In 2024, artificial intelligence adoption has experienced a remarkable surge across global organizations. The percentage of companies integrating AI into at least one business function has dramatically increased to ** percent, representing a substantial leap from ** percent in the previous year. Even more striking is the exponential growth of generative AI, which has been embraced by ** percent of organizations worldwide. This represents an impressive increase of over ** percentage points, highlighting the technology's swift transition from an emerging trend to a mainstream business tool.

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.

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.

A broad dataset providing insights into artificial intelligence statistics and trends for 2025, covering market growth, adoption rates across industries, impacts on employment, AI applications in healthcare, education, and more.

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.

This dataset captures insights into the use of Artificial Intelligence (AI) among 535 students in Indonesian higher education, focusing on their expectations, challenges, attitudes, perceptions, and motivations regarding AI-based learning tools. Collected through a structured survey, the dataset includes demographic variables such as university type, field of study, and educational level, along with students' self-reported experiences with AI in academic settings. The dataset serves as a valuable resource for understanding AI adoption trends in higher education, identifying barriers to AI integration, and evaluating its impact on student engagement and learning outcomes. It enables comparative analysis across different academic disciplines and institutional contexts, offering opportunities for policymakers and educators to design AI-informed curricula. Additionally, this dataset is structured for reproducibility and reuse, allowing researchers to extend its findings, apply alternative analytical approaches, and conduct cross-regional or longitudinal studies on AI integration in higher education.

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.

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.

Introduction

Business Innovation Statistics: In the year 2024, business innovation is seen as an important force for growth, competitiveness, and sustainability. Therefore, agents across the world heavily invest in R&D, digital transformation, and new technologies so that they might keep up with fast-evolving markets.

This article is therefore a thorough analytical study of the business innovations statistics in 2024, showing some of the main trends, investment patterns, and how innovation affects the overall performance of organisations.

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.

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

These files are supplementary files for the “Toward Responsible AI adoption in the Public Sector: A Theory-Informed Taxonomy of Data-AI Challenges” study being under review (reference will be added upon acceptance).

File "Data_AI_challenges.docx" presents the list of data-AI challenges surrounding AI adoption in public sector as identified through the SLR.

According to a global 2023 survey, ** percent of company CEOs were planning to explore options for the adoption of artificial intelligence (AI) in the future, as opposed to ** percent of CMOs stating the same. An equal amount of CEOs and CMOs - ** percent - were planning to adopt AI immediately but only across some business units/operations.

This study examines AI adoption in US hospitals using three distinct datasets: (i) Survey data from the American Hospital Association on AI for operations-related uses (27% adopt), (ii) Employment data from Revelio Labs on workers at hospitals with AI skills (14% adopt), and (iii) Publication data from Dimensions on hospital-affiliated researcher publications (8% adopt). Consistent with adoption patterns for the business internet and for electronic medical records, AI adoption is higher in metro areas and in larger hospitals. In contrast to the business internet, metro area and firm size do not appear to be substitute correlates with adoption.

AI Data Analysis Tool Market Outlook

The global AI Data Analysis Tool market size was valued at approximately USD 15.3 billion in 2023 and is projected to reach USD 57.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.5% during the forecast period. The rapid growth factor of this market can be attributed to the increasing adoption of artificial intelligence and machine learning technologies across various industries to enhance data processing and analytics capabilities, driving the demand for advanced AI-powered data analysis tools.

One of the primary growth factors in the AI Data Analysis Tool market is the exponential increase in the volume of data generated by digital devices, social media, online transactions, and IoT sensors. This data deluge has created an urgent need for robust tools that can analyze and extract actionable insights from large datasets. AI data analysis tools, leveraging machine learning algorithms and deep learning techniques, facilitate real-time data processing, trend analysis, pattern recognition, and predictive analytics, making them indispensable for modern businesses looking to stay competitive in the data-driven era.

Another significant growth driver is the expanding application of AI data analysis tools in various industries such as healthcare, finance, retail, and manufacturing. In healthcare, for instance, these tools are utilized to analyze patient data for improved diagnostics, treatment plans, and personalized medicine. In finance, AI data analysis is employed for risk assessment, fraud detection, and investment strategies. Retailers use these tools to understand consumer behavior, optimize inventory management, and enhance customer experiences. In manufacturing, AI-driven data analysis enhances predictive maintenance, process optimization, and quality control, leading to increased efficiency and cost savings.

The surge in cloud computing adoption is also contributing to the growth of the AI Data Analysis Tool market. Cloud-based AI data analysis tools offer scalability, flexibility, and cost-effectiveness, allowing businesses to access powerful analytics capabilities without the need for substantial upfront investments in hardware and infrastructure. This shift towards cloud deployment is particularly beneficial for small and medium enterprises (SMEs) that aim to leverage advanced analytics without bearing the high costs associated with on-premises solutions. Additionally, the integration of AI data analysis tools with other cloud services, such as storage and data warehousing, further enhances their utility and appeal.

