AI and sustainability in the manufacturing industry


The manufacturing industry is in the midst of a revolutionary transformation, driven by advanced technologies such as Artificial Intelligence (AI). At the same time, there is a growing awareness of sustainability and the need to integrate greener practices. The combination of AI and sustainability offers immense opportunities to increase efficiency, conserve resources and reduce environmental impact.

AI in the manufacturing industry

AI is not new to manufacturing, but it has become much more important in recent years. ChatGPT in particular has given the topic of generative AI a massive boost and accelerated the topic worldwide. According to a survey by McKinsey, 79 percent of managers worldwide say they are familiar with it and 22 percent say they use it regularly in their work.

The IDC 2023 GenAI ARC Survey also emphasises that generative AI solutions are being evaluated or implemented in manufacturing companies.

Around 30 percent of European companies surveyed have already invested significantly in generative AI, with further spending planned on training, the purchase of generative AI-supported software and consulting. Almost 20 percent are conducting initial testing of models and targeted proofs of concept but have not yet established a spending plan.

According to the Gartner AI Hype Cycle 2023, one of the most promising applications of AI in the manufacturing industry is predictive maintenance. The global market share for predictive maintenance is estimated to be between 4.3 and 7.8 billion US dollars in 2023, depending on the source, with an average annual growth rate of 30 percent. Another important area is intelligent automation. In this area, Gartner expects the use of technologies such as hyperautomation, which includes AI solutions, to increase by almost 50 percent by 2025, which shows how important these innovations are for the industry.

An overview of the most important AI applications in manufacturing:

  1. Automation and robotics – In modern factories, collaborative robots (cobots) are increasingly taking over tasks that were previously performed by humans. These robots are not only able to work with high precision and speed, but also, through the use of AI, to learn new skills and adapt to changing production conditions. The automation of routine activities through robotics and process automation software (RPA) improves efficiency and reduces errors, resulting in significant energy savings. A particular focus of robotics is on logistics. In 2023, Interact Analysis examined the forecast growth rates for robot deliveries in this area and put the average annual growth rate for cobots at 46 percent between 2023 and 2027.
  2. Digital twins – The use of digital twins increases the agility of companies. Using innovations in artificial intelligence, machine learning, Internet of Things (IoT) connectivity and sensor technology, an exact real-time replica of a physical product or component is created. Manufacturers are using digital twins to increase productivity and streamline the entire product lifecycle – from design and development to maintenance.
  3. Predictive maintenance – Predictive maintenance (PdM) is one of the most important applications of AI in the manufacturing industry. By analysing sensor data and applying machine learning algorithms, potential machine failures can be detected and rectified at an early stage. This reduces unplanned downtimes and resource consumption. Studies show that PdM can increase uptime by 10 to 20 percent and extend the service life of machines by up to 40 percent.
  4. Autonomous manufacturing – Autonomous manufacturing utilises advances in AI, robotics and IoT connectivity to improve operational flexibility in factories. Manufacturers are using smart, data-driven technologies to increase product quality and optimise production.

Sustainability in the manufacturing industry

Sustainability is crucial in manufacturing. It includes measures to reduce energy consumption, minimise waste and reduce CO2 emissions. Sustainable practices not only help to protect the environment but can also increase operational efficiency and reduce costs.

Integrating AI to promote sustainability

The combination of AI and sustainability can profoundly change the manufacturing industry. Here are some of the key use cases:

  1. Optimising energy consumption – AI can optimise energy consumption by analysing operational data and identifying inefficient processes. Machine learning models can make predictions about energy demand and adjust energy distribution in real time. This not only reduces energy consumption, but also costs and the carbon footprint.
  2. Waste reduction – By using AI, manufacturers can optimise production processes and minimise waste. Algorithms can analyse data from production and make suggestions for process improvement. This can help reduce material waste by up to 30 percent.
  3. Sustainable product development – Generative AI can help in the development of sustainable products. It enables engineers to create and optimise multiple design iterations to maximise resource efficiency. For example, lighter and stronger materials can be developed that require less energy to produce and extend the life of the product. Research shows that generative design approaches can enable material savings of up to 25 percent.
  4. Optimisation of the supply chain – AI can optimise the supply chain by improving inventory management, determining optimal routing strategies and predicting market demand. This reduces transport costs and the associated CO2 emissions. Studies show that AI-powered supply chain optimisation can reduce operating costs by 10 to 15 percent and reduce CO2 emissions by up to 20 percent.
  5. Predictive maintenance – AI-powered predictive maintenance can continuously monitor the condition of machines and anticipate when maintenance is required. This reduces unplanned downtime and extends machine life, which in turn saves resources and reduces environmental impact. McKinsey estimates that predictive maintenance can reduce maintenance costs by 10-40 percent and reduce unplanned downtime by 50 percent.

