QGdata

Global AI Tool Adoption Across Industries

Artificial Intelligence

$800

Sold 0

17.63MB

Data Identifier:D17527199636615881

Publish Time:2025/07/17

Data Description

About Dataset

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

Column Name	Description
`country`	Country where the organization or user is located (e.g., USA, India, China, etc.)
`industry`	Industry sector of the organization (e.g., Technology, Healthcare, Retail, etc.)
`ai_tool`	Name of the AI tool used (e.g., ChatGPT, Midjourney, Bard, Stable Diffusion, Claude)
`adoption_rate`	Percentage representing the adoption rate of the AI tool within the sector or company (0–100)
`daily_active_users`	Estimated number of daily active users for the AI tool in the given context
`year`	Year in which the data was recorded (2023 or 2024)
`user_feedback`	Free-text feedback from users about their experience with the AI tool (up to 150 characters)
`age_group`	Age group of users (e.g., 18-24, 25-34, 35-44, 45-54, 55+)
`company_size`	Size category of the organization (Startup, SME, Enterprise)

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

age_group_stats = df.groupby('age_group')['adoption_rate'].mean()
print(age_group_stats)

Why This Dataset is Valuable

Comprehensive Coverage: Includes multiple countries, industries, and company sizes for cross-sectional and longitudinal analysis.
Rich for NLP: User feedback enables sentiment and topic modeling.
Ready for Machine Learning: Clean, structured data for regression, classification, clustering, and forecasting.
Business and Policy Insights: Benchmark AI adoption and inform digital transformation strategies.
Educational Utility: Ideal for teaching data wrangling, EDA, and applied machine learning.

Potential Research Questions

Which industries and regions are leading in AI tool adoption?
How do company size and user age group affect AI integration?
What are the most common sentiments and topics in user feedback?
How has AI tool adoption changed from 2023 to 2025?
Which AI tools are most popular and why?

Data Preparation and Quality

Format: CSV, UTF-8 encoding
Size: ~14MB, 145,000+ rows
Data Cleaning: Missing values handled, categorical fields standardized, free-text fields checked for length and encoding
Synthetic Data Generation: Follows best practices for data simulation, ensuring realistic distributions and relationships[3].