Manufacturing has entered a new era where artificial intelligence is becoming the deciding factor determining competitive advantage. Industry leaders understand that AI maturity involves comprehensive organizational transformation, revolutionizing everything from production processes to customer engagement across global operations.
At IMD’s TONOMUS Global Center for Digital and AI Transformation, we assess organizations across five critical dimensions that distinguish AI leaders from followers. Our proprietary AI Maturity Index reveals how manufacturing leaders are building capabilities to create sustainable competitive advantages in today’s rapidly evolving industrial landscape.
Analysis of the 2024 AI Maturity Ranking uncovers distinct patterns among the most AI-advanced manufacturing companies, including Lockheed Martin (#30), General Electric (#31), Contemporary Amperex Technology (#35), Mitsui & Co. (#40), and Siemens (#41).
We have domain know-how – we understand our industries. And we have the data. Together with AI, this is a winning combination.
Manufacturing AI maturity originates with decisive executive leadership that positions artificial intelligence as central to competitive strategy rather than supplementary technology investment.
General Electric, now operating as three separate companies (GE Healthcare, GE Aerospace, and GE Vernova), has embedded AI leadership across each division. GE Healthcare appointed Parminder Bhatia as Chief AI Officer in 2023, focusing on AI applications for medical imaging and diagnostic equipment, while each division has integrated AI into core operational strategies.
Contemporary Amperex Technology (CATL) CEO Robin Zeng envisions AI as critical for breakthroughs in battery technology, particularly for fast-charging batteries and energy storage systems, building a 20,000-person research team focused on maintaining global market leadership through AI-driven innovation.
Mitsui & Co. has established enterprise-wide digital transformation initiatives with AI as a cornerstone technology. The company’s DX Comprehensive Strategy encompasses both business transformation and data-driven management, with plans to develop over 1,000 DX Business Professionals globally by March 2026.
Siemens exemplifies strategic commitment through CEO Roland Busch’s positioning of industrial AI as “the biggest technological lever” for manufacturing transformation. The company has developed the industry’s first industrial foundation model in collaboration with Microsoft, with Busch emphasizing: “We have domain know-how – we understand our industries. And we have the data. Together with AI, this is a winning combination.”
“CATL has advanced R&D infrastructure including a planned supercomputing center to enhance AI model development for discovering next-generation battery materials.”
The most AI-mature manufacturing organizations have built sophisticated technical infrastructures and innovative AI-powered products that create new value propositions while supporting scalable deployment throughout global operations.
Lockheed Martin has built sophisticated platforms including their AI factory supporting more than 8,000 engineers and developers with standardized access to machine learning pipelines, data, and security tools. Their ARISE™ capability standardizes modeling and simulation while utilizing AI to process large data volumes, providing significant cost savings for defense applications.
GE Healthcare leverages AI foundation models to analyze massive clinical datasets through their collaboration with AWS, developing industry-specific AI models using Amazon Bedrock. Their foundation models can be fine-tuned for specific medical tasks, enabling rapid diagnosis and personalized treatment recommendations by analyzing comprehensive patient histories in seconds rather than days.
CATL has advanced R&D infrastructure including a planned supercomputing center to enhance AI model development for discovering next-generation battery materials. This is complemented by their partnership with Tencent Cloud, which created an AI innovation base focused on quality inspection and computer vision applications.
Mitsui has developed sophisticated AI platforms for business innovation. Through their collaboration with IBM Watson, the company processes over five million external data points to identify new applications. Mitsui Chemicals has deployed this across business development and R&D functions, with over 20 business units utilizing AI-powered discovery tools that have generated more than 100 new product applications.
Leading manufacturers are integrating artificial intelligence into core manufacturing processes, customer service, quality control, and business operations, moving beyond isolated applications to transform essential functions.
Lockheed Martin has operationalized AI across defense and space applications through their HercFusion platform, which analyzes data from almost three million C-130J Super Hercules military transport aircraft flight hours to predict maintenance needs. Each aircraft generates 3GB of data per flight hour through 600 sensors, enabling predictive maintenance that shows a 3% increase in mission capability rate and a 15% reduction in fuel usage.
GE Healthcare has integrated AI into clinical workflows. Their CareIntellect platform aggregates and summarizes multimodal patient data to help clinicians at the point of care, improving patient outcomes and operational efficiency.
CATL has deployed AI throughout its operations, from predictive maintenance and supply chain optimization to automated quality inspection and customer service via chatbots and virtual assistants.
