CPQ Podcast

How much should you know about CPQ before relying on software vendors, consultants or implementation partners to guide your next steps?

In this episode, Frank Sohn discusses why CPQ buyers should first learn enough to ask good questions, challenge assumptions and look at the market from more than one perspective.

He also introduces the new Novus CPQ Advisor, an independent starting point for exploring CPQ vendors, solutions, research and next steps. The Advisor can help people who are new to CPQ as well as experienced CPQ stakeholders who want another perspective, information on a vendor, or help finding relevant Novus resources.

The Advisor is not intended to provide the final answer to every CPQ question. Instead, it gives users a place to start, learn and explore before deciding where deeper research, technical expertise or direct advisory support may be useful.

Try the Novus CPQ Advisor: novuscpq.com/#cpq-advisor

Don't forget to subscribe on YouTube and your favorite podcast platform. To stay up to date with the CPQ Podcast and the latest Novus CPQ research and insights, visit novuscpq.com.

In this episode of the CPQ Podcast, Frank speaks with Antonio Brkic, Chief Product Officer at Helioz Technologies, about the evolution of zipCPQ into HeliozOne and the company's broader vision for configurable product sales.

Antonio explains why Helioz is moving from a CPQ-focused solution toward a modular platform that can support product configuration, pricing, quoting, e-commerce, spare parts, document generation, production quoting, and integrations. He also discusses what this transition means for existing zipCPQ customers, noting that current solutions and support remain in place and that adoption of additional HeliozOne capabilities is customer-driven rather than mandatory.

The conversation explores several areas that are becoming increasingly important for manufacturers and other companies selling configurable products, including:

• AI-assisted product configuration and PDF-to-configurator workflows
• 2D and 3D product visualization
• CAD integration, including Onshape
• CRM, ERP, accounting, and e-commerce integrations
• API-based data exchange and modular architecture
• Production quoting and cost calculation
• Self-service CPQ for small and midsize businesses

Antonio also shares Helioz' approach to AI: using artificial intelligence to suggest, extract, and accelerate work while keeping people involved in reviewing and validating the results. He discusses growing customer interest in AI-driven configuration, sales guidance, and converting information from PDFs into structured configurator data.

Frank and Antonio also look at where 2D and 3D visualization add the most value, how Helioz integrates with systems such as Salesforce, HubSpot, Microsoft Dynamics, and accounting platforms, and what types of organizations are the best fit for HeliozOne.

If you are evaluating CPQ software, working with complex or configurable products, or interested in how AI, visualization, CAD, and integrations are changing Configure-Price-Quote processes, this episode provides a practical look at the evolution of a modern CPQ platform.

Learn more: Antonio Brkic on LinkedIn | HeliozOne | Helioz Technologies

Don't forget to subscribe on YouTube and your favorite podcast platform. To stay up to date with the CPQ Podcast and the latest Novus CPQ research and insights, visit novuscpq.com.

Copilot, assistant, agent — the terms get thrown around interchangeably, but they describe three very different levels of AI autonomy. So where does agentic AI actually belong in the configure-price-quote process, and where should humans stay firmly in control? In this episode, Frank sits down with Sourabh Kukar, Director of Technical Consulting at Salesforce, where he leads a team of enterprise architects working across Revenue Cloud, Agentforce Revenue Management, CPQ, and H&A solutions for large enterprise customers. Sourabh was named Salesforce's FY24 CMT Technical Architect of the Year, holds 23 industry certifications, and is an IEEE Senior Member. Sourabh brings a rare mix of backgrounds to the conversation — starting as a Java developer, spending a decade as an integration architect, then moving deep into the Salesforce platform in 2019. That combination of coding roots, systems breadth, and platform depth shapes how he thinks about which parts of quote-to-cash are ready for real AI autonomy and which still need deterministic guardrails and human sign-off. Frank and Sourabh also dig into how to architect multiple specialized agents so one agent's bad output doesn't become another agent's bad input, why fixing your data and simplifying your processes matters more than the AI layer itself, and what changes for revenue operations teams as quoting and approvals move into conversational tools like Slack.

In this conversation we cover:

• The real difference between a copilot, an assistant, and an agent — and where Salesforce's Agentforce quoting agent fits
• Which parts of configure-price-quote are ready for high AI autonomy, and where humans need to stay in the loop
• How to architect multiple specialized agents so errors don't cascade downstream
• Why fixing your data and simplifying your processes matters more than the AI layer on top
• What changes architecturally when quoting and approvals move into Slack
• A practical framework for choosing phased coexistence over a full CPQ-to-Revenue-Cloud migration

If you're a Salesforce CPQ customer evaluating a move to Revenue Cloud, an enterprise architect thinking through agentic AI governance, or a revenue operations leader trying to separate real AI capability from hype, this episode offers grounded, practitioner-level insight from someone building these systems for enterprise customers every day.

