The number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market worldwide was modeled to stand at ************** in 2024. Following a continuous upward trend, the number of AI tools users has risen by ************** since 2020. Between 2024 and 2031, the number of AI tools users will rise by **************, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Artificial Intelligence.

Artificial intelligence is one of the technological areas with the greatest economic projection in the short and medium term. So much so that its market value could exceed the 300 billion U.S. dollars mark by 2027. Alongside revenues, the number of users is also increasing, which could surpass the 500 million mark by 2028.

Global AI Tool Adoption Across Industries and Regions (2023–2025)

A comprehensive, research-grade dataset capturing the adoption, usage, and impact of leading AI tools—such as ChatGPT, Midjourney, Stable Diffusion, Bard, and Claude—across multiple industries, countries, and user demographics. This dataset is designed for advanced analytics, machine learning, natural language processing, and business intelligence applications.

Dataset Overview

This dataset provides a panoramic view of how AI technologies are transforming business, industry, and society worldwide. Drawing inspiration from real-world adoption surveys, academic research, and industry reports, it enables users to:

• Analyze adoption rates of popular AI tools across regions and sectors.
• Study user demographics and company profiles influencing AI integration.
• Explore textual user feedback for sentiment and topic modeling.
• Perform time series analysis on AI adoption trends from 2023 to 2025.
• Benchmark industries, countries, and company sizes for AI readiness.

To add a column descriptor (column description) to your Kaggle dataset's Data Card, you should provide a clear and concise explanation for each column. This improves dataset usability and helps users understand your data structure, which is highly recommended for achieving a 10/10 usability score on Kaggle[2][9].

Below is a ready-to-copy Column Descriptions table for your dataset. You can paste this into the "Column Descriptions" section of your Kaggle Data Card (after clicking the pencil/edit icon in the Data tab)[2][9]:

Column Descriptions

Example Data

country,industry,ai_tool,adoption_rate,daily_active_users,year,user_feedback,age_group,company_size
USA,Technology,ChatGPT,78.5,5423,2024,"Great productivity boost for our team!",25-34,Enterprise
India,Healthcare,Midjourney,62.3,2345,2024,"Improved patient engagement and workflow.",35-44,SME
Germany,Manufacturing,Stable Diffusion,45.1,1842,2023,"Enhanced our design process.",45-54,Enterprise
Brazil,Retail,Bard,33.2,1200,2024,"Helped automate our customer support.",18-24,Startup
UK,Finance,Claude,55.7,2100,2023,"Increased accuracy in financial forecasting.",25-34,SME

How to Use This Dataset

1. Load and Preview the Data

import pandas as pd

df = pd.read_csv('/path/to/ai_adoption_dataset.csv')
print(df.head())
print(df.info())

2. Analyze Adoption Rates by Industry and Country

industry_adoption = df.groupby(['industry', 'country'])['adoption_rate'].mean().reset_index()
print(industry_adoption.sort_values(by='adoption_rate', ascending=False).head(10))

3. Visualize AI Tool Popularity

import matplotlib.pyplot as plt

tool_counts = df['ai_tool'].value_counts()
tool_counts.plot(kind='bar', title='AI Tool Usage Distribution')
plt.xlabel('AI Tool')
plt.ylabel('Number of Records')
plt.show()

4. Sentiment Analysis on User Feedback

from textblob import TextBlob

df['feedback_sentiment'] = df['user_feedback'].apply(lambda x: TextBlob(x).sentiment.polarity)
print(df[['user_feedback', 'feedback_sentiment']].head())

5. Time Series Analysis of Adoption Trends

yearly_trends = df.groupby(['year', 'ai_tool'])['adoption_rate'].mean().unstack()
yearly_trends.plot(marker='o', title='AI Tool Adoption Rate Over Time')
plt.xlabel('Year')
plt.ylabel('Average Adoption Rate (%)')
plt.show()

**6. Demographic Insights*...

How confident are you that you can tell the difference between what is true and what is false when it comes to information on artificial intelligence, or AI, chatbots such as ChatGPT, Microsoft Copilot, or Google Gemini?. Notes: AI users are those who say that they use or interact with artificial intelligence. See topline for full question wording.

The Daily AI Assistant Usage Behavior Dataset captures real-world interaction patterns between users and AI assistants throughout their day. It includes details such as query types, time-of-day usage, session duration, device type, user intent, and follow-up behavior.

This dataset is designed to help researchers, developers, and data enthusiasts analyze how people rely on AI tools for productivity, creativity, learning, and routine tasks. It is ideal for building models around user behavior prediction, recommendation systems, personalization, and conversational AI improvements.

The largest share of AI users in the workplace in the United States was recorded in the technology sector, at ** percent in the fourth quarter of 2025, followed by finance at ** percent and the college and university sector at ** percent. The organizational adoption of AI in all presented industries increased from the previous quarter or stayed the same.

