Top reasons why manufacturers are implementing AI technologies

Written by Anna Stuk

May 3, 2021

Industry 4.0 smart manufacturing

   AI technologies are changing our way of living and working. Enterprises are actively looking for innovation through AI. The benefits of artificial intelligence are manifold and reach into all functions of the company: production, project management, internal and external communication, – in any area you find some AI solutions that optimize processes and bring innovation to workflows. The worldwide market of AI applications for businesses is estimated to reach over 100 billion US dollars in 2025 [1]. This figure means that not only the market leaders will apply AI-powered solutions, but also mid-sized companies will approach innovative technologies.

   We are currently experiencing a global transformation that AI is bringing to the manufacturing ecosystem. Manufacturers in 2021 have two most significant topics: Green Manufacturing and Industrial Digitalization. And the latter actually positively influences the former. For example, Mercedes is about to start a new partnership with Siemens to increase digitalization for sustainable production [2]. Bosch is also planning to implement AI technologies to further improve sustainability [3]. BMW, Tesla, VW, Miele – just to name a few manufacturers that set digitalization of production as one of the top goals in their enterprise strategy.

   Industry 4.0 generates change and sets new standards in production. According to a recent German study, over 80% of manufacturers in Germany already applied or are planning to apply digital technologies in their production plants [4]. Among the most important reasons why manufacturing enterprises are willing to implement AI technologies in the context of Industry 4.0 are increased productivity (41%), optimized manufacturing processes (39%) and improved product quality (29%) [4]. Besides those benefits related to the process and quality optimization, industry 4.0 also brings economic benefits. Numerous studies have proven that digitalization of manufacturing reduces production costs [5].

     One big part of the digitalization of factories is AI-powered pattern recognition for fault detection. Quality assurance is one of the leading areas of industrial AI applications [6]. Due to automated and very precise error detection enabled by Deep Learning-based technologies, defective parts can be immediately removed from the production line, which leads to “[…] huge savings in recalls, repairs, and lost business” [5]. Regarding the latter, yes, poor quality indeed is one of the most significant reasons for image ruination and a major quality error can cause destruction of an enterprise [7].

   When AI was introduced as a concept to the public, many feared that smart machines would replace human workers. In contrast, numerous studies report a positive tendency. With the help of intelligent technologies, employees are freed from physically hard or repetitive tasks [8]. Skilled employees can focus on essential tasks and be much more efficient and innovative in collaboration with AI solutions.

In summary, the most important reasons for AI implementation in a factory are:


Sustainability of production


Increased productivity


Reduced waste


Decreased rework and recalls


Improved quality assurance


Optimization of production process


Economical savings


Support for employees


[1]Statista. (April 12, 2021). Market size and revenue comparison for artificial intelligence worldwide from 2018 to 2027.
[2]Daimler. Digitalization push: Mercedes-Benz and Siemens launch strategic partnership for sustainable automotive production.
[3]Bosch. Bosch setzt auf AIoT, Elektrifizierung und grünen Wasserstoff.
[4]Berg, A. Industrie 4.0 – so digital sind Deutschlands Fabriken. In: Bitkom Research 2020.
[5] Statista Digital Market Outlook. In-depth: Industry 4.0 2020.
[6] IoT Analytics. (December 6, 2019). Leading industrial Artificial Intelligence (AI) use cases worldwide in 2018, by market share.
[7] Poll, H 2016. Reputational Quotient.
[8] Heinen, N. et al. 2017. Künstliche Intelligenz und der Faktor Arbeit. Implikationen für Unternehmen und Wirtschaftspolitik.

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