How industrial companies can become “AI-ready” and future-proof

When people, machines, and algorithms work as a "team," more than automation emerges—a learning and resilient factory. But how can industrial companies make the leap into the AI era without losing their human element?

In times of increasing pressure for efficiency, costs, and differentiation, heightened geopolitical tensions, supply chain risks, and sustainability requirements, and where resources and skilled workers are becoming scarcer, industrial companies must become "AI ready." And fast.

Case studies already show that AI- supported factories achieve up to 20 percent higher OEE (Overall Equipment Effectiveness) values than traditional factories. Analyzing data in real time, proactively managing maintenance cycles, and networking supply chains: this reduces costs and increases resilience. By investing in AI early on, companies gain measurable competitive advantages and actively shape their future viability.

The focus is shifting from pure automation to true autonomy – self-learning, self-regulating systems under human supervision. The path to this is not a leap, but an evolution.

Maturity levels measure how companies can gradually raise the interaction between humans and machines to a new level – from context-based optimization recommendations from AI (assistance) to joint problem-solving (co-creation) or orchestration and prioritization tasks (moderation) to AI systems that regulate or “heal” themselves (high autonomy) – with humans as supervisors.

How to build an AI-supported organization

Technological complexity, a lack of skills among employees, regulatory hurdles, and cultural reservations are hindering many pilot projects. Without a robust data architecture and a reliable MLOPS framework ( Machine Learning Operations), AI models remain isolated solutions. Furthermore, the transition from siloed, hierarchical organizations to agile, cross-functional teams requires significant leadership and active change management.

Implementing AI is obviously not a plug-and-play project. It requires a holistic approach that considers both people and technology together. Four dimensions prove to be particularly critical to success on the path to the "cybernetic enterprise":

• Data-driven production: When sensors continuously supply the necessary "raw material," edge AI can process the data directly at the machine – without detours via the cloud. This saves time, increases reaction speed, and minimizes downtime.

• Platform-based architecture: Open interfaces and digital twins enable the in-depth networking of development, production, and service processes. A scalable platform forms the basis for the fast, secure, flexible, and interconnected use of AI applications.

• Learning systems: An organization is constantly in motion – like a living system. Feedback loops are essential for this. They form the core of continuous development and optimization.

• Humans as conductors: Collaborative robots (cobots) take over monotonous tasks. Humans orchestrate the value creation – supported by Explainable AI, which makes the decisions, predictions, or recommendations of an AI system comprehensible to humans, thus creating transparency and trust.

Why "cybernetic," exactly? For the past 15 to 20 years, digital transformation has been the dominant term. But in the age of AI, that falls short – both in terms of content and concept. The term cybernetics derives from the Greek word for "helmsman" and refers to control principles in complex systems – feedback and circular processes play a central role here. This makes it ideally suited to describing models for collaboration between humans and AI.

“Cybernetic Transformation”: Step by step

To successfully navigate the transformation to a "cybernetic enterprise," a solid transformation path is essential. A proven roadmap helps companies avoid or overcome obstacles:

1. Prioritize vision and use cases: A clear target image is essential. Furthermore, realistic use cases that offer both quick wins and long-term potential make getting started tangible – from condition monitoring to autonomous intralogistics.

2. Define your data strategy: Who is responsible for which data? How is governance organized? What security standards apply? A data strategy must answer these key questions.

3. Establish platform engineering: It is recommended to build central platform teams that automatically deploy Devsecops pipelines (development, security, operations), self-service APIs, and standardized infrastructure components (microservices). These teams are key enablers for independent, fast, and secure work with AI products.

4. Empowering the workforce: Further training is essential. Whether it's upskilling, a citizen data scientist program, or AI ethics training – employees must be able to work with the technology "as a team." Because this is about nothing less than a paradigm shift.

5. Ensuring scalability and operation: AI systems need clear processes for edge deployment, model lifecycle management, and compliance checks to keep things running smoothly.

Or perhaps "Industry 5.0"?

After using the term Industry 4.0 for so many years, it's tempting to simply declare it a new release. But beware: the term can lead to Industry 4.0 being considered complete – even though many of its technological foundations, such as robotics, IoT (Internet of Things), and AI, are only now experiencing their productive breakthrough.

What's new is the even stronger focus on people, resilience, and sustainability – this is how the EU Commission defined this industrial phase several years ago. However, as long as "5.0" remains more of a buzzword and narrative than a reliable level of maturity, companies should critically examine the label, align themselves with the principles of the "cybernetic enterprise," and pragmatically focus on concrete business results.

Man and machine: competitive advantage and unbeatable team

In the organization of the future, there is no longer a separation between human intelligence and machine precision – a new operating system for value creation is emerging: collaborative, adaptive, resilient.

Those who have the courage to put people first and orchestrate AI responsibly are not only redesigning processes – but also the culture of collaboration. The path to the "cybernetic enterprise" is not a trend, but an evolutionary step. And it begins now.