The survey of 86 manufacturing companies across Europe has implications for the sector across the world, revealing that 81% agree that AI has become more important to their business over the last 12 months, but only 10% say they have a detailed plan to get there. defined initiatives and responsibilities. Meanwhile, 16% say they continue to develop and implement AI on an ad hoc basis. Only 12% have successfully scaled AI enterprise-wide, and these leaders have been on the journey for more than five years on average, reporting reduced costs, more accurate decision-making and greater customer engagement.
Becoming a leader who integrates AI into your business is about more than installing new software and reaping the rewards. It takes time and careful planning, but the results are substantial. Our investigation (described in more detail in the full report) shows where opportunities lie and how manufacturers can replicate some of the best practices.
Opportunities and obstacles
Since 2014, manufacturing companies have participated in only five AI-driven M&A deals, according to EY Embryonic. This number increased to 59 in 2019, totaling 179 transactions over this period, with a compound annual growth rate (CAGR) of 64% and a total transaction value of €1.4 billion.
It’s easy to see why manufacturers are excited about AI. In factories, smart sensors, the Internet of Things and AI enable predictive maintenance – used by 68% of respondents – to reduce costs and extend the life of important assets. Then there is digital twinswhich are virtual replicas of a product, process or equipment for use in simulations. In the survey, 62% say they have adopted digital twins, for example to make supply chains more resilient. AI can also play a role on the other side of the value chain by enabling chatbots – used by 66% of respondents – to quickly respond to requests through text analysis, and identification of intrusions regarding cybersecurity is also a popular response (69%). .
Other use cases are more nascent but also powerful: for example, AI can help predict customer demand (37%) and manage inventory (32%) for seamless execution. Analytics can also enable better decision-making and more efficient use of labor, and AI visual analysis can be used in maintenance for faster inspections and checks.