The 2025 AI Maturity Index is composed of the top 300 companies listed in the 2024 Forbes 2000 list. The scoring for each company is a summation of the resulting score in each of the three components below: 

1. Content similarity analysis

Machine learning algorithms were used to analyze the content similarity of corporate texts, which included 2024 corporate annual reports, 2024 and 2025 Q1and Q2 analyst quarterly calls, and 2024 and 2025 Q1 and Q2 company press releases against two benchmark texts 1) HBR 10 Must Reads on AI, published in 2023, selected for its popularity and broad perspective on AI transformation; and 2) UNESCO’s 2023 Ethical Impact Assessment on AI, chosen for its coverage of the entire AI lifecycle.  

Two types of similarities were measured: lexical similarity (which captures the number of words in common) and semantic similarity (which captures the similarity of the meanings of those words). To measure both types of similarities, a combination of cosine similarity (which accounts for lexical similarity) and text embedding (which accounts for semantic similarity) were used.   

An analysis was conducted on each of the three data sources—annual reports, analyst quarterly calls, and company press releases.  The average of the three data sets was used as the final score.    

2. Number of AI-related keywords

A list of AI-related keywords was developed via desk research and with categorization algorithms.  This process generated 18 distinct categories, with each category consisting of an average of 5 keywords.  Each company received a score (for each of the three data sources) ranging between 0-13, depending on how many keywords in in each of the categories were mentioned.  The scores from each data source were standardized for each company. The final score was calculated as the average of the Z-scores from each data source.

3. AI deals and amounts invested

Using data from Pitchbook, the number of AI-related deals from 2020 to June 2025 was collected. Additionally, the investment amounts (in million US dollars) for each company, during the same period, were also recorded  

Both AI-related deals and investments were normalized using the ln(𝑛+1) transformation. Each variable—the number of deals and the investment amounts—was then standardized separately, and then averaged to produce a final score.