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.

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AI Training Dataset Market Size 2025-2029

The ai training dataset market size is valued to increase by USD 7.33 billion, at a CAGR of 29% from 2024 to 2029. Proliferation and increasing complexity of foundational AI models will drive the ai training dataset market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Service Type - Text segment was valued at USD 742.60 billion in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 479.81 million 
Market Future Opportunities 2024: USD 7334.90 million
CAGR from 2024 to 2029 : 29%

Market Summary

The market is experiencing significant growth as businesses increasingly rely on artificial intelligence (AI) to optimize operations, enhance customer experiences, and drive innovation. The proliferation and increasing complexity of foundational AI models necessitate large, high-quality datasets for effective training and improvement. This shift from data quantity to data quality and curation is a key trend in the market. Navigating data privacy, security, and copyright complexities, however, poses a significant challenge. Businesses must ensure that their datasets are ethically sourced, anonymized, and securely stored to mitigate risks and maintain compliance. For instance, in the supply chain optimization sector, companies use AI models to predict demand, optimize inventory levels, and improve logistics. Access to accurate and up-to-date training datasets is essential for these applications to function efficiently and effectively. Despite these challenges, the benefits of AI and the need for high-quality training datasets continue to drive market growth. The potential applications of AI are vast and varied, from healthcare and finance to manufacturing and transportation. As businesses continue to explore the possibilities of AI, the demand for curated, reliable, and secure training datasets will only increase.

What will be the size of the AI Training Dataset Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleThe market continues to evolve, with businesses increasingly recognizing the importance of high-quality datasets for developing and refining artificial intelligence models. According to recent studies, the use of AI in various industries is projected to grow by over 40% in the next five years, creating a significant demand for training datasets. This trend is particularly relevant for boardrooms, as companies grapple with compliance requirements, budgeting decisions, and product strategy. Moreover, the importance of data labeling, feature selection, and imbalanced data handling in model performance cannot be overstated. For instance, a mislabeled dataset can lead to biased and inaccurate models, potentially resulting in costly errors. Similarly, effective feature selection algorithms can significantly improve model accuracy and reduce computational resources. Despite these challenges, advances in model compression methods, dataset scalability, and data lineage tracking are helping to address some of the most pressing issues in the market. For example, model compression techniques can reduce the size of models, making them more efficient and easier to deploy. Similarly, data lineage tracking can help ensure data consistency and improve model interpretability. In conclusion, the market is a critical component of the broader AI ecosystem, with significant implications for businesses across industries. By focusing on data quality, effective labeling, and advanced techniques for handling imbalanced data and improving model performance, organizations can stay ahead of the curve and unlock the full potential of AI.

Unpacking the AI Training Dataset Market Landscape

In the realm of artificial intelligence (AI), the significance of high-quality training datasets is indisputable. Businesses harnessing AI technologies invest substantially in acquiring and managing these datasets to ensure model robustness and accuracy. According to recent studies, up to 80% of machine learning projects fail due to insufficient or poor-quality data. Conversely, organizations that effectively manage their training data experience an average ROI improvement of 15% through cost reduction and enhanced model performance.

Distributed computing systems and high-performance computing facilitate the processing of vast datasets, enabling businesses to train models at scale. Data security protocols and privacy preservation techniques are crucial to protect sensitive information within these datasets. Reinforcement learning models and supervised learning models each have their unique applications, with the former demonstrating a 30% faster convergence rate in certain use cases.

Data annot

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

API Integration: Our datasets are easily accessible via API, allowing for seamless integration into your existing systems. This ensures that you can automate data retrieval and update processes, maintaining the flow of fresh, accurate information directly into your applications.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understandin...

