AI CPQ: What’s coming and how it will redefine revenue workflows

Configure-Price-Quote (CPQ) software has become a cornerstone of modern sales and monetization workflows. By centralizing product catalogs, pricing rules, and contract templates, CPQ tools help reps generate accurate quotes in minutes.

But there isn’t a lot of AI functionality in CPQ (yet).

That said—the CPQ market is changing.

Currently, it’s on a steep upward curve, growing from $7.4 billion in 2022 to an expected $11.2 billion by 2026, according to MGI Research.

This surge is driven by the rise of AI-powered automation and the shift toward usage-based pricing models. With 56% of SaaS companies now using consumption-based pricing, traditional quoting tools are struggling to keep up.

It’s a fact: clunky CPQ tools slow down sales.

Now imagine this: faster deal configuration, predictive pricing, automated approvals, and infallible quote accuracy.

That’s the impact of the next generation of AI CPQ software.

In this blog post, we look at the difference between traditional CPQ and AI CPQ, emerging features, and how it’ll change revenue workflows—for good.

What is CPQ?

​​CPQ stands for Configure-Price-Quote, and is a system that organizes your product catalog and pricing in one central place. This provides a representation of the monetization rules that govern product packaging, support pricing and margin discipline, and manage timely and comprehensive quote creation, review, approval, submission, and tracking.

CPQ platforms integrate with CRMs in the front end and ERP in the backend to make the sales process streamlined, faster, and organized—making sure everyone’s working based on the same data.

Traditional CPQ vs. AI CPQ

Historically, CPQ solutions were big, monolithic platforms (think Salesforce CPQ and Oracle CPQ) tailored to standard license sales. They were powerful but often slow to deploy, costly to customize, and rigid about pricing models. Plus, any change usually required an IT ticket.

By contrast, a new wave of AI CPQ platforms is emerging for SaaS and usage-driven businesses. These are built cloud-first, with simple interfaces, and seamless billing integration from day one. 

In practice that means you get real-time usage metering, no-code pricing changes, and instant deployment. Companies like Alguna (and others such as Subskribe or Nue) are already betting that combining CPQ and billing into one AI-ready system is the future.

In short, whereas legacy CPQs focus on rigid rules, AI CPQ will be designed to learn and adapt based on your sales workflow.

The promise of AI in CPQ: 4 emerging features to look for

While true AI-driven CPQ remains a work in progress, a handful of real, tangible capabilities are starting to surface as platforms like Alguna and Zenskar are offering features like AI-enabled contract uploads..

These are just early signs of how AI is beginning to shape the quoting process. 

From smarter automation to adaptive pricing models, here are some of the most promising features gaining traction—and why RevOps and finance teams should be paying close attention.

• Dynamic pricing and bundling: Some platforms are experimenting with AI-driven pricing recommendations. Given a deal’s attributes, the system might propose which products or bundles to include, or what discount to apply, based on similar past deals. Over time these suggestions will get smarter as the AI trains on win/loss history.
• Automated approvals and deal scoring: Rather than blanket manual sign-offs, AI CPQs will be increasingly equipped to flag only risky deals. For instance, they might highlight deals with unusually deep discounts or terms, routing only those for extra review. In theory, a future AI CPQ could even score each quote for win probability or margin impact.
• Guided selling: Interactive proposal generators (“guided selling”) are also maturing. Some CPQ platforms already include AI chat assistants that help reps configure quotes. While these are not full AI chatbots (yet), these features make the quoting process more intuitive.
• Predictive features: AI could also drive new predictive features. Imagine the system scoring each deal and predicting its win probability or ideal price similar to lead scoring in CRM. Or picture AI analyzing customer usage patterns to suggest churn-preventing upsells.

These are budding use cases in the pipeline. Some startups hint at real-time AI deal scoring, or chat-assistant interfaces for quoting, even if most CPQs don’t do that just yet.

