Ai in manufacturing: why this industry plays by different rules? Prompt & Response webcast

Manufacturing is often discussed alongside banking, retail, or e-commerce when it comes to AI. In practice, however, it operates under very different conditions. This episode of Prompt & Response continues the journey started in earlier conversations about AI strategy and use case discovery, moving from high-level strategy to the reality of physical processes and factory floors.

The key difference does not start with algorithms or platforms. It starts with the nature of data itself.

This episode is especially relevant for leaders and architects who want to move beyond Industry 4.0 dashboards and understand how modern data platforms and generative AI can support real manufacturing processes, not just proofs of concept.

🎥 Watch the full episode below

Prompt & Response Webcast #4 – Transcript & Key Insights

Data does not start digital in manufacturing industry

In digital-native industries, data is born digital. Transactions, clicks, and interactions are structured from the very beginning. Manufacturing works in the opposite direction.

Here, data originates in the physical world. Pressure, vibration, temperature, sound, movement – all of these signals must first be measured and translated into digital form. Only then can analytics or AI be applied.

This is why working with manufacturing data is never just about dashboards. It requires engineering understanding and domain knowledge. You need to know how machines behave, how materials age, and how small physical deviations can signal much larger problems.

The challenge is amplified by legacy environments. Many factories still rely on machines and PLC controllers designed decades ago. AI systems must integrate with technology that was never meant to be connected, scalable, or data-driven.

In manufacturing, data doesn’t come from clicks or transactions. It comes from physics. From pressure, vibration, temperature.
If you don’t understand the physical process behind the data, no AI model will save you.

Marek Czachorowski
(AI & Data Practice Leader, Inetum)

Repeatable processes create real analytical potential for manufacturing companies

Despite these constraints, manufacturing has a powerful advantage: repeatability. Production lines, assembly steps, quality checks, and logistics flows run every day, often thousands of times.

Where repetition exists, patterns emerge. Deviations become visible. This is why manufacturing, when approached correctly, is such a strong candidate for analytics and AI.

The value goes beyond the production line itself. Supporting processes like supply chain management or logistics generate equally important signals. When analyzed together, they reveal how a factory truly operates – not just how it was designed to operate.

Products continue to generate data after leaving the factory

Modern manufacturing no longer ends at production. Products themselves become continuous sources of data once they are in use.

Predictive maintenance is often called the holy grail of manufacturing AI. Not because it’s technically impressive, but because even small improvements can save millions when downtime is critical.

Sebastian Stefanowski
(Chief Architect)

Sensors embedded in machines, vehicles, or equipment allow manufacturers to understand how products behave in real conditions. This enables a shift from reactive maintenance to predictive maintenance – identifying issues before failures occur.

In highly complex environments, such as aviation, even small improvements have a massive impact. Unplanned maintenance is expensive, downtime is critical, and better prediction translates directly into safety improvements and measurable cost savings.

What distinguishes successful cases is rarely the sophistication of the model. It is the maturity of the data foundations behind it.

Quality control moves from inspection to prevention

AI also changes how quality is managed inside the factory. Instead of discovering defects at the end of production, machine learning models can identify early signals that something is going wrong.

Subtle anomalies appear long before a defect becomes visible. This allows production to stop earlier, avoid unnecessary cost, and focus resources where they actually matter.

The result is not just better quality, but higher throughput and lower waste at the same time – a combination that traditional approaches struggle to deliver.

Use AI to your advantage: from collecting data to understanding it

For many organizations, Industry 4.0 initiatives focused on connectivity and dashboards. Sensors were installed, data was collected, charts were built. What is changing now is the role of generative AI.

The focus shifts from collecting data to understanding it. GenAI acts as a bridge between humans and complex technical environments, helping engineers navigate documentation, maintenance history, sensor logs, and operational data more efficiently.

This mirrors patterns discussed earlier in Prompt & Response, including voice of the customer in the age of GenAI, where GenAI serves as a translator between raw data and human decision-making.

The impact is especially visible in knowledge retention. Experienced engineers leave, taking years of tacit knowledge with them. AI does not replace expertise, but it makes existing knowledge accessible and usable at scale.

Start with business reality, not with ai

Most AI initiatives in manufacturing don’t fail because of algorithms. They fail because data foundations and business context were never fixed first

Piotr Mechliński
(Host, Head of AI & Data, EEMEA)

One message remains consistent throughout this episode and the entire series. Successful AI in manufacturing does not start with AI.

It starts with business reality. Clear goals, well-understood processes, and reliable data foundations come first. Only then does it make sense to decide whether machine learning, predictive models, or Generative AI are the right tools — and where they will actually deliver value.

This is the natural continuation of the journey discussed in From AI strategy to execution, where strategy only matters if it can survive contact with reality.

Manufacturing is not harder than other industries when it comes to AI. It is simply different. Organizations that respect that difference are the ones that turn AI from hype into measurable results.