Learn more: Sourabh Kukar on LinkedIn

Don't forget to subscribe on YouTube and your favorite podcast platform.

More CPQ research and resources at https://novuscpq.com

Renewals are becoming a strategic revenue priority — but why do so many companies still manage them through disconnected systems, spreadsheets, emails, and partner portals?

In this episode, Frank talks with Ram Rai, founder & CEO of Yagna iQ, and Manish Agrawal, Co-Founder & CTO, about how recurring revenue and renewal management are changing as companies move toward subscription and lifecycle-based business models. They discuss why renewals become particularly challenging in indirect sales channels, where OEMs, distributors, resellers, and end customers may all participate in the process.

Ram and Manish explain Yagna's approach to channel CPQ and renewal automation, including how renewal processes can extend beyond simple expiration reminders to customer onboarding, adoption monitoring, account health, value realization, quoting, and expansion opportunities. They also discuss how Yagna integrates with existing CRM, ERP, and CPQ environments rather than replacing them.

The conversation also explores the growing role of agentic AI in renewals and quoting—from processing supplier quotes and automating RFQs to AI-assisted customer outreach—as well as where human oversight, data readiness, integration, and governance remain important.

In this conversation we cover:

• Why renewal management is moving from a transactional process to a strategic revenue discipline
• Why indirect channels create unique CPQ and renewal challenges across OEMs, distributors, and resellers
• How Channel CPQ can complement existing vendor-side CPQ systems
• Where agentic AI, voice agents, and automated quote processing can support renewal workflows
• Why data cleansing, CRM/ERP/CPQ integration, governance, and human-in-the-loop processes remain important
• How automation can help address the "Long-tail" of lower-value, high-volume renewals that are often uneconomical to manage manually

If you work in CPQ, Revenue Operations, recurring revenue, channel sales, or partner management, this episode provides a useful look at how renewal automation is expanding beyond reminders and traditional quoting — and how AI may change the economics of managing recurring revenue at scale.

Learn more: Yagna iQ Yagna iQ AI · Ram Rai on LinkedIn · Manish Agrawal on LinkedIn · Yagna iQ Customer Pulse Report – Recurring Revenue and Renewals in 2026

Don't forget to subscribe on YouTube and your favorite podcast platform.

More CPQ research and resources at https://novuscpq.com

Where does traditional CPQ stop—and where does engineering automation need to take over?

In this episode, Frank sits down with Scott Heide, CEO of Engineering Intent, to explore how CPQ can support products that require far more than standard configuration, pricing, and quoting. Scott brings more than 30 years of experience working with engineer-to-order companies, knowledge-based engineering, and sales engineering automation.

Returning to the CPQ Podcast for the first time since 2023, Scott explains why customers typically approach Engineering Intent when their products involve complex engineering calculations, dynamic geometry, visual configuration, detailed bills of materials, or automatically generated sales and manufacturing drawings. He also discusses KBridge, Engineering Intent's design automation platform, and how it can connect the commercial CPQ process with the engineering work required to produce a valid custom design.

Frank and Scott also examine Engineering Intent's new direct Salesforce integration, its established relationship with ServiceNow, and the growing role of systems integrators in projects that combine CRM, CPQ, and engineering automation. Scott shares how Engineering Intent is using AI to accelerate the capture of product knowledge and design rules—while maintaining the repeatable, deterministic, and documentable outputs required in manufacturing.

In this conversation, we cover:

• Why traditional CPQ solutions may struggle with highly engineered and graphically complex products

• Where CPQ ends and engineering or design automation begins

• How KBridge generates dynamic 3D models, visualizations, drawings, calculations, and detailed bills of materials

• The difference between fixed-geometry visualization tools and parametric models that change dynamically

• Engineering Intent's new Salesforce integration and how KBridge can work alongside Salesforce Revenue Cloud

• How AI may reduce the time and effort required to capture and automate product-specific engineering rules

• Recent capabilities including advanced solid modeling, animation, source merge, and offline runtime

• Engineering Intent's growing focus on material handling, MEP, precast concrete, and other vertical applications

• Why systems integrators may need stronger engineering skills to implement advanced design automation successfully

• What Engineering Intent plans to demonstrate at Dreamforce and other upcoming industry events

If you work with engineer-to-order products, visual product configuration, manufacturing CPQ, or complex sales and engineering processes, this episode provides a practical look at how CPQ and design automation can work together across the full lifecycle—from early sales visualization to manufacturing-ready drawings and bills of materials.

