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- Outdated, Manual Delivery Processes – Legacy quoting methods and processes that drag out releases and hurt the business.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. Let’s dive into the details of Unstructured Configuration Data Stifling AI.
4. Unstructured Configuration Data Makes AI Hard to Operationalize
The rise of Artificial Intelligence (AI) presents big opportunities to improve CPQ – from intelligent product recommendations to automated pricing optimizations. However, many organizations can’t take advantage of AI in CPQ because their configuration and sales data are unstructured or siloed. AI algorithms feed on well-structured, consistent data.
Unfortunately, CPQ rules and product data in many companies reside in spreadsheets, free-form documents, or disparate systems, making it difficult to harness for machine learning or even basic automation.
Think about a global manufacturer’s product catalog: they might have thousands of configurations defined in PDF manuals or an engineer’s notebook language. Until that data is structured (e.g. in a CPQ rules engine or a database), an AI can’t learn from it.
As a CPQ expert noted, manual quote documents or homegrown tools are often designed for humans to read, “and therefore aren’t structured in a way that lends themselves to automation”.
For example, if sales reps have been writing custom deal configs in email threads or if pricing approvals live in Word documents, an AI can’t easily parse that historical data to find patterns. In life sciences and healthcare tech sales, we’ve seen reps maintain “shadow” spreadsheets for bespoke customer configurations – again, essentially dead data from an AI standpoint.
Even within a CPQ system, data inconsistency can be an issue. If a company hasn’t standardized names, units, or option codes, the data might be technically structured in tables but semantically messy.
One implementation study found that configuration data is often “inconsistent, incomplete, or redundant”, which leads to incorrect quotes and would likewise confuse any AI model.
Unstructured data can also mean disconnected data – for instance, if your CRM, ERP, and eCommerce store each hold pieces of the configuration puzzle, it’s hard to apply AI holistically. (Leaders can’t get visibility either: “when data is spread out over different systems… the likelihood of errors increases and quote generation becomes far less efficient,” which also hampers strategic planning valorx.com.)
Why It Matters
AI and advanced analytics thrive on clean, unified datasets. If companies cannot operationalize AI in CPQ, they miss out on substantial benefits – like recommending optimal product bundles, forecasting demand for configurations, or using generative AI to speed up rule creation.
For instance, modern AI-powered CPQ engines can now analyze historical configurations and suggest new product rules or optimizations automatically. But without structured data, those algorithms have nothing to learn from.
Additionally, unstructured configuration data perpetuates manual work. Human users might decipher a messy quote or a complex product note, but as one CEO put it:
Automation works by interpreting well-formed data and taking action upon it”, which means your data foundation has to be solid.
In summary, companies with poor CPQ data practices find themselves unable to leverage AI and even basic automation – leaving potential efficiency gains on the table. This is increasingly a competitive disadvantage as peers invest in AI-driven selling tools.
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. Second, the integration into ServiceNow’s ecosystem addresses fragmented tooling. ServiceNow positions itself as a platform of platforms, and by adding CPQ, they extend their workflow and DevOps capabilities to cover quoting. This means Sales Ops teams could use the same toolchain as the rest of IT – breaking down the silos.
Easier Data Flows
ServiceNow’s CRM & workflow modules connected with CPQ ensure that quoting isn’t a standalone island. For example, approvals can leverage ServiceNow’s process engine rather than a custom solution, and product data can tie into a single data model. ServiceNow’s EVP of CRM workflows highlights that the goal is “a fundamentally different approach to traditional CRM – one that addresses the real pain points in end-to-end customer experiences”.
In practice, this means eliminating those disconnected approval chains and spreadsheet handoffs. A salesperson, a partner reseller, or a field service tech could all operate on the same CPQ interface via ServiceNow, ensuring consistency and speed.
Finally, the Logik.ai + ServiceNow combination is explicitly focused on scalability and AI, which attacks the remaining challenges. Logik.ai’s engine “solves for speed, simplicity, and scale” in even the most complex configurations.
ServiceNow’s AI Capabilities
ServiceNow’s AI capabilities (they brand themselves “the AI platform for business transformation”) applied to CPQ can enable real-time analytics and recommendations across the quote-to-cash process. The companies have noted that together they want to deliver “unparalleled speed and efficiency through the full sales lifecycle” with greater simplicity and scale across the entire process.
This could mean, for instance, real-time coaching for sales reps on optimal configurations, or instant analysis of quote trends – things that are very hard when data is buried in unstructured forms. From an industry perspective, these modern solutions are particularly beneficial in manufacturing, high-tech, and life sciences domains. These are exactly the areas where legacy CPQ struggles: high product complexity, lots of data silos, and a need for agility.
A modern, AI-infused CPQ on a unified platform can offer “consumer-grade experiences that are simple, scalable, fast, and accurate” even for highly complex B2B sales.
Early signs are promising – companies using Logik.ai’s approach have reported faster time-to-quote and fewer errors, and the acquisition by ServiceNow is expected to accelerate innovation in the space.
A Modern CPQ Strategy Is a Business Imperative
B2B configuration and CPQ delivery have long been slowed by a perfect storm of challenges: not enough skilled people, clunky manual processes, patchwork tools, and underutilized data. These challenges have very real impacts – from delayed quotes and lost deals to overworked CPQ admins and stale pricing.
The good news is that the industry recognizes these pain points. Solutions like Logik.ai’s AI-driven CPQ (now bolstered by ServiceNow’s platform) are emerging to directly address talent gaps, modernize delivery workflows, standardize tooling, and unlock data for AI.
For organizations embarking on CPQ initiatives, it’s critical to tackle these challenges head-on. Invest in training and upskilling to grow the talent pool (or leverage partners who have it). Aim to eliminate manual quote steps by mapping out your quote process and applying automation where possible.
Push your vendors or IT team to implement a proper DevOps strategy for CPQ – even if it means bringing in third-party tools to fill the gaps. And lay the groundwork for AI by cleaning and centralizing your product and pricing data.
"The difference is dramatic: companies that modernize their CPQ delivery see faster sales cycles, higher quote accuracy, and greater scalability in their sales operations.”
In a world where B2B buyers expect instant, error-free quotes and seamless service, addressing these CPQ challenges isn’t just an IT project – it’s a strategic imperative for revenue growth and customer satisfaction.
Ultimately, the future of CPQ looks bright. With next-generation platforms reducing complexity and leveraging AI, we can expect CPQ delivery to become faster and more reliable.
The companies that embrace these changes will turn what was once a slow, painful process into a competitive advantage – configuring and quoting the right product at the right price, at the speed of modern business.
Ready to modernize your CPQ approach? Speak with Zaelab’s experts to explore how your organization can overcome these challenges and accelerate time-to-value.
