If you make a list of the most cited tech buzzwords in the media, Artificial Intelligence (AI) and Industry 4.0 will certainly be right at the top.
The growing popularity of both concepts is not in vain. Indeed, the global AI market was valued at $62 billion in 2020 and is expected to reach $93 billion by the end of 2021. The global Industry 4.0 market , in turn, was estimated at $70 billion in 2019 and is expected to grow to $210 billion by 2026.
Over the past decade, AI has brought smart technologies to the center of various industries , such as automotive, healthcare, retail, finance, and manufacturing. Currently, tagging, clustering, categorization, hypothesis generation, alerting, filtering, navigation, and visualization remain the industry 4.0 market leaders. Meanwhile, cloud-based computing platforms and on-premises hardware equipment play a decisive role in facilitating the further development of smart industrial solutions on a long-term basis.
4 Major Applications of AI in Industry
It should come as no surprise that AI has given the industrial sector a huge boost. Thus, 9 out of 10 EU-based industrial companies currently invest in creating smart factories, that is highly digitalized and interconnected environments where machinery and equipment improve industrial processes through automation and self-optimization. But how does AI actually disrupt today's industry? The 4 most significant applications of AI in industry are the following:
- Production. - AI algorithms provide different production optimization opportunities. In particular, they improve the quality control and enable the prediction of machine incidents and breakdowns, thus optimizing the industrial equipment maintenance.
- Supply Chain Management. - AI solutions forecast customer demands, optimize transport costs, and forecast delivery times.
- Sales & Marketing. - AI tools carry out the hyper-personalized marketing campaigns and the automatic and massive analysis of customer reviews.
- Research & Development. - AI helps analyze technical trials, carry out targeted laboratory experiments, and adapt the product portfolio to customer expectations.