BCG-WEF Project: AI-Powered Industrial Operations

Recent Publications

Frontier Technologies in Industrial Operations: The Rise of AI Agents

AI agents have the potential to lead manufacturers toward a future of self-controlling, near-autonomous operations, unlocking opportunities in many industries. In the AI-centric factory, human workers will transition from hands-on operators to orchestrators, stepping in when judgment or creativity is required. This shift will boost operational efficiency, allowing people to focus on the strategic tasks and ethical decision-making that drive innovation and growth.

BCG has collaborated with the World Economic Forum, operations and technology executives, and academic experts on an initiative to support manufacturers in the adoption of AI agents. This work has focused on two types of agents:

Virtual AI agents enable software applications to achieve defined goals autonomously in the digital environment. These agents support workers and can independently control and steer processes and machinery. The maturity of virtual AI agents can be categorized into three levels: assistance, recommendation, and automation. The distinct objectives at each maturity level are pursued by specialist agents. Meta agents orchestrate specialist agents to achieve broader objectives.

Embodied AI agents equip physical systems, such as robots, with the ability to perceive and act within the physical environment, allowing for dynamic and complex movements. These advancements are overcoming the current limitations of robotic automation. Unlike rule-based systems that depend on manual coding, AI-enabled, training-based robotics can now acquire skills through reinforcement learning in simulated environments. Going further, context-based robotics built on robotics foundation models (RFMs) have a general understanding of the world. Because they require neither coding nor training, they can lead to a paradigm shift in robotics: zero-shot learning. (See the exhibit.) RFMs are undergoing rapid advancements, with breakthrough applications expected in the coming years.

Successfully navigating the transition to near-autonomous operations driven by AI agents requires a comprehensive, value-driven approach to technology adoption. Solutions should be scalable and aligned with long-term business objectives. Establishing strong organizational and technological foundations that support this vision is a strategic imperative for manufacturers looking to capture their full potential.

Unleashing AI’s Potential: A Step-by-Step Guide

The growing significance of data and advanced manufacturing technologies, such as artificial intelligence (AI), offers fresh avenues for companies to improve production efficiency and flexibility as well as to promote sustainability and empower their workforce.

Many manufacturers have already integrated AI into their operations. However, a BCG survey, conducted in 2023, found that only one in six of these companies have met their AI-related objectives to date. This shortfall predominantly arises from inadequate organizational and technological foundations, which are essential for scaling AI solutions throughout production networks.

At the same time, AI’s evolution continues unabated, with powerful innovations such as generative AI emerging on a regular basis. Generative AI presents additional opportunities to reimagine certain operational processes and transform how employees work in plants. For example, manufacturers can use this technology to give employees detailed work instructions, including visualizations and the required spare parts, for specific maintenance incidents. Such capabilities remain largely untapped but can be successfully adopted with the right implementation approach.

To support manufacturing companies on their AI journey, BCG collaborated with the World Economic Forum, operations and technology executives, and academic experts to develop a guidebook for harnessing the AI revolution. This effort drew upon insights from our exploration of the untapped potential of AI in industrial operations and the variety of AI applications that manufacturers currently deploy.

The guidebook consists of five sections. The first three represent the different stages of a manufacturing company’s AI journey, while the latter two describe the building blocks needed for successfully implementing and scaling AI:

• Paving the Way for Success from AI in Industrial Operations. This section highlights the importance of articulating an organization’s long-term AI objectives and transformation principles as a starting point of the AI journey.
• Mastering the AI Journey Across Production Networks. This part lays out a step-by-step approach from the status quo assessment to the design, engineering, implementation, and scaling of value-adding AI applications and required foundations.
• Staying at the Forefront of AI Innovations. This segment explains the importance of conducting periodic AI reviews to continuously identify and integrate AI innovations and related opportunities as they emerge.
• Value-Adding AI Applications for Industrial Operations. This section pinpoints AI-based applications that address inefficiencies and operational opportunities to achieve intended improvements and outcomes.
• Foundations for AI Implementation in Industrial Operations at Scale. This part defines the AI-related organizational and technology foundations to enable implementation at scale and long-lasting success.

Recognizing that an AI journey is not a one-time effort, the guidebook empowers manufacturers to continually adapt to the rapid advancements and innovations of AI applications in industrial operations.

AI-Powered Industrial Operations: AI Survey Results

The world of industrial operations is changing, and manufacturing companies are facing considerable challenges—including rising economic pressure, the sustainability imperative, volatile resource prices, and supply chain disruptions as well as increasing capability challenges and talent shortages. In this complex environment, the expanding role of data and advanced manufacturing technologies such as artificial intelligence (AI) offers companies new opportunities to address these challenges and to significantly augment industrial operations.

To evaluate the current state of AI within industrial operations, BCG conducted a global survey of almost 1,800 manufacturing executives. The survey covered seven different industries across 15 nations worldwide.

What We Learned

Here are five key takeaways from the survey:

• AI is highly relevant to industrial operations: 89% of companies plan to implement AI in their production networks. 
• AI is boosting production efficiency: early adopters saw 14% savings on addressed manufacturing costs. 
• Adoption is increasing: 68% of companies have already started implementing AI solutions. 
• It’s not a sure-fire success: only 16% of companies have achieved their AI-related targets. 
• Scaling is particularly difficult: 98% of companies face challenges scaling their AI solutions. 

Among our other findings: Manufacturers in China and India lead those of other nations in terms of AI maturity, while among industries, technology equipment manufacturers are most mature in their use of AI. Quality control, robotics and production automation, production alert systems, and inventory optimization are some of the leading AI use cases for industrial operations, according to the study. But even the most common use cases were relatively immature—25% or less of the companies surveyed had fully rolled out those applications.

When it comes to the barriers to scaling AI throughout their production networks, 88% of executives named a lack of AI-related technology infrastructure as a major challenge, with data processing (34%) and visualization infrastructure (34%) being the most common areas of concern. Meanwhile, 92% of executives said the lack of an AI-related people and organization foundation was a challenge, with a shortage of digital skills and capabilities (39%) and the lack of an AI strategy and roadmap (33%) being cited most often.

The survey results underline both the relevance and potential of AI for industrial operations. Manufacturers who act now to incorporate AI solutions can gain a significant advantage in addressing today’s challenges, while those who delay risk falling further behind.