Explore Character AI Stats for unmatched user trends, engagement and insights, learn the surprising benefits that capture attention now.

Character.ai is one of the many AI chatbots to explode in popularity, as the success of ChatGPT has led millions of people to find alternative chatbots offering different experiences. In...

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This dataset focuses on understanding user trust, skepticism, and evaluation behavior toward AI-generated responses across different AI models and query types. It contains 1,000 records that capture how users perceive AI answers based on confidence levels, accuracy, detail, and transparency features such as citations, disclaimers, and hedging language. The dataset also includes user-related attributes like age group, education level, digital literacy, AI familiarity, and subject-matter expertise, allowing analysis of how demographic and cognitive factors influence trust in AI. Additionally, it tracks whether users performed fact-checking, the methods they used, time spent on verification, and how these actions relate to final trust scores and skepticism categories. Overall, this dataset is designed to support research on AI credibility, trust calibration, and human decision-making when interacting with AI systems.

context

This dataset was created to provide context for how people interact with and judge AI-generated information in real-world scenarios. As AI tools are increasingly used for learning, decision-making, and problem-solving, users often rely on AI responses without fully understanding their accuracy or limitations. The dataset captures user reactions to AI outputs across different models and types of questions, focusing on trust, skepticism, and verification behavior. It reflects the growing need to study how transparency, confidence, and explanation quality influence whether users accept, question, or verify AI-generated content. By combining user demographics, AI familiarity, and evaluation behavior, this dataset offers a realistic context for analyzing human–AI interaction and for improving responsible AI design, trust calibration, and user awareness in AI-assisted environments.

This dataset explores factors that influence human trust in AI systems by analyzing characteristics of AI-generated responses and associated metadata. It is designed to support research in human–AI interaction, explainable AI, user perception, and trust calibration. The dataset captures both objective properties of AI outputs (e.g., response length, presence of citations) and subjective or contextual indicators (e.g., confidence signaling, answer detail level) that may affect how users perceive and trust AI systems.

Context & Inspiration: The dataset was inspired by growing reliance on AI assistants in decision-making, education, and information retrieval. Understanding which response traits foster or undermine trust can help designers build safer, more transparent, and more user-aligned AI systems.

Potential Use Cases:

• Trust modeling and prediction

• Human–AI interaction studies

• UX evaluation of AI assistants

• Comparative analysis of AI models

• Academic research and teaching datasets

In 2025, the highest share of artificial intelligence (AI) users was in the age group of 18 to 34 years old, with ** percent having used AI tools. Those aged between 65 and 84 had the lowest AI usage rate, with ** percent.

Explore key AI usage statistics, uncover adoption rates, industry trends, user demographics, and how artificial intelligence is transforming!

In the U.S. technology sector, ** percent of respondents reported using artificial intelligence (AI) at work daily in the last quarter of 2024, the highest figure among the presented industries. Finance and professional services followed with a daily organizational AI adoption of ** percent and ** percent, respectively. The technology sector also ranked first by total, not only daily, workplace AI user share in the U.S.

The ChatGPT Users Reviews Dataset contains structured user feedback including ratings, textual reviews, timestamps, and sentiment indicators. It is designed for sentiment analysis, text classification, opinion mining, and NLP model benchmarking.

When asked about how their organizations are adjusting their talent strategies due to the adoption of artificial intelligence (AI), over half of AI leaders worldwide named educating a broader workforce to raise overall AI fluency in 2025. The second-most common adjustment was designing and implementing strategies for employee reskilling and upskilling.

Explore essential Claude AI statistics, from user growth and adoption trends to performance insights shaping this rising AI model!

The application of artificial intelligence (AI) systems has surged in the high-risk area of medicine, and these systems must explain their decisions to different users. However, existing explainable AI (XAI) design practices in the medical domain are mostly focused on domain experts, such as physicians, and there is a lack of XAI design practices for consumer users. Therefore, we developed a library of XAI user needs in the medical domain, which can be used as an auxiliary tool for the development of user-centered XAI design solutions in this domain. Moreover, through empirical research, based on our XAI user Needs Library, we designed an XAI-based electrocardiogram diagnostic system prototype for consumer users and conducted a user evaluation. The results provide the empirical experience of the design space of XAI and promote consumer user-centered XAI practices.

Artificial intelligence (AI) companion apps let users have virtual relationships and friendships with AI systems and bots. As of february 2025,Character AI app collected 15 types of data from its users on iOS worldwide. EVA AI ranked second with 11 unique data points collected from its users. Among the examined AI companion apps, Kindroid collected the least number of unique data points as of the examined period.

AI has already changed and will continue to change the way that we live. These are the latest Artificial Intelligence statistics you need to know.

The Large-Scale AI Models database documents over 200 models trained with more than 10^23 floating point operations, at the leading edge of scale and capabilities.