B2B companies today rely on Configure-Price-Quote (CPQ) solutions to sell complex products and services. Platforms like Logik.ai (now part of ServiceNow), Salesforce CPQ, Oracle CPQ, and SAP CPQ promise faster quotes and fewer errors. Yet many organizations still struggle with CPQ project delivery. We’ll explore four real-world challenges that slow down CPQ implementations and daily operations – and why modern approaches are emerging to tackle them.
We’ll focus on data and examples from Manufacturing, High Tech, and Health & Life Sciences, and consider how Logik.ai’s integration with ServiceNow can help overcome these hurdles.
Major challenges include:
- Shortage of CPQ-Skilled Talent – Limited availability of experienced CPQ implementers in the U.S. and globally.
- Outdated, Manual Delivery Processes – Legacy quoting methods and processes that drag out releases and hurt the business.
- Fragmented Tooling and Siloed Implementation – Lack of standardized, scalable toolkits in the CPQ ecosystem, leading to inefficiencies.
- Unstructured Configuration Data Stifling AI – Difficulty applying AI/automation because product and pricing data are not properly structured.
Each of these pain points can derail a CPQ initiative. In our blog post series, we'll dive into the details of each challenge and how they manifest in practice, starting with point one:
1. CPQ Talent Shortages: A Specialized Skills Gap
One of the biggest hurdles in CPQ delivery is simply finding people who know how to do it. CPQ implementations are complex, and seasoned experts are in short supply. According to industry analysts, talent shortage is the single biggest threat to growth in the Salesforce/CPQ ecosystem – demand for projects is outpacing the supply of skilled consultants. In other words, many companies want CPQ, but there aren’t enough qualified implementers to go around.
This skills gap is evident in the Salesforce world. Salesforce’s own “Revenue Cloud” (CPQ & Billing) specialists are “in high demand and short supply,” driving up salaries for those with the expertise. Consultants who hold the CPQ Specialist certification command premium rates, especially since many firms in tech, manufacturing, and telecom are eager to streamline complex quote-to-cash processes. The niche nature of CPQ means fewer professionals have deep experience in it – it’s often called one of the most complex tools to implement, and “it is often impossible for anyone who is not experienced in it to configure [CPQ tools]” properly.
The result is a war for talent: companies may post CPQ roles for months without finding the right fit, or they must pay a premium for consultants with the rare blend of product configuration and systems knowledge. This talent crunch isn’t limited to the U.S.; globally, the pool of CPQ experts is limited, and many SI firms turn to offshore teams to fill the gap when possible.
Why it matters: A shortage of skilled CPQ implementers leads to longer project timelines and higher costs. If you can’t find an expert to design your product rules or pricing logic, your CPQ rollout can stall for months. In some cases, projects are delayed or even abandoned because companies “can’t find the experts to do the work”, as one ecosystem report noted. The talent gap also means less knowledge transfer – if only one person on the team truly understands the CPQ configuration, it creates a risky dependency. All of this slows down delivery and makes scaling CPQ across the business more challenging.
Modern Solutions on the Horizon: Logik.ai and ServiceNow’s Approach
It’s not all doom and gloom – the industry is actively addressing these challenges. A notable development is ServiceNow’s acquisition of Logik.ai, an AI-powered CPQ platform. This move signals a push toward more unified, modern CPQ solutions that tackle the very issues we’ve outlined. By integrating Logik.ai’s configuration engine into the ServiceNow platform, the aim is to create a seamless experience where sales, fulfillment, and service all coexist on a single, scalable platform.
How might this help? First, Logik.ai is built with AI and ease-of-use in mind, lowering the skill barrier. Its rule engine allows point-and-click setup and even natural language input for product rules (using NLP), meaning you don’t always need a niche CPQ developer for every change. This could alleviate talent shortages by enabling a broader pool of admins to manage CPQ, and by automating some of the heavy lifting.
In short, an AI-assisted CPQ can transfer some expertise from human to machine, speeding up implementations.Are you ready to tackle the next three major challenges slowing down B2B configuration and CPQ delivery? Check back next week for our continuing series. If you just can’t wait, reach out to one of Zaelab’s B2B experts for our deep dive on CPQ delivery.
