Organisational AI adoption: context for manufacturers
Stanford's AI Index Report 2025 reports that 78% of surveyed organisations used AI in 2024, compared with 55% in 2023. These are cross-industry survey results, not a manufacturing-specific adoption rate. They provide context for manufacturers, not a benchmark for an individual plant.
Source: Stanford HAI — AI Index Report 2025 (2025)
What it means
Reported adoption rose by 23 percentage points between 2023 and 2024. This indicates wider organisational use; it does not establish the return, reliability or suitability of an AI application at a particular plant.
Context
The report year is 2025, but these observations refer to 2023 and 2024. Survey scope and the definition of AI use matter: these figures should not be presented as the percentage of manufacturers using AI or as evidence of production deployment. A plant should evaluate its own use case, data quality and measured outcomes.
How to interpret this data
About the source: This data comes from Stanford HAI. Use the linked publication to check the observation year, population and definitions behind the chart. Access to a public summary does not imply that the underlying data is freely downloadable or licensed for reuse. Check the publisher's access and reuse terms before redistributing data.
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