10 ways AI CPQ will change revenue workflows

1. Faster deal turnaround
   AI streamlines quote creation by recommending optimal configurations and pricing instantly. What used to take hours (or days) now takes minutes.
2. Smarter discounting and pricing
   AI learns from past deals to suggest ideal price points and discount strategies based on what actually closes — reducing guesswork and improving margins.
3. Approval bottlenecks, eliminated
   Instead of routing every quote for manual review, AI flags only outliers — saving time and accelerating deal velocity without compromising control.
4. Live usage-based quoting
   AI-powered CPQ can ingest real-time product usage (API calls, compute hours, transactions) and reflect that instantly in quotes and billing — critical for modern SaaS, AI, and fintech.
5. Predictive deal intelligence
   AI can score deals, forecast likelihood to close, and suggest adjustments — giving RevOps real visibility into pipeline quality and risk.
6. Error reduction through automation
   By automating rule validation, AI CPQs catch inconsistencies before a quote goes out — preventing downstream billing mismatches and revenue leakage.
7. Finance-ready from day one
   Modern AI CPQs integrate with billing and rev rec systems, ensuring quote terms align with how revenue is recognized — no manual reconciliation needed.
8. No-code agility
   RevOps and finance teams can update pricing logic, launch promotions, or test new billing models without engineering — AI helps validate and simulate changes before launch.
9. Better customer experience
   Quotes become more accurate, transparent, and tailored — which builds trust, reduces back-and-forth, and shortens the sales cycle.
10. Scales with complexity
    As your business grows — more products, currencies, entities, pricing models — AI CPQ systems learn and adapt, reducing the admin burden and risk of human error.

3 platforms leading the next wave of AI-powered CPQ

1. Alguna: AI monetization platform with AI-enabled contract uploads

Alguna is an AI monetization platform that offers a no-code CPQ that's built from the ground up for usage-based, credit-based, and outcome-based pricing models.

• Why it leads: Unified quote-to-cash (CPQ + billing + rev-rec) with no-code pricing and usage metering in one platform.
  
  ✨ Recently added AI for contract uploads, meaning AI will extract customer details, products and plans, billing cadence, payment terms, and renewals without reps having to do any manual entry.
  
• Ideal for: Scaling SaaS, fintech, and AI companies that need flexibility and automation without engineering bottlenecks.
• Edge: Ships features faster than competitors (especially legacy players), combining configuration intelligence with real-time revenue data.

2. Subskribe — Deal desk automation with early AI guidance

Subskribe focuses on simplifying quote-to-revenue operations for subscription and SaaS businesses.

• Why it leads: Its DealDesk AI assists with pricing scenarios and policy compliance — surfacing the best discount and billing options in real time.
• Ideal for: Finance-driven SaaS companies needing tight alignment between quoting, billing, and revenue recognition.
• Edge: Strong analytics layer for forecasting ARR/MRR and improving deal structure decisions.

3. DealHub: AI-enabled guided selling for revenue teams

DealHub combines CPQ, CLM, and buyer engagement in one digital sales room experience.

• Why it leads: Uses guided selling logic and engagement insights to help reps build smarter proposals and accelerate closing cycles.
• Ideal for: Mid-market B2B sales teams prioritizing collaboration, buyer visibility, and fast implementation.
• Edge: Blends light AI analytics (deal insights, engagement tracking) with a user-friendly interface that speeds time-to-value.

Now’s the time to move to AI CPQ innovators

The CPQ process matters because quoting is critical to revenue velocity and AI could make it a lot smarter. Most tools are busy building and we’ll likely see some of the features mentioned sooner rather than later.

RevOps and finance leaders should take stock of their pain points (error-prone quotes? slow approval bottlenecks? complex usage billing?) and watch AI CPQ developments closely as AI monetization platforms (like Alguna) are sprinting toward the vision of intelligent, self-optimizing quoting.

Ultimately, even if your current CPQ feels adequate, it will soon fall short and you could lose deals based on quote turnaround time alone.