Manufacturing Belongs to the Future, Not the Past!

In recent years, the conversation surrounding industrialization has often focused on the idea that manufacturing belongs to the past. But, in today’s rapidly evolving global landscape, manufacturing is not only relevant — it is essential for the future. Industrialization refers to the large-scale development of industries, which historically led to economic growth, job creation, and technological advancements. Over the past few decades, however, many countries shifted production overseas to reduce costs, leading to a decline in local manufacturing capabilities.

Now, Europe and the United States are experiencing reindustrialization — a renewed focus on bringing manufacturing and supply chains closer to domestic economies. This shift is driven by several factors: the need for more resilient supply chains to prevent disruptions from global pandemics or geopolitical upheaval, the importance of retaining skilled jobs to support local economies, and the growing demand for sustainable production to reduce environmental impact. A Capgemini survey reveals that companies anticipate a 14% reduction in carbon emissions and a 13% increase in customer satisfaction due to reindustrialization. Digital technologies — such as AI (including GenAI), machine learning (ML), automation, and data analytics — drive this transformation. This survey further highlights that 63% of companies believe integrating these technologies into reindustrialization will enhance productivity.

The Role of AI in the Manufacturing Revolution

With the integration of AI and data-driven processes, manufacturers can optimize manufacturing processes, reduce production costs as well as energy consumption, and create products that are not only more efficient but also more environmentally friendly. AI can also improve production quality, better anticipate demand for goods, optimize inventory allocation, and help design more personalized products — all critical factors in the shift toward a more sustainable and competitive manufacturing landscape. Those AI-driven solutions can deliver significant improvements in cost reduction, productivity, quality, and customer service. A BCG study estimates that these technologies can lower the cost of goods sold by up to 10%, improve yield and overall equipment effectiveness (OEE) by up to 25%, and enhance output quality by up to 30%. 

Despite these clear benefits, most manufacturers have yet to unlock AI’s full potential on the shop floor. A BCG survey of nearly 1,800 manufacturing executives across seven industries found that 89% of companies plan to integrate AI into their production networks, and 68% have already begun implementation. Only 16% have successfully met their AI-related targets. Most companies cited difficulties in scaling their AI solutions as a key barrier to success.

Scaling AI in Manufacturing

AI is only as good as the data behind it! So, one critical factor of success for this transformation will be the ability to access diverse types of data housed in myriad systems (ERP, Historian, PLM, CMMS, etc.)  with limited point integrations. Data from real-time machine sensors, supply chain insights, and energy consumption patterns can be leveraged for smarter decision-making. The ability to connect to data stored on-premise,  on modern cloud platforms like Databricks, and solutions such as PI System and SAP ensures that manufacturers can integrate historical and real-time data seamlessly. This connectivity allows companies to break down data silos and apply AI-driven insights. 

For instance, Rio Tinto, a global mining group, used AI-driven analytics on diverse datasets — including sensor data from PI Historian, alloy chemistry, and SQL-based production data — to identify refinery bottlenecks. Their system then writes these insights directly into production systems, allowing factory operators to monitor them in real time. This approach has helped reduce unplanned downtime, optimize energy use, and minimize waste.

Manufacturing companies should adopt a twofold approach, addressing small incremental improvements and transformative moonshot projects. Within each factory, numerous opportunities exist to enhance everyday decision-making. Automating simple statistical computations from machine sensor data or streamlining weekly quality reporting can save time and reduce errors. While each improvement may provide only a small gain, scaling these enhancements across hundreds or thousands of processes can create a significant impact. 

At the same time, moonshot projects have the potential to drive massive value by transforming operations across all factories. These initiatives should be carefully designed to maximize adoption and usability, and they should align with the company’s Industry 4.0 vision and prioritize a user-centric design to ensure end user adoption. For example, AI-driven analytics solutions that assist process and quality engineers identify optimal production parameters can significantly enhance efficiency and decision-making.

Without onboarding people, it is just another technology! All those efforts will inevitably fall short if companies don’t invest in their people and make sure they are properly trained to leverage data and AI with strong data and AI literacy programs. While data scientists, engineers, and analysts drive this transformation, domain experts are essential to unlocking AI’s full potential. Quality engineers will help analyze quality indicators and try to reduce scrap, process engineers will help optimize the manufacturing process and improve yield, and maintenance engineers will distinguish real anomalies from data noise. Zeus, a fluoropolymer extrusion manufacturer, has embraced this approach by involving all key stakeholders, including operators, plant managers, automation engineers, and floor staff. This strategy enabled Zeus to deploy production-ready models and deliver real-time insights in just two weeks — down from 16–20 weeks.

Doing the Last Mile With Generative AI 

For AI to truly catalyze change, every employee in a manufacturing company must benefit from its capabilities. The most successful companies of tomorrow will be those that leverage GenAI to create hundreds of specialized agents. These agents will provide employees instant access to deep knowledge and seamlessly integrate into workflows, driving continuous and incremental micro-automation.

Finding key information in supplier documents in multiple formats (PPT, Excel, PDF) containing rich content like images, figures, and tables can be challenging. A natural language interface removes barriers between human questions and data-driven insights, ensuring accessibility for all employees. Solutions like Dataiku Answers enable supply chain teams to interact with data intuitively. In the backend, data experts can use the embedding documents recipe and multimodal LLMs to process diverse document formats and deliver seamless, AI-powered search functionality.

Similarly, an AI-driven maintenance planning solution can integrate structured equipment data, time series sensor readings, and unstructured maintenance reports to optimize scheduling. The anomalies predicted by this first solution can then be leveraged by an AI agent to automatically generate work orders with recommended actions and optimized schedules, improving efficiency and reducing downtime.

The Future of Manufacturing: Innovation, AI, and Sustainability

Manufacturing and the integration of cutting-edge technology like AI are relevant today and critical to our future. As we move toward a more sustainable and self-reliant global economy, the reindustrialization of countries will hinge on the manufacturing sector’s ability to innovate, embrace AI, and ensure that every worker benefits from this transformative technology.