Imagine a world where your doctor’s diagnosis is assisted by a machine learning model, your home anticipates your needs before you speak, and your company's biggest asset is no longer its workforce, but its data. That’s not a glimpse of a distant future; it's the reality we’re living in. As...

While artificial intelligence (AI) saw a staggering growth in adoption rates from 2017 to 2018, it has leveled off significantly since 2019. It grew nearly *** times in 2022 compared to its adoption rate in 2017. Much of this can be attributed to AI being more understood as an inherent tool of optimizing business and operations in 2022. It is less amazingly novel and rather an understood factor of value-adding in businesses.

Country-level AI adoption indicators and sector breakdown for the United States in 2025, combining public datasets and modeled estimates.

Overview

The "AI Adoption & Automation Risk (San Francisco, CA)" dataset offers a comprehensive overview of the local job market, focusing on the interplay between artificial intelligence, automation, and employment trends in the San Francisco Bay Area.

This synthetic yet realistic dataset includes a diverse range of job listings, each categorized by industry, AI adoption level, automation risk, required skills, and projected job growth. It serves as a valuable resource for researchers, data scientists, and policymakers investigating the impact of AI on the workforce and the future of work in the region.

Dataset Features

1. Job Title: Description: The title of the job role. Type: Categorical Example Values: "Data Scientist", "Software Engineer", "HR Manager"

2. Industry: Description: The industry in which the job is located. Type: Categorical Example Values: "Technology", "Healthcare", "Finance"

3. AI Adoption Level: Description: The extent to which the company has adopted AI in its operations. Type: Categorical Categories: "Low", "Medium", "High"

4. AI Adoption Score Description: The numerical equivalence of the AI Adoption Level column. Type: Numerical Categories: "1", "2", "3"

5. Automation Risk: Description: The estimated risk that the job could be automated within the next 10 years. Type: Categorical Categories: "Low", "Medium", "High"

6. Automation Risk Score: Description: The numerical equivalence of the Automation Risk Level column. Type: Numerical Categories: "1", "2", "3"

7. Required Skills: Description: The key skills required for the job role. Type: Categorical Example Values: "Python", "Data Analysis", "Project Management"

8. Salary (USD): Description: The annual salary offered for the job in USD. Type: Numerical Value Range: $30,000 - $200,000

9. Job Growth Projection: Description: The projected growth or decline of the job role over the next five years. Type: Categorical Categories: "Decline", "Stable", "Growth"

10. Job Growth Score: Description: The numerical equivalence of the Job Growth column. Type: Numerical Categories: "1", "2", "3"

Potential Uses - Upskilling and reskilling: Focusing on skills less susceptible to automation, such as critical thinking, problem-solving, and complex communication. - Fostering innovation: Encouraging a culture of experimentation and innovation to find new ways to leverage AI for competitive advantage. - Diversifying skill sets: Promoting cross-functional collaboration and developing soft skills to reduce reliance on purely technical skills. - Strategic planning: Monitoring industry trends and developing contingency plans to adapt to changes. - Ethical considerations: Addressing the ethical implications of AI adoption and automation.

Notes

This synthetic dataset is designed to simulate the modern job market, focusing on AI adoption and automation trends in San Francisco. While it closely mirrors real-world data, it's important to note that it's not derived from actual companies, job listings, or individuals. This dataset is intended for educational and research purposes and can be used to model, predict, and analyze trends in the AI-driven workforce. However, it's crucial to validate any findings against real-world data before making decisions based solely on this synthetic dataset.

The adoption rate of artificial intelligence (AI) is expected to rapidly grow in the information technology sector (IT). In 2022, nearly ** percent of IT executives expected their companies to have widescale adoption in AI in their respective companies.

Comprehensive collection of AI adoption rates, marketing automation metrics, EU AI Act compliance data, healthcare AI statistics, and workflow automation ROI figures from leading research institutions.

Singapore was the nation with the highest combined value where enterprises were exploring or had actively deployed AI within their business in 2023. China, India, and the UAE were all close behind, with over ** percent of respondents claiming exploration or deployment of AI. Western countries, in particular European mainland nations such as France, Germany, and Italy, had the highest rate of non-usage or no exploration of AI, though even the U.S. had a similar share of enterprises not engaged with AI. This may reflect the specialized industries that thrive in those countries, needing individualized human skills to operate.

