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Manufacturing is not known for overnight change. Even when a new technology is clearly valuable, adoption usually crawls: pilots, security reviews, integration debates, then a slow rollout, line by line. That’s why the last couple of years have felt different.
In the Industry 4.0 Club webinar Surprises from GenAI We Never Saw Coming featuring Dr. Peter Schopf, Managing Director at Schopf Meta Consult (SMC), and Francesca Margherita Chifari, Industry 4.0 and Servitization expert, one theme came through clearly.
Treat GenAI as a practical engineering tool, not a buzzword. The opportunity is real, but so are the traps.
The first surprise was speed. One widely cited example is the adoption curve showing ChatGPT reaching 100 million users in about two months.
That matters for manufacturing because private life and work life finally overlap. People learn the interface at home, then walk into work and want the same “ask and get a useful answer” experience for internal processes, docs, and troubleshooting.
A second reason is technical. Classical AI often requires significant training and tuning in a specific industrial setting. Pre-trained generative AI arrives with a broad foundation and can be applied quickly across many areas.
In other words, experimentation moved from a formal project into something teams can do immediately.
Once companies start looking seriously, they can identify thousands of possible use cases.
A recurring starting point is internal knowledge: centralizing documents, procedures, and tribal know-how, then making it usable across roles and languages. That can be especially valuable for training and service.
Conversational access through GenAI also changes how knowledge scales. Traditional manuals force you to curate and limit content. With an AI interface, teams can generate and adapt more tailored documentation and make information easier to access regardless of language.
This shows up most clearly in technical assistance and troubleshooting, where faster access to relevant information helps engineers fix issues and get lines running again.
“AI” is not one bucket. Large language models are excellent at language tasks like drafting, summarizing, and translating. Classical AI still matters when you need reliability on numeric signals and narrowly defined predictions.
Sentiment analysis is a simple example of where pre-trained models can replace some earlier domain-specific training work because they already understand language patterns broadly.
Predictive maintenance on IoT data is a different story. Generative AI is not reliably strong there yet, largely due to hallucination risk.
In manufacturing, that boundary matters because confident, wrong output can drive bad decisions fast.
If you have used these tools, you know why people trust them. The output sounds confident and well reasoned.
Research involving Boston Consulting Group with Harvard and MIT makes the risk tangible: people using generative AI performed much better on creative product innovation tasks, but worse on business problem solving because they over-trusted the GenAI’s output even when it was wrong.
A strong default rule for factories is simple: use it as a coach, an idea giver, and a benchmark, but do not treat it as a source of truth by default.
“Be careful” is not enough. There are concrete ways to raise reliability.
Retrieval augmented generation can ground responses in the data and documents you define. Prompt templating can help teams get more consistent outputs by providing structure and examples behind the scenes.
Working with industrial partners and dedicated solutions can also help because common use cases and implementation patterns are often already in place for industrial environments.
The next step is bringing assistance closer to the work. One believable example is smart glasses and augmented reality support during maintenance.
A technician can receive guidance while performing a task and interact with an assistant in real time, improving training and execution consistency.
That direction also connects to digital twins and virtual environments. Digital twins can provide the foundation for richer simulation, and companies can start with twins that are supportable now while preparing for a more connected future.
If you want a simple implementation playbook, three moves cover a lot of ground:
Generative AI is not a factory or company brain, and it is not a substitute for sound process design or sound human reasoning.
The opportunity is practical: enhance knowledge access, improve training and support where people struggle, and build guardrails, so the tool amplifies engineers instead of amplifying risk.
Ready to learn more? Watch the full webinar, Surprises from GenAI We Never Saw Coming.
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