making will require clear audit trails and oversight to maintain compliance and customer trust.

Preparing for the Next Era of AI-Powered CPQ

Businesses that start addressing AI implementation challenges today will be well-positioned to take advantage of the next wave of AI-driven CPQ innovations. To prepare for the future:

✅ Strengthen data governance and quality assurance practices.
✅ Invest in CPQ solutions that offer scalable AI integration.
✅ Train sales teams to collaborate with AI-driven recommendations.
✅ Monitor regulatory developments to ensure compliance.
✅ Continuously refine AI models based on real-world performance and feedback.

AI-powered CPQ isn’t just an upgrade—it’s a fundamental shift in how B2B sales operate. The companies that proactively tackle challenges and embrace AI-driven automation will gain a lasting competitive edge in the years ahead.

Want to discuss how AI-powered CPQ can streamline your sales process? Book a virtual coffee with Magnus or Patrik at cpq.se/meetcpqse. 🚀