Dataset Licensing for AI Training Market Outlook

According to our latest research, the global Dataset Licensing for AI Training market size reached USD 2.1 billion in 2024, with a robust CAGR of 22.4% projected through the forecast period. By 2033, the market is expected to achieve a value of USD 15.2 billion. This remarkable growth is primarily fueled by the exponential rise in demand for high-quality, diverse, and ethically sourced datasets required to train increasingly sophisticated artificial intelligence (AI) models across industries. As organizations continue to scale their AI initiatives, the need for compliant, scalable, and customizable licensing solutions has never been more critical, driving significant investments and innovation in the dataset licensing ecosystem.

A primary growth factor for the Dataset Licensing for AI Training market is the proliferation of AI applications across sectors such as healthcare, finance, automotive, and government. As AI models become more complex, their hunger for diverse and representative datasets intensifies, making data acquisition and licensing a strategic priority for enterprises. The increasing adoption of machine learning, deep learning, and generative AI technologies further amplifies the need for specialized datasets, pushing both data providers and consumers to seek flexible and secure licensing arrangements. Additionally, regulatory developments such as GDPR in Europe and similar data privacy frameworks worldwide are compelling organizations to prioritize licensed, compliant datasets over ad hoc or unlicensed data sources, further accelerating market growth.

Another significant driver is the growing sophistication of dataset licensing models themselves. Vendors are moving beyond traditional open-source or proprietary licenses, introducing hybrid, creative commons, and custom-negotiated agreements tailored to specific use cases and industries. This evolution is enabling AI developers to access a broader variety of data types—text, image, audio, video, and multimodal—while ensuring legal clarity and minimizing risk. Moreover, the rise of data marketplaces and third-party platforms is streamlining the process of dataset discovery, negotiation, and compliance monitoring, making it easier for organizations of all sizes to source and license the data they need for AI training at scale.

The surging demand for high-quality annotated datasets is also fostering partnerships between data providers, annotation service vendors, and AI developers. These collaborations are leading to the creation of bespoke datasets that cater to niche applications, such as autonomous driving, medical diagnostics, and advanced robotics. At the same time, advances in synthetic data generation and data augmentation are expanding the universe of licensable datasets, offering new avenues for licensing and monetization. As the market matures, we expect to see increased standardization, transparency, and interoperability in licensing frameworks, further lowering barriers to entry and accelerating innovation in AI model development.

Regionally, North America continues to dominate the Dataset Licensing for AI Training market, accounting for the largest share in 2024, driven by the presence of leading technology companies, robust regulatory frameworks, and a mature AI ecosystem. Europe follows closely, with significant investments in ethical AI and data governance initiatives. Asia Pacific is emerging as a high-growth region, fueled by rapid digital transformation, government-backed AI strategies, and a burgeoning startup landscape. Latin America and the Middle East & Africa are also witnessing increased adoption of licensed datasets, particularly in sectors such as healthcare and public administration, although their market shares remain comparatively smaller. This global momentum underscores the universal need for high-quality, licensed datasets as the foundation of responsible and effective AI training.

License Type Analysis

The License Type segment in the Dataset Licensing for AI Training market is characterized by a diverse range of options, including Open Source, Proprietary, Creative Commons, and Custom/Negotiated licenses. Open source licenses have long been favored by academic and research communities due to their accessibility and collaborative ethos. However, their adoption in commercial AI projects is often tempered by concerns over data provenance, usage restrictions, a

More and more companies use artificial intelligence (AI). Research aimed to understand acceptance from the perspective of AI users or people affected by AI decisions. However, the perspective of decision-makers in companies (i.e., managers) has not been considered. To address this gap, we investigate managers’ acceptance of AI usage in companies, focusing on two potential determinants. Across four experimental studies (Ntotal = 2025), we tested whether the business area (i.e., human resources vs. finances/ marketing) and AI functionality affect managers’ acceptance of AI (i.e., perceived risk of negative consequences, willingness to invest). Findings indicate that managers (a) perceive more risk of and (b) are less willing to invest in AI usage in human resources than in finances and marketing. Besides, the results suggest that acceptance declines if functionality crosses a critical boundary and AI autonomously implements decisions without prior human control. Accordingly, the current research sheds light on the AI acceptance of managers and gives insights into the role of the business area and AI functionality.