Learn more about Engineering Intent and connect with Scott Heide on LinkedIn.

Don't forget to subscribe on YouTube and your favorite podcast platform.

More CPQ research and resources at https://novuscpq.com

Salesforce CPQ is officially end of sale — but what does that actually mean for your business, and where do you go from here?

In this episode, host Frank sits down with Jamie Trigg and Timo Taipale from ShiftLogic.io to unpack the shift from Salesforce CPQ to Agentforce Revenue Management (ARM), and why this transition is a full revenue transformation, not just a platform swap. Jamie brings nearly 20 years of CPQ experience, from the early days at Big Machines and Steelbrick through a long run in Salesforce Revenue Cloud. Timo shares his journey as a self-described "accidental admin" who chose Revenue Cloud over Steelbrick CPQ during a greenfield implementation, long before joining ShiftLogic.io.

Frank, Jamie, and Timo also examine why "end of sale" doesn't mean "end of life," and why experienced CPQ practitioners often find ARM the hardest transition to make — thanks to a deeply ingrained "quote-centric" mindset that doesn't always translate cleanly to a revenue-centric world.

In this conversation we cover:

• Why the real urgency behind ARM adoption comes from customers wanting more capability, not from Salesforce forcing a switch
• Why experienced CPQ practitioners often struggle most with the shift to ARM
• How to tackle technical debt and legacy CPQ rules with a "vision lock" approach
• The difference between engineering CPQ and sales CPQ, and where ARM fits for manufacturing and complex products
• How AI and Agentforce adoption is accelerating across CPQ and revenue lifecycle management
• Real customer adoption patterns, typical implementation timelines, and what to expect from a Salesforce CPQ to Revenue Cloud migration

If you're a Salesforce CPQ admin evaluating your next move, a revenue operations leader planning an ARM implementation, or simply curious about where CPQ and revenue management are headed, this episode offers grounded, practitioner-level insight from two people who've lived it on both sides of the table.

Learn more: ShiftLogic.io · Jamie Trigg on LinkedIn · Timo Taipale on LinkedIn

Don't forget to subscribe on YouTube and your favorite podcast platform.

With Javed Jafar, CPQ and Billing Expert with 20+ years of experience across enterprise implementations, advisory work, and customer-side transformation programs.

How will AI change CPQ for customers, vendors, system integrators, and CPQ professionals?

In this episode, Javed shares his perspective on why AI is not replacing CPQ expertise, but changing what good CPQ expertise looks like. Routine configuration work, documentation, testing, and delivery tasks are becoming easier to accelerate with AI tools. At the same time, deep domain knowledge is becoming more important, since customers still need experts who can recognize incorrect AI output, manage complexity, and translate business requirements into scalable commercial processes.

Frank and Javed also discuss how AI is changing expectations for system integrators, as customers increasingly expect more value for every professional services dollar — a shift that may push the market toward fixed-price offerings, reusable accelerators, boutique specialists, and AI-enabled delivery models. They also examine the current business climate around AI adoption, including why many organizations are investing in AI tools but still struggle to demonstrate measurable ROI. In CPQ specifically, Javed sees many companies experimenting with automation and AI assistance, while true agentic CPQ adoption is still in the early stages.

In this conversation we cover:

• Why AI is changing what good CPQ expertise looks like, not replacing it
• How AI is accelerating configuration, documentation, testing, and delivery work
• Why deep domain knowledge matters more as AI output needs expert review
• How AI is reshaping expectations and pricing models for system integrators
• The gap between AI investment and measurable ROI in CPQ today
• The state of agentic CPQ adoption and Salesforce Agentforce Revenue Management
• The impact of legacy CPQ end-of-sale messaging
• The shift from rule-based to constraint-based thinking
• What types of customers benefit most from AI-first, API-driven revenue architecture

If you are a CPQ customer, part of a Salesforce Revenue Cloud team, a system integrator, a CPQ vendor, or a revenue operations leader trying to understand how AI will reshape Configure, Price, Quote over the next few years, this episode offers a grounded, experience-based perspective.

