If your CPQ system lets customers order anything they want, there’s a good chance they’re ordering things you shouldn’t be selling.
It sounds counterintuitive: after all, the whole point of a configurator is to give customers what they need, right? But in practice, many companies fall into a dangerous trap — they try to accommodate every possible customer request through endless product variations and special rules. The result? Bloated models, inconsistent margins, hard-to-deliver solutions, and quote processes that slow to a crawl.
This post explores why over-customization in CPQ is a silent killer of efficiency and profit — and how AI can help enforce smart standardization without hurting your customer experience. If your product models are starting to feel more like a wishlist than a catalog, read on.
It usually starts with good intentions. A customer asks for a small variation. Then another one asks for something similar, but slightly different. Before long, your CPQ model includes hundreds of edge-case configurations — each with custom rules, pricing exceptions, and approval flows.
The problem? Sales reps don’t always know when a request crosses from “valid” to “unprofitable.” And configurators don’t always push back. Left unchecked, over-customization leads to:
Higher production costs from one-off parts or assembly steps
Increased quote errors and approval delays
Confusion in sales teams about what can actually be sold
Complex, brittle CPQ models that are hard to maintain
It’s death by a thousand rule exceptions. And the worst part is, it’s often invisible until it affects delivery or margins.
Of course, not all customization is bad. In manufacturing, especially in engineer-to-order or configure-to-order businesses, a certain level of product flexibility is a must. The real issue is knowing where to draw the line — and being able to enforce that line systematically, not manually.
But many companies leave that decision to individual sales reps or engineers, relying on tribal knowledge or outdated Excel-based guardrails. That’s not sustainable.
What’s needed is a CPQ system that encourages valid, profitable configurations by default — and guides users away from problem areas. That’s where AI comes in.
Modern CPQ platforms like Tacton already support constraint-based configuration. But when you add AI-driven guidance on top, you get something more powerful: automated product governance.
Here’s how AI helps curb over-customization:
Pattern detection: AI can analyze historical quote data to spot which configurations are frequently customized, but rarely won — allowing you to flag or remove low-value options.
Profitability scoring: Some AI tools can assess the margin impact of specific combinations in real-time, warning reps when a config might be risky to produce or deliver.
Rule pruning: AI can identify rules that never get triggered (or that conflict with others) and suggest cleanup, helping you keep the model lean.
Smart nudges: Instead of letting users wander the entire product tree, AI can steer them toward popular, profitable configurations that match the customer’s needs — a kind of sales enablement built into the model.
This turns your configurator into more than a tool — it becomes an advisor.
One cpq.se customer, Swift Lifts, had this exact problem. Their product options had grown organically over years. Every salesperson had their own go-to set of customizations. It was slowing them down and confusing production. By introducing smart defaults and reducing unnecessary options, they simplified the model significantly — and quote turnaround times dropped. Not because the team worked faster, but because the system finally helped them sell what made sense.
Similarly, we’ve seen AI-powered suggestions in Tacton guide reps toward pre-approved bundles or pricing strategies, especially useful for companies like HMF that build highly configurable products with strict production requirements.
If you suspect your CPQ model is overgrown, here are three steps to start regaining control:
Review your quote history: Identify which configurations are frequently customized, and ask why. Are they truly needed, or are they workarounds for unclear options?
Set up a standardization policy: Define which combinations should be prioritized or defaulted — and align this with your CPQ logic.
Use AI analytics: If your CPQ platform supports it, start analyzing win/loss rates and margin by configuration type. Look for patterns. AI can often find what manual reviews miss.
Above all, resist the urge to solve customer problems by just adding more options. Sometimes, clarity beats flexibility — and the best way to serve the customer is to guide them toward the right fit, not every possible one.
Companies that control customization don’t just win on margin. They quote faster. Train new salespeople quicker. Launch new product lines without rewriting half their configurator. And they give their customers a more confident, streamlined experience.
In a world where AI can help you standardize intelligently, there’s no reason to keep carrying the weight of every special request you’ve ever granted. Start trimming. Your bottom line will thank you.
For a practical path to cleaning up your CPQ logic and building smarter guidance, explore our CPQ Analysis Workshop — we run these for manufacturers who want to scale without complexity.
And if you’d rather talk it through over a coffee, Magnus and Patrik are always up for a chat. Bring your most painful customization story — we’ve seen worse.