Industry 4.0: From hype to harvest
23 March 2026
Geir Jåsund examines what Industry 4.0 has delivered since its introduction in 2011. He highlights where it fell short, and how its lessons are shaping the next phase of industrial digitalisation.
When German Chancellor, Angela Merkel introduced Industry 4.0 in 2011, the idea was to integrate cyber-physical systems, automation, and real-time data into self-optimising industrial ecosystems. Fifteen years later, the important question is not whether Industry 4.0 succeeded, but what we learned from its implementation and what that means for the next evolution of industrial competitiveness.
To recap, Industry 4.0 was built on five central pillars – connectivity, intelligence, automation, flexibility, and integration. It envisioned plants capable of adapting automatically to changes in demand, quality conditions, or supply constraints.
Early advocates spoke of ‘smart factories’ where sensors, machines, and systems would communicate seamlessly, eliminating inefficiencies through real-time optimisation. But many manufacturers quickly discovered that the leap from vision to execution was substantial, and that data without context can be as overwhelming as it is enlightening.
While digitalisation has undeniably improved visibility and control, the path for many has been more incremental than revolutionary. Three main lessons hve been learned: Data alone is not intelligence; interoperability remains a constraint; and people remain central.
Progress and gaps
According to Deloitte’s 2025 Smart Manufacturing Survey, more than 80% of manufacturers have launched digital initiatives, yet only a minority report scaled, consistent returns. Many remain in the ‘pilot purgatory’ stage – trapped between demonstration and deployment.
This reality reflects both the complexity of industrial environments and the need for strategic alignment. Successful organisations now treat digitalisation as an operating philosophy, not a collection of projects.
While Industry 4.0 proved that it was able to deliver operational visibility, quality & traceability, and energy and emissions management, many have also experienced unfulfilled promises.
Fully self-optimising systems, for example, remain rare and many AI models have struggled with variability across assets which has reduced the ability to achieve predictive maintenance at scale. Unified IT/OT ecosystems have also not been widely achievable for many with data silos still persisting, particularly in multi-site organisations.
The gap between aspiration and execution often came down to five structural issues:
• Legacy infrastructure incompatible with new architectures.
• Fragmented vendor ecosystems limiting integration.
• Skill shortages in data and process analytics.
• Poorly defined business cases for digital investments.
• Resistance to cultural change across departments.
These challenges are the natural growing pains of a sector in transition and should not be considered to be a failure.
The next phase of digital transformation is defined by industrial intelligence –systems that unify, contextualise, and analyse data to support decision-making at every level.
Where Industry 4.0 focused on connectivity and automation, industrial intelligence focuses on insight, adaptability, and human empowerment.
Modern platforms integrate data from production systems, laboratory information management systems (LIMS), energy tracking, and ERP systems, enabling cross-functional visibility. This shift turns data into actionable intelligence, helping manufacturers manage variability, optimise performance, and sustain improvement over time.
Enter Industry 5.0!
A growing number of industrial leaders and policymakers are now discussing Industry 5.0. While definitions vary, most agree it emphasises three main principles:
• Human-centric manufacturing
• Resilience and adaptability
• Sustainability and circularity
Rather than replacing people, the next wave aims to amplify human judgment with machine precision. The European Commission describes this as ‘using new technologies to provide prosperity beyond jobs and growth.’
In this context, data-driven collaboration becomes the ultimate differentiator. Systems that combine analytics, context, and intuitive user interfaces will define the factories of the future.
For manufacturing leaders evaluating their digital roadmap, several priorities have emerged. There is a need to define measurable business outcomes before investing in tools; It is important to integrate and contextualise data across production, quality, and energy domains; There is a need to adopt modular architectures to enable scalability without wholesale system replacement; Empowering the workforce is key so there needs to be investment in digital skills and user-friendly interfaces to encourage adoption; Digital transformation needs to be treated as an iterative process, not a destination with the focus being on continuous improvement.
While Industry 4.0 did not produce the autonomous, lights-out factories its early advocates imagined, it has fundamentally changed how the manufacturing sector views data, systems, and people and it has laid the groundwork for what matters most today – real-time decision support, intelligent data models, and empowered human collaboration.
Industry 4.0’s greatest success, therefore, may not be in the technology at all, but in how it has reshaped industrial thinking.
Geir Jåsund is CEO at Mikon AS.