AI Adoption in Manufacturing: Insights from 100 Companies
Our analysis of the top 100 manufacturing companies by revenue from the Forbes Global 2000, spanning automotive, industrial equipment, chemicals, consumer electronics, and more across 15 countries, reveals two clear patterns in how manufacturers approach artificial intelligence.
We evaluated three key metrics across all 100 companies: AI partnerships, open-source contributions, and AI initiative outputs. Our team verified each data point across multiple sources, including company press releases, SEC filings, GitHub repositories, and industry databases, conducting manual reviews to filter out generic “digital transformation” announcements from genuine AI adoption.
Our analysis reveals two clear patterns in how manufacturers approach artificial intelligence:
- 89 companies formed at least one AI partnership, but only 7 contribute to open-source AI projects
- Strategic partnerships focus on cloud platforms and joint ventures, while open-source contributions remain limited to industry-specific tools
We examined two key indicators of AI maturity: strategic partnerships (including startup investments and joint ventures) and open-source contributions to AI projects.
Metrics | Companies with contributions | Leading sectors | Most frequent partners |
|---|---|---|---|
AI Partnerships | 89 of 100 | Automotive (32%), Industrial Equipment (28%), Electronics (15%) | Microsoft (12), NVIDIA (11), Huawei (7) |
Open-Source Contributions | 7 of 100 | Industrial Equipment (43%), Automotive (29%), Energy/Battery (28%) | BMW Group, Siemens, Contemporary Amperex Technology/CATL, Caterpillar, GE Vernova |
AI Adoption Output | Siemens (9 initiatives), GE Vernova (4) | AI-enabled products, predictive maintenance, automation tools | Siemens (9 initiatives), GE Vernova (4 initiatives), KIA (2 initiatives), John Deere (2 initiatives) |
Methodology
1. Data Collection
Partnership data came from three sources: company press releases, SEC filings, and industry databases. We searched for announcements between 2020 and 2025 that mentioned joint ventures, technology agreements, or research collaborations involving AI.
For open-source contributions, we searched GitHub for official company accounts and verified repositories with AI-related keywords. We tracked repository activity and licensing, and confirmed corporate affiliation for each entry.
All data was verified through multiple sources with an “AI Term Found” classification to distinguish genuine AI initiatives from generic technology announcements.
2. Verification and Filtering
Manufacturing companies announce “digital transformation initiatives” frequently, most of which involve no AI at all. We applied a keyword filter that searched page text for specific terms: AI, machine learning, deep learning, data science, and related variants.
- Status code filtering: Only live pages (200 or 301) were accepted. Redirects were manually reviewed to confirm relevance.
- Keyword validation: Entries were marked relevant only when AI-related terms appeared more than once in the page body. Single mentions and domain-level references (e.g., “.ai” in URLs) were excluded.
- Manual inspection: For companies with ambiguous results, researchers reviewed the page content directly to confirm whether the findings reflected actual AI adoption or unrelated references. This step removed results, such as sustainability reports, that were mistakenly surfaced as AI-related.
3. Metrics Used
Following multiple test runs and revisions, five metrics were finalized:
We started with five metrics, but only three gave us usable data.
- Partnerships and open-source contributions had concrete numbers we could track. Each partnership had a date, companies involved, and documentation. GitHub repositories showed commit history and activity levels. These made it straightforward to build charts.
- Employee training and use cases didn’t work out. Companies mention “AI training programs” in annual reports without explaining what employees actually learned.
- AI Adoption Output turned out to duplicate partnership data. A company announcing an “AI initiative” was usually just describing a partnership we’d already counted.
1. AI Partnerships
Manufacturers are increasingly partnering with major technology companies to build AI capabilities. The table below shows how manufacturers structure these collaborations. Each entry represents a verified partnership announcement between 2020 and 2025, categorized by sector, partnership type, and technology partner.
Partnership Types
Technology agreements account for 68% of partnerships. These give manufacturers access to cloud platforms and pre-built models without building internal capabilities. GM processes vehicle data through Microsoft Azure; Toyota runs autonomous driving simulations on NVIDIA infrastructure.
Joint ventures account for 18%. These involve shared investment and longer development timelines. HD Hyundai and Palantir built a data analytics platform for shipbuilding. CNH Industrial created a joint venture with AI startups focused on autonomous farming equipment. Continental partnered with Horizon Robotics to manufacture AI chips for vehicles.
