How widely manufacturers have adopted AI
Enterprise AI use jumped from about 35% of organisations in 2023 to roughly 67% in 2025, according to the Stanford AI Index. In manufacturing specifically, surveys put the share of firms using AI in some form around half. Adoption has moved from early experiment to mainstream within a few years.
Source: Stanford HAI — AI Index Report 2025 (2025)
What it means
Doubling in roughly two years means AI is no longer a competitive edge for early movers — it is becoming table stakes. For a plant that has not started, the relevant question has shifted from 'should we?' to 'where do we get the fastest, lowest-risk return?' — usually maintenance, energy or quality.
Context
Adoption figures vary by survey and by how 'using AI' is defined, but the trend is consistent across sources: a steep rise in the share of organisations deploying AI, including in manufacturing and heavy industry. The practical reading is that the technology and the skills to apply it are diffusing quickly, lowering the risk of being an early adopter.
How to interpret this data
About the source: This data comes from Stanford HAI. Public datasets like this are the foundation of fact-based decision-making in industry. When you see these numbers cited in vendor proposals or consultant reports, remember: the raw data is freely available, and the value is in how you interpret it for your specific plant and situation.
Where this matters: AI agents for industrial maintenance, Generative AI in manufacturing are built on insights like the data shown here. Rather than treat data in isolation, read the deeper guides to see how these trends translate into actionable levers for your plant.
Sector relevance: This dataset is especially relevant to Chemicals, Food Processing. These sectors face the trends and challenges you see in this chart daily — energy cost pressure, the push for decarbonization, adoption of AI and predictive maintenance. Use this data to benchmark your plant against the industry average and identify where you lag or lead.
How to use this data: Take the headline number but look deeper at the chart. Is it growing or shrinking? Which segments or regions drive the trend? Does your plant's data align, or are you an outlier? Outliers are often where the best opportunities hide — either an efficiency gap you can exploit, or a leading practice you can copy.
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Related topics
AI Agents for Industrial Maintenance: What They Are and Where They Help · Generative AI in Manufacturing: Practical Examples · Generative AI · Large Language Model (LLM) · Machine Learning (Industrial)
Relevant to: Chemicals · Food Processing · Pharmaceuticals