Learn more: Javed on LinkedIn

Can AI actually replace the sales engineer in complex product configuration — without sacrificing accuracy? In this episode of the CPQ Podcast, host Frank sits down with Stanislav "Stan" Chirk, founder of R[ai]sing Sun, an EU-based AI solutions company building custom AI agents for B2B companies. Stan walks us through his journey from engineering and entrepreneurship into the world of Configure, Price, Quote (CPQ), and how that combination led him to found R[ai]sing Sun just over a year ago. We dig into the company's "AI-native" philosophy — building AI-first rather than retrofitting AI onto legacy systems — and their guiding principle: no AI for the sake of AI. The conversation centers on R[ai]sing Sun's flagship product, Talkulate AI CPQ, a self-service configurator that guides buyers through complex product decisions and delivers instant, accurate quotes — no sales engineer required. Frank pushes Stan on one of the biggest questions in AI-driven CPQ: how do you guarantee deterministic, repeatable outputs when generative AI is involved? Stan explains how structured product databases and rule-based logic keep quote generation accurate, even while the upfront sales conversation stays flexible. You'll also hear about R[ai]sing Sun's phased delivery model (including a bold promise to walk away from projects that don't hit KPIs), a typical 3–5 week go-live timeline, their traction in medical and engineering industries, and how AI-native CPQ stacks up against established, traditional platforms. Whether you're a CPQ practitioner, a sales operations leader, or simply curious about how AI agents are reshaping B2B quoting and configuration, this episode offers a grounded, practical look at where AI-powered CPQ is headed.

🎥 Stick around for a separate demo video showing Talkulate AI CPQ in action, configuring a real-world "home media server" request from scratch.

🔗 Learn more about R[ai]sing Sun at https://r-sun.ai/

📬 Contact Stan on LinkedIn at https://www.linkedin.com/in/stani-chirk/

Can you trust an AI agent to configure your products, apply your pricing rules, and generate an accurate quote — every single time? According to Dr. Sanjay Mittal, founder of Predictika.ai and one of the original pioneers of constraint-based product configuration, the answer right now is: not without guardrails.

In this episode of the CPQ Podcast, Sanjay breaks down one of the most underappreciated risks in enterprise AI adoption — logic hallucinations. Unlike factual hallucinations, where an AI simply makes up information it doesn't have, logic hallucinations happen when an AI has all the right facts but still applies your business rules, constraints, and pricing policies incorrectly. For CPQ, where 100% correctness and completeness are non-negotiable, that gap can mean revenue leakage, incompatible configurations, and costly disputes with customers.

Sanjay speaks from deep experience. He founded Selectica in 1996, building constraint-based CPQ solutions for Cisco, IBM, GE, and Dell — guaranteeing 100% configuration correctness at scale. Cisco ran on their platform for 15 years. Today, at Predictica.ai, he's applying the same deterministic reasoning engine as a co-pilot alongside large language models, letting LLMs handle natural conversation while a constraint engine validates every output before it reaches the customer.

In this conversation we cover:

• The difference between factual and logic hallucinations in LLMs
• Why probabilistic AI and deterministic CPQ are fundamentally mismatched
• What "correct and complete" really means in product configuration
• How a deterministic co-pilot architecture bridges that gap
• What questions enterprise buyers should ask any CPQ vendor adding AI

If you're evaluating AI-powered CPQ, building one, or just trying to understand where the hype ends and the real engineering begins — this episode is for you.

🔗 Learn more about predictika at https://predictika.com/

📬 Contact Sanjay on LinkedIn at https://www.linkedin.com/in/sanjaymittal/

In this episode of the CPQ Podcast, Frank Sohn speaks with Tiffany Devlin-Drye, Head of Solution Architecture at Roadrunner.ai, about Roadrunner's view of the CPQ market and its AI-native approach to quoting, approvals, and revenue operations. Tiffany shares her background as a Salesforce CPQ developer, solution architect, and former system integrator before joining Roadrunner.ai. She explains why Roadrunner describes its platform as PQA — Prompt, Quote, Approve — and why the company believes traditional CPQ is being challenged by a new generation of AI-native quote automation platforms. The conversation covers Roadrunner's standalone platform, its Salesforce managed package, and its out-of-the-box connectors for systems such as Ironclad, DocuSign, NetSuite, Zuora, and Slack. Tiffany also discusses how prompt-based quoting can support complex scenarios, usage-based pricing, multi-year deal structures, real-time calculations, and approval workflows. A key topic is Roadrunner's implementation model. Tiffany explains why the company currently does not rely on system integrator partners and instead provides implementation teams directly as part of the first-year license. She also describes how Roadrunner uses AI to analyze existing CPQ implementations, requirements documents, and configuration logic to accelerate project delivery. This episode also explores the difference between AI-native and AI bolt-on approaches, the role of deterministic rules in preventing hallucinations in quoting, and why early Roadrunner customers include large SaaS and AI companies with complex pricing and approval requirements. For anyone evaluating the future of CPQ, Salesforce CPQ alternatives, AI-native quoting, quote-to-cash automation, or usage-based pricing, this episode provides a timely look at one of the newer entrants trying to redefine the category.

🔗 Learn more about Roadrunner at https://roadrunner.ai/

📬 Contact Tiffany on LinkedIn at https://www.linkedin.com/in/tdevlindrye/