Industrial Copilots: From Assistants to Essential Team Members

Industrial Copilots: From Assistants to Essential Team Members

In recent years, AI has made significant strides in augmenting human capability across industries. Now, in manufacturing and industrial environments, we are witnessing the emergence of a new class of AI assistants—industrial copilots. These systems are designed not to replace humans, but to collaborate with them, assisting workers, engineers, and managers in navigating complex systems, processes, and data landscapes.

One compelling example comes from a German automotive plant, where employees now work alongside a new AI assistant. This industrial copilot acts like a specialized internal search engine, retrieving answers and suggesting solutions to system problems within seconds. But unlike consumer-grade chatbots, this one doesn’t rely on internet data. Instead, it draws exclusively from internal sources—equipment manuals, planning documents, shift reports, quality logs, and historical fault data. This makes it both secure and highly relevant to the plant’s specific context.

What sets such copilots apart is their integration of Large Language Models (LLMs) tailored to technical content. These models allow the assistant to understand domain-specific terminology and deliver results within the correct engineering context. The above-mentioned copilot, for instance, not only retrieves the most relevant documents but also summarizes maintenance instructions in natural language. Employees can then interact with the assistant through a chat interface, asking follow-up questions or requesting clarification.

However, even technically sound tools face organizational hurdles. As one plant manager put it, “Our chatbot for maintenance, operations, and service is currently running. From a technical side it is OK. But to integrate it into daily life I have to adapt the organization.” This reflects a broader theme in industrial AI adoption: success is as much about change management and user training as it is about algorithms and infrastructure.

Speaking the language of engineers: the case for industrial-grade AI

A similar story is unfolding at another automobile OEM, where the company’s AI Enterprise Team is scaling the use of copilots to support enterprise-wide use cases. Leveraging Microsoft Azure’s tools, it was able to skip months of groundwork and quickly deploy production-ready pilots. Their copilots run on a secure, scalable platform with built-in monitoring, compliance, and authentication, simplifying operations and allowing engineering teams to focus on innovation. Yet, the OEM also recognizes a limitation in existing AI: until recently, copilots were not able to understand the “language of engineers” — a language composed not just of text, but also diagrams, code, models, sensor data, and simulation outputs.

This is why the concept of industrial-grade AI is gaining traction. Unlike generic chatbots, industrial copilots must be able to natively understand and reason with engineering modalities. They must offer trustworthy, explainable insights derived from highly specific datasets. In essence, they must be trained on what some refer to as an industrial foundation model, capable of comprehending the interplay between physics, electronics, software, and production workflows.

Copilots or Agents? Finding the right fit for industrial AI

As AI tools evolve, so does the conversation around copilots versus AI agents. A recent survey conducted by Rootstock Software in 2024-2025 sheds light on current preferences in manufacturing. A clear majority — 53 percent — favor copilots as supportive tools that assist human workers rather than automate entire tasks. Only 22 percent preferred AI agents, suggesting that trust in full automation is still limited. Interestingly, 25 percent of respondents were unsure about the difference, pointing to the need for broader education on the role and capabilities of these tools.

The use cases for industrial copilots are varied and impactful. Maintenance technicians can use copilots to retrieve repair histories or access schematics in natural language. Production managers can get real-time insights into performance metrics like Overall Equipment Effectiveness (OEE) or yield. Engineers can query past projects to reuse components and optimize workflows. Some systems can even generate service tickets or connect users with remote experts for guided support, with transcripts feeding back into the knowledge base to continuously improve accuracy.

Beyond chat: the expanding capabilities of industrial copilots

Behind the scenes, industrial copilots are supported by a technical stack that includes predictive analytics, real-time data integration, and cross-platform interoperability. These assistants do more than just respond — they help automate code generation, validate engineering logic, and reduce the burden of repetitive tasks. In doing so, they enable faster deployment of production systems while improving the quality and efficiency of engineering work.

Despite these advances, several challenges remain. Data remains the bedrock of effective copilots, yet many workers on the shop floor are still not accustomed to working with data directly. Upskilling and improving data literacy among frontline staff is critical. Additionally, industrial companies are learning that while not all problems need AI, AI absolutely needs high-quality data to function well.

An important lesson shared during Siemens’ AI with Purpose Summit was the importance of a data classification framework. To ensure copilots have access to usable data without risking intellectual property or compliance violations, one company adopted a color-coded approach: white for synthetic data (freely usable), green for uncritical data (approval required), yellow for sensitive information, and red for internal IP (restricted to internal use only). This structured approach provides a safe path for organizations to begin their journey with AI copilots — starting small, starting safe, and scaling up.

Boris Scharinger, Siemens Digital Industries, shared his view: “The race for AI-driven productivity needs AI innovation partnerships, and industrial data becomes the currency in such partnerships. The cumbersome work of mining this currency needs a progressive approach — what data can we unlock for partners and what is truly a trade secret that is worth protecting?”

Rethinking work: what comes next in the AI-augmented factory

Ultimately, industrial copilots mark a significant step forward in the digital transformation of manufacturing. By embedding AI into the workflows of engineers, operators, and technicians, companies are not just digitizing tasks — they’re reshaping how industrial knowledge is captured, shared, and applied. And as these systems mature, the question will no longer be whether AI can assist in production or engineering—but how we design work to best take advantage of it!

About the author

This article was written by Jan Burian, a global manufacturing industry analyst, serves as the Head of Industry Insights at Trask. His expertise spans digital transformation, management, leadership, and the geopolitical influences shaping manufacturing and global supply chains.