AI companies are on the rise. Many existing and new companies are looking to provide AI related service offerings. This dataset provides AI companies data from around the world.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
• Global Reach: Access over 28 million verified company profiles across 195 countries.
• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
• Accurate & Verified: AI-validated with a 99% accuracy rate, ensuring high-quality data.
• Real-Time Updates: Stay ahead with continuously updated company information.
• Ethically Sourced Data: Our B2B data is compliant with global privacy laws, ensuring responsible use.
• Dedicated Service: Receive personalized, curated data without the hassle of managing platforms.
• Tailored Solutions: Custom datasets are built to fit your unique business needs and industries.

Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new markets. With continuous data updates, Success.ai ensures you’re always working with the freshest information.

Key Use Cases:

• Targeted Lead Generation: Build accurate lead lists by filtering data by company size, industry, or location. Target decision-makers in key industries to streamline your B2B sales outreach.
• Account-Based Marketing (ABM): Use B2B company data to personalize marketing campaigns, focusing on high-value accounts and improving conversion rates.
• Investment Research: Track company growth, funding rounds, and employee trends to identify investment opportunities or potential M&A targets.
• Market Research: Enrich your market intelligence initiatives by gain...

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.

This dataset provides a comprehensive overview of the global AI industry's financial performance, software market trends, and usage statistics. It is designed to offer insights into various aspects of the AI market, enabling analysts, researchers, and business professionals to understand the current landscape and forecast future trends.

AI Dataset Search Platform Market Outlook

According to our latest research, the global AI Dataset Search Platform market size is valued at USD 1.18 billion in 2024, with a robust year-over-year expansion driven by the escalating demand for high-quality datasets to fuel artificial intelligence and machine learning initiatives across industries. The market is expected to grow at a CAGR of 22.6% from 2025 to 2033, reaching an estimated USD 9.62 billion by 2033. This exponential growth is primarily attributed to the increasing recognition of data as a strategic asset, the proliferation of AI applications across sectors, and the need for efficient, scalable, and secure platforms to discover, curate, and manage diverse datasets.

One of the primary growth factors propelling the AI Dataset Search Platform market is the exponential surge in AI adoption across both public and private sectors. Businesses and institutions are increasingly leveraging AI to gain competitive advantages, enhance operational efficiencies, and deliver personalized experiences. However, the effectiveness of AI models is fundamentally reliant on the quality and diversity of training datasets. As organizations strive to accelerate their AI initiatives, the need for platforms that can efficiently search, aggregate, and validate datasets from disparate sources has become paramount. This has led to a significant uptick in investments in AI dataset search platforms, as they enable faster data discovery, reduce development cycles, and ensure compliance with data governance standards.

Another key driver for the market is the growing complexity and volume of data generated from emerging technologies such as IoT, edge computing, and connected devices. The sheer scale and heterogeneity of data sources necessitate advanced search platforms equipped with intelligent indexing, semantic search, and metadata management capabilities. These platforms not only facilitate the identification of relevant datasets but also support data annotation, labeling, and preprocessing, which are critical for building robust AI models. Furthermore, the integration of AI-powered search algorithms within these platforms enhances the accuracy and relevance of search results, thereby improving the overall efficiency of data scientists and AI practitioners.

Additionally, regulatory pressures and the increasing emphasis on ethical AI have underscored the importance of transparent and auditable data sourcing. Organizations are compelled to demonstrate the provenance and integrity of the datasets used in their AI models to mitigate risks related to bias, privacy, and compliance. AI dataset search platforms address these challenges by providing traceability, version control, and access management features, ensuring that only authorized and compliant datasets are utilized. This not only reduces legal and reputational risks but also fosters trust among stakeholders, further accelerating market adoption.

