Enterprise software
How AI is changing cable design engineering
As cable manufacturers deal with increasing product complexity, shorter lead times, and stricter compliance requirements, artificial intelligence is starting to play a more practical role in engineering workflows. According to InnoVites, the most valuable application of AI in cable design is not autonomous cable creation, but faster access to engineering knowledge and validation processes.
From engineering expertise to engineering intelligence
Traditional cable design workflows still rely heavily on historical know-how and manual processes. When a new specification arrives, engineering teams typically need to:
- search for similar existing designs
- rebuild calculations
- verify applicable standards
- review material options
- confirm manufacturability with production teams
While effective, this approach can become increasingly time-consuming as cable portfolios grow more complex. According to InnoVites, much of the required knowledge already exists inside cable manufacturers’ systems and archives, but it is often fragmented across spreadsheets, PDFs, ERP platforms, test reports, and internal documentation.
AI as a support tool for cable engineering
InnoVites highlights that AI can help engineering teams retrieve and connect this information more efficiently, supporting decision-making by helping teams:
- identify comparable cable constructions
- compare material and design alternatives
- accelerate quotation and planning activities
This approach becomes particularly relevant in cable manufacturing, where design choices directly affect compliance, scrap rates, material costs, and production feasibility. In copper-conductor cables, raw material alone can account for 70–80% of total cost, making even small design or data errors potentially expensive.
Connecting design and manufacturing
Another key aspect highlighted by InnoVites is the growing need to connect cable design activities with manufacturing operations. Modern engineering workflows increasingly depend on the integration of design data with routing, costing, simulation, quality assurance, and ERP/MES systems.
For example, when developing a modified medium-voltage cable for a new installation environment, engineering teams may need to review previous designs, verify machine capabilities, and validate standards compliance before production can begin. AI-supported engineering intelligence can help streamline these activities by surfacing relevant information faster and reducing repetitive manual work.
Supporting engineering, not replacing it
According to InnoVites, the future role of AI in cable manufacturing will depend less on replacing engineers and more on strengthening engineering workflows through better data accessibility, validation tools, and production connectivity.
As digitalization continues across the wire and cable industry, engineering intelligence is increasingly becoming a key factor in improving responsiveness, consistency, and manufacturing efficiency.
