The most practical AI move for complex manufacturers may be upstream of the factory floor
For manufacturers with highly configurable products, the bottleneck that slows the business is rarely on the shop floor. It is in the work that happens before production starts – turning product complexity into accurate, governed quotes quickly enough to win the order.
That is the problem Tacton’s AI Product Modeling Assistant is designed to solve.
What the tool does and why it matters
Product modeling is typically the longest and most resource-intensive phase of any CPQ implementation. Before a sales team can use configure-price-quote software to generate accurate quotes, someone has to build structured product models from engineering data – assemblies, attributes, constraints, pricing rules – by hand. In high-mix and engineer-to-order environments, that process can stretch for months, delaying go-live and limiting how much of the product portfolio is quote-ready from day one.
Tacton’s assistant works as a co-pilot for modelers rather than a fully autonomous tool. It takes inputs – spreadsheets, technical specifications, PDFs, legacy data – and generates a structured CPQ model foundation that is typically 70 to 80 percent complete. Modelers then review, refine, and approve outputs before they go live. Human validation is always required.
That last point matters. For most manufacturers in this segment, the near-term value of AI is not full autonomy. It is reducing manual effort without losing control over product logic, pricing integrity, or downstream fulfillment.
What early users report
Alimak, a global manufacturer of vertical access solutions, used the tool across multiple product lines and regions. According to the company’s group product support manager, they saw between 40 and 80 percent improvement in how quickly they could build product models – allowing the team to focus on refining configurations rather than building from scratch.
PERI SE, the Germany-based formwork and scaffolding systems manufacturer, reported a different kind of result. Their senior manager for sales solutions described a two-day modelling effort becoming a two-hour task – a shift that, in his words, fundamentally changed how quickly the business can structure new products in CPQ.
Both examples point to the same dynamic: the constraint was not engineering capability. It was the time and specialist effort required to translate that capability into a form the commercial team could use.
The investment logic this changes
For COOs, digital leaders, and sales engineering heads at complex manufacturers, the real question is no longer whether AI belongs in manufacturing. It is where AI removes delay fastest.
In many mid-sized manufacturers, that answer may be product modeling, quote logic, and engineering-to-sales knowledge transfer – not a large factory-wide program. A governed AI layer inside CPQ can improve RFQ responsiveness, expand how much of the portfolio is quote-ready, and reduce dependence on scarce modeling experts without requiring a full replacement of core systems.
That is a more practical starting point for firms that have limited automation maturity but clear commercial bottlenecks. The gains tend to show up in three places: faster time to quote, faster rollout of new configurations, and less strain on engineering teams that are already carrying too much.
There is also a structural reason this problem is getting harder to ignore. As experienced engineers retire and specialist modeling expertise becomes scarcer, the manual approach does not just stay slow – it becomes fragile. AI-assisted modeling is partly a productivity play and partly a knowledge-retention play.
The wider pattern
What Tacton is doing with CPQ modeling fits a broader shift. AI is increasingly being aimed at the coordination and knowledge work that surrounds production rather than production itself. Quote configuration, RFQ handling, engineering knowledge transfer, supplier document processing – these are the workflows where skilled people spend time they cannot afford to spend.
For manufacturers in DACH and Poland with complex products and large RFQ volumes, that is where a meaningful part of the next productivity gain is likely to come from.
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