AVEVA, a subsidiary of Schneider Electric, demonstrates operational integration with an AI-infused hybrid Manufacturing Execution System (MES), launched in 2024. This solution combines edge-based sensor data with cloud-based AI analytics. It delivers tangible benefits by providing setup recommendations, anomaly notifications, and generative drill-down assistance to improve yield, quality, and energy efficiency. Maple Leaf Foods, for instance, reported a 10–12% gross profit increase by applying advanced analytics to the MES.
Siemens has deployed AI for failure detection and quality optimization across their Digital Lighthouse factories, which produce automation systems and industrial equipment. Their enhanced Senseye solution incorporates generative AI functionality, creating conversational interfaces that make maintenance operations more intuitive.
The most AI-mature manufacturing companies are investing in workforce development, recognizing that technological advancement requires corresponding enhancement in human capabilities and cultural adaptation to AI-augmented operations.
Lockheed Martin leverages its AI Center (LAIC) to provide essential AI tools, research, and training across diverse engineering teams, using a “hub and spoke” talent development model to unite engineers from different disciplines. The company has deployed its LMText Navigator generative AI tool across its US workforce to increase efficiency in code generation, data analysis, and business processes while ensuring security standards.
Mitsui has established the Mitsui DX Academy with comprehensive training programs tailored to specific business needs. The company has certified over 200 DX Business Professionals who bridge business and technology domains, with their Boot Camp program providing on-the-job training in practical DX projects.
Siemens has created comprehensive workforce development programs that combine traditional manufacturing expertise with AI competencies. Their Industrial Copilot platform is designed with intuitive interfaces that enable engineers to leverage AI capabilities without extensive coding knowledge, democratizing access to advanced technologies.
The adoption of AI in the manufacturing sector is creating competitive advantages in operational efficiency, innovation velocity, and market responsiveness.
As AI becomes central to manufacturing operations and safety-critical systems, leading companies have implemented comprehensive governance frameworks ensuring ethical deployment while maintaining operational excellence and regulatory compliance.
Lockheed Martin has established comprehensive ethical frameworks aligned with Department of Defense AI Ethics Principles – Responsible, Equitable, Traceable, Reliable, and Governable. The company founded its Artificial Intelligence Ethics Advisory Committee to ensure the development of explainable, robust, trustworthy AI models.
GE Healthcare’s approach addresses patient privacy and data security through HIPAA compliance and advanced security measures. Their collaboration with AWS ensures healthcare data remains protected while enabling AI-driven insights that improve patient care.
CATL has implemented governance frameworks that address data privacy, algorithmic transparency, and responsible AI deployment across their global operations, particularly important given their role in automotive safety systems and the critical nature of battery technology for electric vehicle performance.
Highlighting a commitment to safety, Schneider Electric announced a patent in early 2025 for an AI-driven technology designed to enhance process safety. This solution can automatically analyze potential process hazards and validate protection mechanisms by simulating a vast range of scenarios.
Mitsui has developed comprehensive governance frameworks that address data privacy, algorithmic transparency, and responsible AI deployment across its diverse global business portfolio, emphasizing collaborative benefits between departments while ensuring ethical use of customer and market data.
Siemens has established robust ethical frameworks emphasizing human-machine collaboration rather than replacement. Their achievement of TÜV safety certification for virtual programmable logic controllers – a rigorous German technical inspection standard for safety-critical industrial systems – demonstrates a commitment to maintaining the highest safety standards while embracing innovative AI applications.
“Manufacturing organizations positioned for success will systematically develop AI capabilities across all five of these dimensions while navigating complex operational requirements.”
The adoption of AI in the manufacturing sector is creating competitive advantages in operational efficiency, innovation velocity, and market responsiveness. Companies that excel across the five dimensions of AI maturity consistently outperform peers in productivity, quality, and customer value creation.
For manufacturing organizations seeking to enhance their own AI maturity, the examples set by industry leaders provide valuable guidance:
The manufacturing organizations positioned for success will systematically develop AI capabilities across all five of these dimensions while navigating complex operational requirements, safety considerations, and regulatory environments. This comprehensive approach will create resilient organizations capable of building intelligent, efficient, and sustainable manufacturing operations that will define the future of the industry.
IMD’s TONOMUS Global Center for Digital and AI Transformation provides proprietary analysis and executive education focused on helping organizations enhance their digital and AI maturity. If you are interested in the AI Maturity Index methodology or would like to assess your organization’s current capabilities, you’ll find more information here.
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