In today’s industrial landscape, manufacturers are grappling with a myriad of challenges ranging from supply disruptions and price volatility to talent shortages and the sustainability imperative. To overcome these hurdles and unlock new value, leading manufacturers are turning to cutting-edge manufacturing technologies powered by artificial intelligence (AI).

These AI-powered technologies offer industrial companies fresh avenues to navigate through turbulence and significantly enhance operational performance. Moreover, recent technological breakthroughs, such as generative AI, have further expanded the potential applications of AI in manufacturing. While some companies have already made successful strides in implementing AI, many others are still in the early stages of their AI journey.

To shed light on the current state of AI adoption in manufacturing, Boston Consulting Group (BCG) conducted a study titled “AI-Powered Industrial Operations,” analyzing insights from nearly 1,800 manufacturing executives worldwide in 2023.

Key findings from the study are as follows:

  1. AI is making inroads into industrial operations globally: An overwhelming 89% of executives see AI as essential to their operations and plan to incorporate it into their production processes. Manufacturing executives have a deep understanding of AI’s promise, with 64% recognizing its high potential for driving production efficiency improvements. Furthermore, executives view AI as a catalyst for sustainability (53%), production flexibility (52%), and workforce support (47%).
  2. AI implementation is already underway: A significant 68% of executives have fully implemented at least one AI use case. While there is a wide range of AI applications within industrial operations, quality control, particularly computer vision-based applications, stands out as the most mature and relevant use case currently. One in four executives report using AI in their quality control processes. Other AI use cases gaining traction include robotics and production automation, production alert systems, and inventory optimization.
  3. Industry adoption and benefits: Electronics and technology equipment lead the way in AI adoption, with 83% of companies in this sector integrating AI into their operations. Energy follows at 72%, and the process industry at 68%. Early adopters have realized an average of 14% savings in manufacturing costs.
  4. Challenges in scaling AI implementation: Scaling AI within production networks remains a significant challenge for many companies. Only 16% of respondents have achieved their AI-related targets, with 98% citing scaling AI as particularly challenging. Two categories of enablers, organizational and technological, hinder successful AI implementation. Insufficient organizational foundations, including a scarcity of digital skills and capabilities (39%) and the absence of an AI strategy and roadmap (33%), pose significant obstacles. Inadequate technology foundations, such as data processing and visualization infrastructure, also impede progress.

As industrial companies strive to unlock the full potential of AI, the technology continues to evolve. Innovations like large language models and generative AI present new opportunities for transforming industrial operations. Here are three main types of generative AI applications in the manufacturing environment:

  1. Providing information support: Well-established generative AI models and tools, like ChatGPT, can automatically generate text to support operations. This includes generating maintenance instructions for remote support or creating standard operating procedures to aid equipment maintenance.
  2. Converting information to output: Generative AI applications capable of converting text to code or images accelerate manufacturing-related processes. For example, they can create program code based on text input, streamlining process automation.
  3. Enhancing robotics: Generative AI in robotics enables robots to autonomously act on voice commands without requiring task-specific training or regular retraining. This opens up new application areas for robotics in industrial operations, such as automated material or tool supply, leading to increased production efficiency while reducing engineering costs and ramp-up times.

The integration of AI in industrial operations holds immense potential for manufacturers. By addressing challenges and leveraging the advancements in generative AI, companies can revolutionize their operations, enhance productivity, and achieve sustainable growth.

By Impact Lab