AI and Analytics Systems are becoming increasingly integral to the modern business landscape, offering unparalleled capabilities in data processing and insight generation. These systems leverage the power of artificial intelligence to analyze vast datasets, uncovering patterns and trends that were previously inaccessible. By integrating AI and Analytics Systems, companies can enhance their decision-making processes, improve operational efficiency, and gain a competitive edge in their respective industries. The ability to process and analyze data in real-time allows businesses to respond swiftly to market changes and customer demands, driving innovation and growth. As these systems continue to evolve, they are expected to play a crucial role in shaping the future of data-driven enterprises.

Regionally, North America holds a prominent share in the AI Data Analysis Tool market due to the early adoption of advanced technologies, presence of major tech companies, and significant investments in AI research and development. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. This growth can be attributed to the rapid digital transformation across emerging economies, increasing government initiatives to promote AI adoption, and the rising number of tech startups focusing on AI and data analytics. The growing awareness of the benefits of AI-driven data analysis among businesses in this region is also a key factor propelling market growth.

Component Analysis

The component segment of the AI Data Analysis Tool market is categorized into software, hardware, and services. Software is the largest segment, holding the majority share due to the extensive adoption of AI-driven analytics platforms and applications across various industries. These software solutions include machine learning algorithms, data visualization too

This data is collected for a research purpose

Paper title this data set accompanies (submitted for peer review) Cognitive frames that drive AI adoption Paper Abstract We investigate the role of cognitive frames in the adoption of artificial intelligence in the workplace. We propose a theoretical model of a three-level hierarchy of AI adoption that distinguishes between acceptance, collaboration, and co-creation. Each level represents higher levels of sophistication in human involvement in working with AI, ranging from passive utilitarian acceptance to active collaboration and co-creation. Using reasoning from technological frames of reference theory, we tested a structural model connecting individual cognitive frames to AI adoption. We developed a structural equation model that analyzed data collected from a sample of 305 professionals in Europe who have a high usage of technology in the workplace. Our results show the model exhibits high goodness of fit and predictive relevance. The work contributes to a greater understanding of the role of cognitive frames in driving the behavioural intention of adopting increasingly sophisticated AI technologies. Grant Information This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101023024 for Augmented-Humans.

Generative Artificial Intelligence (Gen AI) Market Outlook

The global Generative AI (Gen AI) market is valued at USD 38.06 billion in 2024 and is expanding at a compound annual growth rate (CAGR) of around 35%, reaching an estimated value of $200 billion by 2032.



Key segments contributing to this growth include software, which accounts for approximately 60% of the market share, and the healthcare and finance applications, which are forecasted to see the highest adoption rates. The cloud deployment mode will dominate with over 70% of the market share, reflecting the ongoing trend towards cloud-based solutions. Large enterprises will continue to lead in terms of enterprise size, while the Asia Pacific region is anticipated to exhibit the fastest growth, fuelled by rapid technological advancements and increasing investments in AI infrastructure.

The Generative AI market is set to experience significant growth driven by the continuous advancements in machine learning and deep learning technologies. As these AI models become more capable and efficient, they are being integrated into a broader array of business processes, enhancing productivity and innovation. The growing digital transformation across industries also propels the demand for AI capabilities, particularly in areas like customer experience management, predictive maintenance, and supply chain optimization. Additionally, the reduction in costs associated with AI technologies, due to improvements in cloud computing infrastructures and the democratization of AI tools, makes these technologies accessible to a wider range of businesses, including small and medium-sized enterprises. The global push towards more data-driven decision-making further amplifies the adoption and investment in Generative AI, underpinning its market growth.

Scope of the Generative Artificial Intelligence (Gen AI) Market Report

The market report includes an assessment of the market trends, segments, and regional markets. Overview and dynamics are included in the report.

Generative Ai Media Software is playing a pivotal role in transforming the media landscape by enabling the creation of highly realistic and engaging content. This software leverages advanced algorithms to generate images, videos, and even music, offering new possibilities for content creators and media companies. By automating parts of the creative process, Generative Ai Media Software allows for more efficient production workflows and the ability to personalize content at scale. This has led to a surge in innovative applications, such as virtual influencers and AI-generated characters, which are reshaping how audiences intera

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

(Source: about:blank)

Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

OverviewThis dataset contains anonymised responses (N = 44) from a November 2024 pilot survey that explored artificial-intelligence (AI) adoption among graduate students and young professionals (“young leaders”) in Uzbekistan and neighbouring Central-Asian countries.PurposeThe survey tested constructs later used in the main study on behavioural intention to adopt AI-enabled data-analytics tools for managerial decision making. Key variables include AI familiarity, usage frequency, comfort level, perceived efficiency gains, and concerns about transparency or bias.MethodData were collected online via Google Forms; participation was voluntary and fully anonymous. The CSV file contains Likert-scale and categorical responses, with a separate README describing each variable and coding scheme.Potential reuseResearchers can replicate or extend technology-acceptance models in emerging-economy contexts, compare student versus professional cohorts, or conduct secondary analyses on AI self-efficacy and algorithmic trust.File list• ai_pilot_radman2024.csv – raw responses• README.md – variable descriptions, survey instrument, codebookLicensed under CC BY 4.0.