Research partnerships represent 14%. Siemens collaborates with technical universities on industrial AI. Hitachi funds research labs working on predictive maintenance algorithms. BASF partners with AI institutes to optimize chemical processes.
Industry Patterns
Automotive companies formed the most partnerships 32% of the total. General Motors and Hyundai Motor each have 10. Autonomous driving requires integration across sensors, computing, and decision-making systems, which creates pressure to work with multiple AI providers simultaneously.
Industrial equipment manufacturers account for 28% of partnerships. Caterpillar partners with cloud providers for equipment monitoring. Komatsu is working with NVIDIA on construction-site automation.
Electronics and component makers represent 15%. These companies typically partner to integrate AI into their products rather than their manufacturing processes.
Geographic Distribution
China leads in partnership volume. CRRC formed 8 deals, Great Wall Motor 7, Midea Group 6 driven by government digitalization policies.
European companies spread partnerships across provider types, typically forming 3–4 deals mixing cloud services, university research, and specialized AI firms.
North American companies Ford and Lockheed Martin are representative and tend to concentrate on one or two major tech partners rather than diversifying.
2. AI Open-Source Contributions
Only 7 companies contribute to open-source AI—compared to 89 forming partnerships. When they do contribute, it’s narrow: battery simulation software or tire analysis tools, not frameworks like PyTorch.
Figure 2. Companies with AI Open-Source Contributions (2020–2025)
Contribution Patterns
Most open-source work addresses specific manufacturing problems that general AI libraries do not cover. Siemens released anomaly-detection models to detect unusual sensor patterns on production lines. Caterpillar added modules to Apache Spark for handling IoT sensor data from heavy equipment. GE Vernova contributed energy-forecasting models to Apache PredictionIO, focusing on the data infrastructure layer that feeds AI systems rather than on the models themselves.
BMW Group leads in computer vision contributions, releasing simulation tools that allow researchers to test autonomous driving perception systems without physical vehicles. Bridgestone contributed tire analysis algorithms to OpenCV for detecting wear patterns and road conditions.
The consistent pattern: companies release tools that solve problems unique to their industry. This contrasts with technology companies, where contributing to major AI frameworks is standard practice. In manufacturing, open-source is treated as a way to share niche tooling, not as a core development strategy.
3. AI Adoption Output
This metric captured the number of identifiable AI initiatives per company.
The findings showed a concentration among a few leaders: Siemens (9 initiatives), GE Vernova (4), and several others with one or two projects. These initiatives include AI-enabled products, predictive maintenance systems, and automation tools.
Figure 3. Number of AI Initiatives by Company (Top 10)
Key Findings
Partnerships Are the Default Strategy
- 89 companies formed at least one AI partnership since 2020. Microsoft, NVIDIA, and Huawei appear most frequently—together accounting for 30% of all partnerships.
- Most manufacturers skip internal AI development and go straight to established providers. The logic is straightforward: cloud platforms deploy faster than building in-house capabilities.
Open-Source Barely Registers
- Only 7 companies contribute to public AI projects. The gap is stark 12 times more manufacturers partner than share code.
- When they do contribute, it’s industry-specific tools: battery simulation, tire analysis, equipment monitoring. Nobody’s publishing general-purpose AI models or contributing to frameworks like TensorFlow.
Investment Levels Vary Wildly
- Siemens leads with 9 AI initiatives. GE Vernova and KIA follow with fewer but consistent projects. Most other companies have 1-2 partnerships and nothing else.
- This suggests manufacturers are testing AI rather than committing to it. A couple of pilot projects doesn’t indicate deep investment.
Automotive and Industrial Equipment Pull Ahead
- Automotive represents 32% of partnerships. General Motors and Hyundai Motor both have 10 partnerships each—the highest counts.
- Autonomous driving creates pressure to integrate multiple AI systems: computer vision, sensor processing, decision algorithms, simulation. Companies can’t build all of this alone.
- Industrial equipment manufacturers follow similar patterns. Caterpillar and Komatsu need AI for equipment monitoring and automation.
- Chemical and materials producers move slower. BASF has research partnerships, but most chemical manufacturers show minimal AI activity.
Closed Innovation Wins
Manufacturers buy AI through partnerships but keep their implementations private. They’ll license Microsoft Azure but won’t publish their predictive maintenance algorithms.
Furher Reading
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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