From a regional perspective, North America dominates the AI Dataset Search Platform market in 2024, accounting for over 38% of the global revenue. This leadership is driven by the presence of major technology providers, a mature AI ecosystem, and substantial investments in research and development. Europe follows closely, benefiting from stringent data privacy regulations and strong government support for AI innovation. The Asia Pacific region is experiencing the fastest growth, propelled by rapid digital transformation, expanding AI research communities, and increasing government initiatives to foster AI adoption. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven solutions.

Component Analysis

The AI Dataset Search Platform market by component is segmented into platforms and services, each playing a pivotal role in the ecosystem. The platform segment encompasses the core software infrastructure that enables users to search, index, curate, and manage datasets. This segmen

The offered dataset includes details about several businesses, such as their name, industry, profits in USD, and the advantages associated with integrating AI technologies. The dataset is described as follows: The dataset includes multiple businesses from various industries, each of which is distinguished by its own special qualities and advantages provided by AI. This dataset shows the financial performance of many businesses across industries and emphasizes the advantages AI has brought about. The data can be used to examine the effects of AI adoption across various industries and the unique benefits enjoyed by businesses operating in each industry.

The global Artificial Intelligence (AI) Training Dataset market is projected to reach $1605.2 million by 2033, exhibiting a CAGR of 9.4% from 2025 to 2033. The surge in demand for AI training datasets is driven by the increasing adoption of AI and machine learning technologies in various industries such as healthcare, financial services, and manufacturing. Moreover, the growing need for reliable and high-quality data for training AI models is further fueling the market growth. Key market trends include the increasing adoption of cloud-based AI training datasets, the emergence of synthetic data generation, and the growing focus on data privacy and security. The market is segmented by type (image classification dataset, voice recognition dataset, natural language processing dataset, object detection dataset, and others) and application (smart campus, smart medical, autopilot, smart home, and others). North America is the largest regional market, followed by Europe and Asia Pacific. Key companies operating in the market include Appen, Speechocean, TELUS International, Summa Linguae Technologies, and Scale AI. Artificial Intelligence (AI) training datasets are critical for developing and deploying AI models. These datasets provide the data that AI models need to learn, and the quality of the data directly impacts the performance of the model. The AI training dataset market landscape is complex, with many different providers offering datasets for a variety of applications. The market is also rapidly evolving, as new technologies and techniques are developed for collecting, labeling, and managing AI training data.

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

• User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.
• Call Duration: Analyze engagement levels through call lengths.
• Geographical Information: Detailed data on city, state, and country for regional analysis.
• Call Timing: Track peak interaction times with precise timestamps.
• Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues.
• Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models

WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset comprises a diverse range of real-world customer service calls, featuring various accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as order inquiries, account management, or technical troubleshooting without needing human intervention.

Improving Multilingual and Cross-Regional Support: Given that the dataset includes geographical information (e.g., city, state, and country), AI agents can be trained to recognize region-specific slang, phrases, and cultural nuances, which is particularly valuable for multinational companies operating in diverse markets (e.g., the USA, UK, and Australia...

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*...

Analyzing the Forbes AI50 2024 dataset using AI tools and data analysis can be beneficial in several areas:

1. Market Trends: Identify emerging trends in the AI industry by analyzing funding patterns, growth metrics, and technological advancements.
2. Investment Opportunities: Evaluate which companies are attracting the most investment and why, helping investors make informed decisions.
3. Competitive Analysis: Compare different AI companies to understand their strengths, weaknesses, and market positioning.
4. Innovation Tracking: Monitor the latest innovations and applications of AI across various sectors such as healthcare, defense, and data analytics.
5. Business Strategy: Develop strategies for businesses to adopt AI technologies by understanding successful implementations and use cases.

These insights can help stakeholders make data-driven decisions and stay ahead in the rapidly evolving AI landscape.

🧠 About Dataset

Overview

The AIToolBuzz — 16,763 AI Tools Dataset is a comprehensive collection of publicly available information on artificial intelligence tools and platforms curated from AIToolBuzz.com.
It compiles detailed metadata about each tool, including name, description, category, founding year, technologies used, website, and operational status.

