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  "documentTitle": "2025 AI Maturity Index 2025",
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      "text": "Using PCA to create an index has two main benefits: Simplification and Objective representation.",
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      "text": "Simplification: PCA helps condense large amounts of information from different questions into a smaller number of key components. This helps generate an index without losing important details.\nObjective representation: PCA ensures that the index reflects the most important patterns in the data, without being influenced by any specific set of factors. This means the index gives a fair and accurate picture of responses, making it more reliable for decision-making.",
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      "text": "This report represents year two of the Enterprise AI Maturity franchise. In partnership with Oxford Economics, we surveyed directors, senior directors, and the C-suite of 4,473 organizations in 16 countries around the globe.",
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      "text": "We then calculated scores for the five dimensions using principal component analysis (PCA). PCA is a statistical method to simplify complex data from a large number of survey responses by transforming it into a smaller set of uncorrelated variables called principal components. PCA is widely used for dimensionality reduction in data analysis and machine learning and therefore lends itself well to index creation.",
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      "text": "The Enterprise AI Maturity Index is based on responses to our global survey, which we analyzed using statistical and econometric modeling techniques. The Index serves as a tool for organizations to better understand their performance in relation to others in their market or industry. Given these objectives, ServiceNow and Oxford Economics defined five dimensions of AI maturity: strategy and leadership, workflow integration, talent and workforce, AI governance, and realizing value in AI investment. Specific questions in the survey were written to understand organizations' maturity based on each of these dimensions.",
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      "text": "The PCA scores are standardized on a scale ranging from zero to 100—with zero representing no enterprise AI maturity and 100 representing full enterprise AI maturity—to enable comparisons. The standardized scores for the five dimensions are then combined and equally weighted to generate a single Enterprise AI Maturity Index rating. The choice of equal weights reflects the researchers' view that all five dimensions are equally important in defining the AI maturity of an organization.",
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