This section provides an in-depth examination of the dataset utilized for quantitative analysis in this study. The dataset comprises comprehensive information on thirty companies spanning the Manufacturing, Retail, and Logistics sectors, carefully curated to reflect contemporary supply chain environments. Sourced from Kaggle, a renowned open-data platform, the dataset was selected for its relevance to AI adoption and operational performance metrics. The inspiration behind this dataset stems from the growing interest in understanding how AI technologies reshape supply chain dynamics across diverse industries. By incorporating variables such as AI adoption rates, financial investments, productivity indices, and sustainability indicators, the dataset offers a multifaceted view that aligns with the study’s objectives. The dataset’s design mirrors real-world business scenarios, enabling meaningful statistical analysis and providing insights into the complex relationship between AI integration and supply chain effectiveness. Through this rich data foundation, the study aims to explore both the measurable impacts of AI and the contextual factors that influence its adoption, setting the stage for the combined quantitative and qualitative analyses that follow.

Country-level AI adoption indicators and sector breakdown for China in 2025, combining public datasets and modeled estimates.

During a 2023 survey conducted among professionals in the United States, it was found that 37 percent of those working in advertising or marketing had used artificial intelligence (AI) to assist with work-related tasks. Healthcare, however, had the lowest rate of AI usage with only 15 percent of those asked having used it at work. The rate of adoption in marketing and advertising is understandable, as it is the industry that most weaves together art and creative mediums in its processes.

Generative AI linked to education

Those positions that require a higher level of education are most at risk of being automated with generative AI in the U.S. This is simply because those jobs that require less formal education are rarely digital positions and are more reliant on physical labor. Jobs that require tertiary education, however, are still the least likely to be automated overall, even with the added influence of generative AI.

ChatGPT has competitors

While the OpenAI-developed ChatGPT is the most well-known AI program and the currently most advanced large language model, - other competitors are catching up. While just over half of respondents in the U.S. had heard of or used ChatGPT, nearly half of respondents had also heard of or used Bing Chat. Google’s Bard was slightly behind, with only around a third of Americans having heard of or used it.

Country-level AI adoption indicators and sector breakdown for India in 2025, combining public datasets and modeled estimates.

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*...

A structured dataset of 150+ AI statistics for 2025 including market size, AI adoption, consumer behavior, generative AI usage, automation impact, enterprise metrics, and future predictions.

Tech, media, and telecoms industries were the most diligent adopters of AI in 2024, with some ** percent of respondents using AI in their business. AI was most used in the product and/or service development functions, with only those working in consumer goods and retail using it less than ** percent.

Country-level AI adoption indicators with sector breakdown for 2025, combining public datasets and modeled estimates.

A curated dataset of AI in marketing adoption and investment trends for 2025, covering CMO budgets, SMB adoption, productivity impact, retailer adoption, and global ad spend share.

The adoption rate of artificial intelligence (AI) is expected to gain considerable importance in product development companies worldwide between 2022 and 2025. Currently, companies operating in that sector were mostly, or ** percent, reporting limited adoption of AI in their production cycles. Technology executives expected this to change considerably by 2025.

These files are supplementary files for the “Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges” study, prepared for the Hawaii International Conference on System Sciences (HICSS 2026)

File "Data_AI_challenges.docx" presents the list of data-AI challenges surrounding AI adoption in public sector as identified through the SLR, with SLR results available in the file "Data_AI_challenges_SLR".

File "Responsible_AI_Adoption_AI_Challenges_public_sector_AN.pdf" a pre-print version of the manuscript prepared for the Hawaii International Conference on System Sciences (HICSS 2026). It is posted here for your personal use. Not for redistribution. Digital Object Identifier (DOI) and link to the article will be added once they are assigned.

Abstract: Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface “symptoms” of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption.

Please cite this paper as:

Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Casiano Flores, C., Sirait, E., Luterek, M., Dreyling, R. M., and Tesarova, B. (2025). Responsible AI Adoption in the Public Sector: A Data Centric Taxonomy of AI Adoption Challenges. In Proceedings of the 59th Hawaii International Conference on System Sciences

This data is available for the research with title: "Understanding the Adoption of Artificial Intelligence in Journalism: An Empirical Study in Vietnam"

The dataset consists of responses collected via an online questionnaire targeting Generation Z individuals in Portugal. It focuses on understanding the adoption of AI-driven chatbots in the tourism and hospitality industries. The data includes demographic information, behavioral variables, and responses to constructs from the AI Device Use Acceptance (AIDUA) model, such as emotional reaction, performance expectancy, anthropomorphism, and social influence.