The dataset serves as a foundation for AI trend analysis, product discovery, market research, and NLP-based categorization projects.
It enables researchers, developers, and analysts to explore the evolution of AI tools, detect emerging sectors, and study keyword trends across industries.

Dataset Composition

• Total Entries: 16,763 AI tools
• Time Period: Data collected in October 2025
• Source: AIToolBuzz.com — a curated directory of AI products and services
• Format: CSV (comma-separated), UTF-8 encoded
• Columns: 13 descriptive fields covering both tool metadata and website status

Use Cases

• 🧩 Market & Trend Analysis — Examine growth and patterns in AI categories, technologies, and geographies.
• 🤖 NLP & ML Projects — Use keywords and descriptions for text clustering or embedding tasks.
• 🏷️ Tool Discovery & Classification — Build AI tool recommenders or taxonomies.
• 📊 Data Visualization — Create dashboards showing trends over time or by region.

Example Entries

Provenance Summary

• Source: AIToolBuzz.com — public web directory.
• Collection Method: Automated web scraping via requests + BeautifulSoup, extracting metadata from each tool’s public page.
• Date Collected: October 2025.
• License: Derived dataset — redistribution permitted with attribution (CC BY 4.0 recommended).
• Collector: Swathik Devadiga.
• Frequency: Planned quarterly updates.

Citation

If you use this dataset, please cite as: AIToolBuzz — 16,763 AI Tools (Complete Directory with Metadata). Kaggle. https://aitoolbuzz.com

License

License: CC BY 4.0 — Creative Commons Attribution 4.0 International

You are free to share and adapt the data for research or analysis with proper attribution to AIToolBuzz.com as the original source.

📌About this dataset

Artificial Intelligence (AI) is rapidly transforming industries, research, and everyday life. This dataset provides a curated overview of 40 major AI tools developed between 2020 and 2024, including well-known names such as ChatGPT, Claude, Gemini, Copilot, Midjourney, and Stable Diffusion.

🔍What’s inside?

Each entry includes:

AI Name – The tool’s official name

Developer/Company – Who created it

Release Year – When it was launched

AI Type – Chatbot, Image Generator, Coding Assistant, LLM, etc.

Main Use Case – Primary application (text generation, art, research, etc.)

Popularity Score (1–10) – A relative score of how popular the tool is

Estimated Valuation (Billion USD) – Market impact and financial estimate

🎯Why this dataset?

This dataset is valuable for:

Trend analysis – How AI tools have evolved in recent years

Market research – Comparing valuations and popularity

Academic studies – AI adoption across different domains

Projects and visualizations – Creating charts and dashboards about AI tools

📈Possible analyses

Which companies dominate AI innovation?

How do AI valuations correlate with popularity scores?

Which AI tool types (chatbots, image generators, coding assistants) are rising fastest?

Timeline analysis of AI releases between 2020 and 2024

Success.ai’s Private Company Data Solutions offer businesses access to over 28 million verified company profiles, delivering detailed insights into private company data across multiple industries. Our solution includes firmographic data and business location data for companies of all sizes, from large enterprises to small businesses. Whether you're seeking small business contact data or company funding data, Success.ai’s company data solutions empower businesses with the accuracy and depth they need to drive B2B sales, marketing, and research initiatives.

At Success.ai, we offer tailored B2B datasets to meet specific business requirements. With our white-glove service, you’ll receive curated datasets customized to fit your needs, without the hassle of managing data platforms yourself. Our solution is GDPR-compliant, AI-validated with a 99% accuracy rate, and offers the best price guarantee on the market.

Why choose Success.ai?

