shadow AI in wire & cable manufacturing: why clean production data matters first

(an article originally published in the May / June 2026 issue of Wiring Harness News)

For example, a planner may produce an export of production data to a spreadsheet and then use an AI application to create a new schedule; an operator may consult a chatbot for guidance on troubleshooting regarding extrusion temperatures or line speed adjustments; or a supervisor may create a dashboard to track the movement of reels, because producing a complete view across departments is difficult. These actions are not necessarily an intention to bypass the company’s existing systems or create risk. Rather, they are intended to keep production running at a high level of efficiency.

The problem is not that there’s a technology embracement among manufacturing employees. In fact, this willingness to create some level of innovation can be positive. The issue arises when employees utilize unofficial tools to create solutions to operational issues out of standard work processes, disconnected from real-time production data, and without reporting to management. What often goes unnoticed is how much time these disconnected activities consume when multiplied across an entire operation.

A few minutes spent by one employee consulting outside AI tools may not seem significant, but when repeated by multiple operators, planners, and supervisors throughout the day, the hidden labor cost grows quickly. For manufacturers, especially those navigating modernization initiatives, shadow AI is less of a technology problem and more of a systems problem. It reveals a growing gap between the operational demands placed on teams and the digital infrastructure available to support them. The conversation around AI in manufacturing often focuses on what the technology might achieve in the future. But on today’s shop floors, the more immediate question is whether the underlying production environment is structured well enough to support reliable automation at all.

Why Shadow AI is Emerging in Wire & Cable Manufacturing

Companies operate with an emphasis on precision, timing, and coordination; this means that even the slightest disruption will have downstream effects, resulting in delays in production, waste, or poor quality in the materials manufactured. Unfortunately, many manufacturers still rely primarily on manual processes, disjointed spreadsheets, and older systems that were never designed to facilitate real-time visibility across the production floor. The gap that results from this environment is often where shadow AI emerges.

When employees cannot quickly find the information they require to do their jobs, they will look for faster means to resolve issues. In the case of wire and cable specifically, these workarounds can multiply rapidly because of the reliance on production processes. Within a manufacturing environment, parameters can have a major impact on the production schedule and process execution. A single outdated routing or missing production update can create confusion across multiple work centers.

A rising need to sustain throughput and delivery performance has resulted in many workers utilizing external tools because they are both publicly available as well as quick/easy to access. Outside AI tools provide immediate answers, summaries, and/or organizational support—all of which are lacking in most production types. While these third-party tools can assist with productivity on an individual level, they also expose the organization to the risk of operational inconsistency when used outside of validated manufacturing systems. This is where the conversation really begins to shift from AI to something more fundamental and foundational, which is data integrity.

The Bigger Risk Isn’t AI. It’s Bad Data

AI has quickly become one of manufacturing’s most discussed technologies, but many conversations skip an important reality: AI does not create operational accuracy on its own. It depends entirely on the quality and consistency of the information it receives. For wire and cable, that distinction matters.

Every day, production environments generate a massive amount of operational data: material usage, quality inspections, labor reports, downtime events, routing updates, and traceability records. When this data is standardized and connected, it becomes a valuable operational resource. When it’s fragmented across spreadsheets, paper documents, different software, or “legacy knowledge,” it becomes very difficult to trust at scale.

This is where shadow AI can unintentionally create greater manufacturing challenges. When an operator references incorrect setup data derived from an incomplete production history, the issues may not manifest immediately. However, over time, errors accumulate, leading to increased scrap rates; greater difficulty in identifying scheduling conflicts; and longer investigations into quality issues due to missing or dispersed traceability records.

In many cases, employees also begin creating separate versions of schedules, process notes, or production tracking methods using disconnected tools and datasets. Eventually, supervisors must reconcile those differences back into one standardized process, forcing teams to spend additional time correcting, recreating, or discarding work that was completed outside the primary manufacturing system.

Relatively small data inaccuracies can have major operational ramifications. Incorrect insulation specifications; outdated conductor measurements; no lot traceability on raw materials used; or inconsistent tracking can all result in compromising production quality, as well as compliance standards. If a manufacturing environment does not have standardized processes, then AI will be unable to independently identify which data is trustworthy.

This is why successful modernization efforts begin with operational discipline before advanced intelligence. It is critical to establish an accurate and reliable production environment initially, as this will help ensure that AI is a viable source of accuracy and reliability. By establishing a solid foundation for accurate and reliable data, manufacturers can develop solid frameworks for adding AI to their processes.

why smaller manufacturers need practical automation first

Small to midsized manufacturers often feel overwhelmed by the pressures of modernization. Discussions in the industry tend to focus on cutting-edge artificial intelligence efforts, predictive-analysis trends, and the existence of completely autonomous operations (suggesting that manufacturers need to be able to completely remake themselves in order to remain competitive).