By now it’s widely known that a strong, enforceable digital transformation strategy is crucial for businesses to thrive. From automated manufacturing plants to AI-driven quality control, leveraging various technologies can improve customer experiences while lowering operational costs.

However, while Gen AI is taking off across numerous organizations and industries, some sectors have been slower to get on board. Traditional manufacturing is behind the Gen AI curve as disparate data and lack of digitization are creating obstacles on this transformation journey.

But there is hope. Manufacturing in traditional industries can look to other advanced sectors for guidance on how to implement Gen AI and revolutionize operations from product design to the shop floor.

Cutting-edge industries

According to the World Economic Forum, the global manufacturing AI market was worth over $3 billion in 2023 and is expected to increase to over $20 billion by 2028.

It’s clear that Gen AI is already making significant strides across a number of industry verticals within manufacturing. Below are some key areas where this technology is having a major impact:

Automotive

The automotive manufacturing sector is already fairly advanced in terms of implementing new technologies, such as Gen AI, because manufacturers are continuously looking at creating the next generation of vehicles and customizing features for specific customer needs. Whether it’s honing in on processes, designing new products, or improving overall efficiencies, Gen AI can assist across all those capacities.

Take a look at autonomous cars for example. These types of vehicles are gaining momentum now more than ever with different components of Gen AI being implemented into the shop floor, product design, or elements that go inside a vehicle.

A Japanese multinational automotive manufacturer  is also utilizing Gen AI to incorporate engineering constraints into the vehicle design, and is optimizing metrics such as aerodynamic drag, which is enhancing the efficiency of electric-vehicle (EV) designs.

Aerospace

Gen AI is making inroads in the aerospace manufacturing sector by optimizing production processes, improving product quality, identifying defects, and reducing costs.

For example, an aerospace and defense company is transforming its operations and innovation processes with Gen AI. AI assistants provide aircraft manufacturing instructions, enhancing accessibility to technical data, and facilitating precise task guidance.

Additionally, digital twins are playing a transformative role within this sector, by improving innovation, efficiency, and safety. Specifically, when you look at aircraft OEMs or other top suppliers who supply aircraft engines. Digital twins enable aircraft OEMs to monitor and manage the performance and safety of the engines, predictive maintenance to ensure maximum uptime, and transform business models to As a Service models (aaS).

Gen AI can help engineering teams in industries such as automotive and aerospace and defense to accelerate design, software development, and transfer of engineering designs to manufacturing and across the shop floor activities. Engineers can create rapid prototypes and simulations that can reduce prototyping costs and time to market. Gen AI models can also assist in designing and developing product designs that are compliant with company design standards as well as regulatory requirements. Additionally, Gen AI can help in reskilling the engineering teams in acquiring new skills that are needed. For example, specific skills are required to help build software systems such as Advanced Driver Assistance systems (ADAS) or Voice Enabled systems.

Lack of data

Data is fundamental when organizations are looking to create a thorough and established Gen AI strategy so that their AI models can adapt to their different business needs and requirements. However, one of the biggest challenges manufacturers face in unlocking the potential of Gen AI is poor data quality.

Every year, companies lose an average of $12.9 million due to inaccurate data. This can contribute to ineffective decision making, business performance, and bad customer service.

Data scarcity is also one of the main reasons why traditional manufacturing companies, such as steel producing plants, are lagging in Gen AI adoption. Their current set of technology and data at the plant and shop floor is outdated compared to other industries. A lot goes into implementing Gen AI technologies and these organizations are still unsure how clean and reliable the data is.

Going green

The importance of sustainability in the manufacturing industry is crucial to the long-term success of businesses, communities, and the environment. Being environmentally friendly can decrease costs, lead to more efficient production processes, reduce waste, meet consumer demand, among other benefits.

Sustainability also comes into play with Gen AI. Research shows that 73% of operation executives rank using Gen AI for waste reduction in manufacturing and operations among the top three priorities in the next 12 months.

There’s a number of small facilities such as airports, warehouses, and manufacturing plants that are implementing solutions to reduce the energy consumption of the buildings. For example, there are buildings that have an energy command center that harness a data-driven approach and digitalization to monitor and manage performance of energy assets and further advance sustainability initiatives. Gen AI is being incorporated in this platform by managing and using more renewable energy, maximizing equipment, and predicting energy demand. In particular, if there’s a specific zone inside a manufacturing plant that has less people working in a given time, Gen AI can use the data that is available and reduce the energy in those zones to save some of the power consumption.

The integration of Gen AI into manufacturing has enormous potential to transform the industry. As Gen AI continues to evolve, its role in manufacturing will only grow driving innovation and competitive advantage.