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• Real-Time Updates: Data is continuously updated to ensure you’re working with the freshest insights.
• Ethically Sourced & Compliant: All data is GDPR-compliant and ethically sourced from trusted partners.
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Comprehensive data points:

Company Name LinkedIn URL Company Domain Company Description Business Location: Full details down to the city, state, and country Company Industry Employee Count Technologies Used Funding Information: Total funding and the latest funding dates

Maximize your sales potential by targeting decision-makers and building targeted account lists using Success.ai’s B2B contact data and company profiles. Our datasets are ideal for account-based marketing (ABM), investment research, market analysis, and CRM enrichment. Success.ai’s company data provides sales and marketing teams with the actionable insights they need to scale their efforts efficiently.

Key Use Cases:

• Targeted Lead Generation: Build precise lead lists by filtering company data by industry, size, or location.
• Account-Based Marketing (ABM): Use detailed firmographic data to focus marketing efforts on high-value accounts.
• Investment Research: Analyze company growth trends and funding history to identify high-potential investments.
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With Success.ai, you’ll benefit from our best price guarantee, industry-leading accuracy, and white-glove service. We specialize in private company data, small business contact data, and business location data, providing comprehensive solutions for B2B marketing, sales, and research teams. Whether you need firmographic data or insights on company funding, our real-time datasets will help you stay ahead of the competition.

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📹Project Video available on YouTube - https://youtu.be/WmJYHz_qn5s

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

Realistic Synthetic - AI Financial & Market Data for Gemini(Google), ChatGPT(OpenAI), Llama(Meta)

This dataset provides a synthetic, daily record of financial market activities related to companies involved in Artificial Intelligence (AI). There are key financial metrics and events that could influence a company's stock performance like launch of Llama by Meta, launch of GPT by OpenAI, launch of Gemini by Google etc. Here, we have the data about how much amount the companies are spending on R & D of their AI's Products & Services, and how much revenue these companies are generating. The data is from January 1, 2015, to December 31, 2024, and includes information for various companies : OpenAI, Google and Meta.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Finance or Share Market domain.

From this dataset, we extract various insights using Python in our Project.

1) How much amount the companies spent on R & D ?

2) Revenue Earned by the companies

3) Date-wise Impact on the Stock

4) Events when Maximum Stock Impact was observed

5) AI Revenue Growth of the companies

6) Correlation between the columns

7) Expenditure vs Revenue year-by-year

8) Event Impact Analysis

9) Change in the index wrt Year & Company

These are the main Features/Columns available in the dataset :

1) Date: This column indicates the specific calendar day for which the financial and AI-related data is recorded. It allows for time-series analysis of the trends and impacts.

2) Company: This column specifies the name of the company to which the data in that particular row belongs. Examples include "OpenAI" and "Meta".

3) R&D_Spending_USD_Mn: This column represents the Research and Development (R&D) spending of the company, measured in Millions of USD. It serves as an indicator of a company's investment in innovation and future growth, particularly in the AI sector.

4) AI_Revenue_USD_Mn: This column denotes the revenue generated specifically from AI-related products or services, also measured in Millions of USD. This metric highlights the direct financial success derived from AI initiatives.

5) AI_Revenue_Growth_%: This column shows the percentage growth of AI-related revenue for the company on a daily basis. It indicates the pace at which a company's AI business is expanding or contracting.

6) Event: This column captures any significant events or announcements made by the company that could potentially influence its financial performance or market perception. Examples include "Cloud AI launch," "AI partnership deal," "AI ethics policy update," and "AI speech recognition release." These events are crucial for understanding sudden shifts in stock impact.

7) Stock_Impact_%: This column quantifies the percentage change in the company's stock price on a given day, likely in response to the recorded financial metrics or events. It serves as a direct measure of market reaction.

Between 2023 and 2027, the majority of companies surveyed worldwide expect big data to have a more positive than negative impact on the global job market and employment, with ** percent of the companies reporting the technology will create jobs and * percent expecting the technology to displace jobs. Meanwhile, artificial intelligence (AI) is expected to result in more significant labor market disruptions, with ** percent of organizations expecting the technology to displace jobs and ** percent expecting AI to create jobs.