Challenges and solutions

Despite the many benefits, there are also challenges to integrating AI into sustainable practices:

Practical examples

To make the developments using AI to drive sustainability in the manufacturing industry more tangible, here are some real-world examples.

Regional differences in AI: Europe and Asia

The integration of AI to promote sustainability in the manufacturing industry shows different progress and approaches worldwide. There are significant differences between Europe and Asia in particular, which can be attributed to various economic, cultural, and regulatory factors.

Europe is known for its strict environmental regulations and regulatory frameworks that strongly influence sustainability in the manufacturing industry. The European Union has introduced numerous initiatives and directives to encourage the industry to reduce its environmental footprint. These include the European Green Deal, which aims to make Europe climate-neutral by 2050, and the Circular Economy Action Plan, which promotes resource-efficient and sustainable production methods.

In Europe, manufacturers are increasingly using AI to achieve these ambitious goals. For example, many European companies are implementing predictive maintenance and intelligent energy use systems to optimise their production processes and reduce energy consumption. According to a study by the European Commission, these technologies could reduce energy consumption in the manufacturing industry by up to 25 percent.

A new addition to the regulatory landscape is the EU Artificial Intelligence Act (“EU AI Act”), which was formally adopted by the European Parliament on 13 March 2024. The EU AI Act has a global impact and triggers regional discussions. It sets requirements for companies that develop or use AI in the EU and provides for strict penalties: up to €15 million or 3 percent of annual global turnover, and up to €35 million or 7 percent for serious infringements. These regulations emphasise transparency and protection of individual rights and pose challenges for Asian companies in terms of compliance and competitiveness.

In Asia, particularly in countries such as China, Japan and South Korea, the manufacturing industry is a central part of the economy. These countries are investing heavily in advanced technologies, including AI, to expand their production capacity and remain competitive. While sustainability regulations in some Asian countries are less stringent than in Europe, there are still significant initiatives to promote sustainable practices.

China, for example, has set clear targets to reduce CO2 emissions and promote green technologies in its five-year plan 2021-2025. Chinese companies are using AI to optimise supply chains, improve energy efficiency and minimise waste.

In Japan and South Korea, companies are also increasingly turning to AI to develop more sustainable production methods. Japanese manufacturers are investing heavily in robotics and automation to both increase efficiency and reduce environmental impact.


The manufacturing industry is experiencing a transformative phase in which artificial intelligence (AI) and sustainability play a central role. AI is increasingly being used to automate processes, perform predictive maintenance, create digital twins and enable autonomous manufacturing. This leads to efficiency gains and quality improvements. At the same time, awareness of sustainability is growing, with companies increasingly focussing on environmentally friendly practices.

AI is being used to optimise energy consumption, reduce waste, develop sustainable products and optimise the supply chain. Practical examples from companies such as Siemens, Mercedes-Benz and Nestlé illustrate the diverse applications of AI to promote sustainability in the manufacturing industry.

Regional differences between Europe and Asia show different approaches to the integration of AI and sustainability. Europe relies on strict environmental regulations and regulatory frameworks, while countries such as China, Japan and South Korea are investing heavily in AI to remain competitive while promoting sustainable practices.

Nevertheless, these regional differences offer valuable learning opportunities and potential for cooperation. An exchange of best practices and technologies between the regions can help to increase the effectiveness and speed of sustainable transformation. European companies can benefit from the innovative strength and technological progress in Asia, while Asian manufacturers can learn from the strict environmental standards and environmental awareness in Europe.

At a time when sustainability is becoming increasingly important, the combination of AI and sustainable practices can be the key to a successful and responsible future in the manufacturing industry.

Silke Hänisch, Market Intelligence Senior Expert