What is an AI-native factory and how does it differ from a smart factory

We have been hearing the term "smart factory" for years. But most projects to date ended up as isolated islands of automation — predictive maintenance on one machine, a quality inspection camera on one line, sensors monitoring temperature in one hall. That is precisely the difference compared to an AI-native factory: here, AI is no longer an additional layer glued onto an existing workflow. It is the very operating system of production.

Todd Edmunds, global CTO for smart manufacturing at Dell Technologies, described it unequivocally in an analysis for Forbes: "AI is no longer an add-on. It is becoming the default operating model." In an AI-native factory, data flows from machines, sensors, and software systems in real time, and AI models evaluate the situation and recommend — or directly execute — production adjustments. It is not about watching what happened yesterday, but influencing what is happening right now.

The difference is best illustrated by comparison: a traditional smart factory works with reactive dashboards and human intervention, while AI-native operations run on continuous real-time learning with assisted or autonomous optimization. Static workflows are replaced by adaptive systems that improve with every production cycle.

Physical AI: When models step out of the cloud onto the factory floor

The key concept behind this shift is physical AI. It is not another buzzword — it is a fundamental architectural change. While traditional AI models run in the cloud and their output is text or a dashboard, physical AI interacts directly with the physical environment: with machines, robotic arms, temperature and vibration sensors, cameras on the production line.

A good example is New Belgium Brewing, where a digital twin of the production centrifuge runs connected to live operational data and simulates the results of different settings in real time. The operator does not wait for a report — they see recommendations instantly and can make a decision within seconds. Edmunds sums it up: "Physical AI comes into play when virtual models not only mirror operations but continuously decide how the factory should run."

A similar approach was taken by semiconductor manufacturer Sandisk, which combines automation, robotics, and AI at its Penang plant. The result is an increase in unattended operation from 80% to 95%, a 32% drop in manufacturing costs, a 46% reduction in energy consumption, and a decrease in defects from 800 to 100 parts per million. These are not laboratory figures — this is a real factory.

Infrastructure: Why edge computing beats the cloud

Physical AI has one critical requirement: low latency. If data from a robotic arm had to travel to the cloud and back, the response would be too slow for any real-time decision-making. That is why manufacturers are massively shifting AI computation to the edge — directly into the factory, close to the data source.

According to a Dell Technologies survey, 73% of manufacturing companies are actively investing in IT infrastructure specifically for AI workloads. A typical setup includes GPU-accelerated servers (most commonly NVIDIA), high-speed storage, and network infrastructure capable of processing data in milliseconds. Platforms like Dell AI Factory with NVIDIA or Siemens Industrial Edge aim to unify this infrastructure into a single whole.

Playing in the same league is NVIDIA Omniverse, used for simulations and digital twins by memory manufacturer Micron, for example, or the Accenture platform, which is building AI-native manufacturing digital twins across plants in Europe for automaker Stellantis.

Data, however, remains a problem. Dell Technologies research shows that 95% of organizations struggle with identifying, preparing, or utilizing data for AI. In manufacturing, where data is often fragmented between IT and OT (operational technology) systems, the challenge is even greater.

Europe is not asleep: BMW, NEURA, and a Czech connection

While much of the discussion about physical AI takes place in an American context (Dell, NVIDIA, Sandisk), Europe has its own strong story. BMW became the first European automaker this year to deploy humanoid robots in live production at its Leipzig plant. The automaker is testing them for parts handling and simple assembly operations alongside human workers.

German startup NEURA Robotics closed a Series C round of 1.2 billion euros this year with the goal of building a European platform for physical AI. Google is also investing massively in robotics in Europe, having opened a robotics research center in Munich, and NVIDIA — Jensen Huang at Davos 2026 called AI robotics "a once-in-a-generation opportunity for Europe".

And what about the Czech Republic? The domestic industry is still at the beginning of the AI-native transformation, but foundations are being laid. Czech AI Factory in Ostrava, which launched at the beginning of the year, represents a new node of European AI infrastructure. For Czech manufacturing companies — from Škoda Auto through automotive suppliers to smaller engineering firms — this means a more accessible path to deploying AI in production. The Czech Republic, as an industrial powerhouse with manufacturing accounting for over 20% of GDP (one of the highest in the EU), has a lot to gain from this transformation.

Security: When AI becomes the factory's control system

When AI stops being a reporting tool and becomes part of production control, security takes on a whole new dimension. According to an Ernst & Young survey, 49% of technology leaders cite data protection and privacy as the main barrier to deploying agentic AI. And 52% of corporate AI initiatives run without formal approval or oversight.

In a manufacturing environment where IT systems (ERP, MES) intersect with operational technologies (PLC controllers, SCADA), the situation is even more complex. Experts recommend the concept of Unified Namespace — a unified data architecture that standardizes data sharing across all systems in the factory. Without it, AI models work with inconsistent information.

From a regulatory perspective, the EU AI Act also plays a role, introducing requirements for high-risk AI systems from 2026 — and this includes AI controlling industrial processes. European manufacturers must therefore address not only technological but also regulatory compliance.

AI as a multiplier of human capabilities, not a replacement

Despite fears of automation, the main goal in AI-native factories is to amplify human abilities, not replace them. Edmunds from Dell speaks of a "force multiplier": engineers can use AI simulations to test thousands of design variants in a fraction of the time, and floor operators receive recommendations from AI assistants directly to their terminal or augmented reality glasses.

At the aforementioned Sandisk plant, generative AI tools help employees search technical documentation, write code for automating routine tasks, and support product design. People can focus on creative and strategic work while AI handles the routine. This is a principle that makes sense for Czech factories suffering from a chronic shortage of skilled labor as well.

What comes next

The transition to AI-native manufacturing does not happen overnight. It requires infrastructure standardization across plants, unification of data layers, and above all — a mindset shift from pilot projects to truly operational deployment. According to estimates referenced by Forbes, AI could bring up to a trillion dollars in productivity to manufacturers that deploy it effectively.

For Czech readers in the manufacturing sector, one thing is clear: the AI-native factory is not sci-fi for Silicon Valley. It is a trend that will fundamentally reshape the competitiveness of manufacturing companies over the next 3–5 years — and whoever starts today will set the pace tomorrow.