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.

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.

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.

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...

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.

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.

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 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.

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.

Artificial intelligence (AI) is heavily used for service operations and strategy and corporate finance, with nearly all industries reporting around ** percent usage of AI in these functions. The greatest use of AI in product making was in the risk industry, with over ** percent of respondents using AI in 2022. The use of AI in manufacturing and marketing is low, as these can require individual human instincts and so lend themselves less easily to AI applications. AI adoption isn’t easy It is no easy task to adapt a new technology of such widespread use as AI. There are numerous pitfalls and problems, both from the use of the technology itself but also from actions by outside agents causing issues. Companies considered cybersecurity to be chief among the risks being mitigated when adapting AI in 2023. In addition, regulatory compliance was a considerable challenge, stemming from a strong need to respect information privacy among users. Employment faces steep headwinds AI will have a considerable effect on the labor needs of nations worldwide. Of the many professions, office and administrative support are facing the greatest risk of automation. These are linear and formulaic positions, with many of their duties delegable to advanced programs.

Details data on AI adoption indecses provided by:

• Oxford Insights Government AI Readiness Index (https://oxfordinsights.com/ai-readiness/ai-readiness-index/) - 2023
• Cisco AI Readiness Index (https://oxfordinsights.com/ai-readiness/ai-readiness-index/) - 2023
• Kearney Report: Racing toward the future: artificial intelligence in Southeast Asia (https://www.kearney.com/service/digital-analytics/article/-/insights/racing-toward-the-future-artificial-intelligence-in-southeast-asia) - 2020

Estimated AI adoption rates across finance, professional services, retail, healthcare, and manufacturing.

 Introduction

Agentic AI Statistics: Agentic AI is a segment of artificial intelligence designed to reach specific goals with little human supervision. It works through AI agents, which are machine learning models that act like human decision-makers to handle problems instantly. Agentic AI builds on generative AI techniques and relies on large language models (LLMs) to work well in dynamic situations.

As interest in this area increases, innovation is speeding up, with major tech firms, startups, and research groups putting strong efforts and resources into its progress. Statistics highlighting adoption trends, funding flows, market growth, and real-world applications that provide valuable insights into how agentic AI is evolving and influencing industries worldwide.

The AI Adoption Survey was designed to empirically validate the constructs derived from the GCC National AI Strategies and the literature review. It targeted mid- to senior-level government employees across the six GCC member states who are directly involved in digital transformation, AI strategy, or IT/innovation functions. Survey Design and Structure The questionnaire included 12 core items (Q1–Q12). Q1 captured demographic and categorical information (country, role, sector). Q2–Q4 measured Technical Infrastructure (TI), focusing on the availability of ICT, data, and cloud resources to support AI. Q5–Q7 measured Organizational Readiness (OR), including workforce readiness, leadership support, and institutional processes for AI integration. Q8–Q9 measured Governance Environment (GE), assessing regulatory clarity, ethical oversight, and legal frameworks surrounding AI. Q10–Q12 measured AI Outcomes (AIO), including perceived impact on service delivery, efficiency, and citizen satisfaction. All items were measured using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Sampling and Data Collection The survey was distributed online between September 2024 and February 2025. Invitations were sent to approximately 400 eligible participants, with 203 valid responses recorded, representing all six GCC countries. Respondents were purposively sampled to ensure coverage of ministries, agencies, and professional associations engaged in AI deployment. Response Characteristics The final dataset provides a balanced representation across GCC states, with respondent distribution ranging from 15–17% per country. All respondents held positions in AI, IT, or digital transformation roles, ensuring relevance to the constructs under study. A non-response bias test (early vs. late respondents) indicated no statistically significant differences, supporting the validity of the sample. Link to Model Development Responses to Q2–Q9 were used to construct the latent variables Technical Infrastructure, Organizational Readines, and Governance Environment, while Q10–Q12 were modeled as AI Outcomes in the PLS-SEM. These survey data provided the empirical basis for validating the GCC-specific AI Adoption Index proposed in this study.

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.

The Artificial Intelligence (AI) market is experiencing explosive growth, projected to reach a substantial size driven by increasing adoption across diverse sectors. The 31.22% CAGR from 2019 to 2024 indicates a rapid expansion, fueled by several key factors. Technological advancements, particularly in deep learning and natural language processing, are enabling the development of more sophisticated and effective AI solutions. The rising availability of big data, coupled with enhanced computing power, further accelerates this growth. Businesses are increasingly leveraging AI for automation, predictive analytics, and improved decision-making, driving demand across industries such as healthcare, finance, and manufacturing. The cloud computing infrastructure plays a pivotal role, enabling scalable and cost-effective deployment of AI solutions. While data privacy and security concerns pose potential restraints, the overall market trajectory remains strongly positive, with significant opportunities for innovation and investment. Leading players like IBM, Intel, Microsoft, Google, Amazon, Oracle, Salesforce, SAP, and others are actively shaping the AI landscape through continuous research and development, strategic partnerships, and acquisitions. The market segmentation likely includes categories based on technology (e.g., machine learning, deep learning, computer vision), application (e.g., robotics, healthcare, finance), and deployment model (e.g., cloud, on-premise). Regional variations in adoption rates are expected, with North America and Europe likely holding significant market share initially, followed by a gradual expansion into Asia-Pacific and other regions as technology matures and affordability increases. Future growth hinges on addressing ethical considerations, ensuring responsible AI development, and fostering collaboration across academia, industry, and governments. The continued convergence of AI with other technologies, like IoT and blockchain, will further unlock new possibilities and market expansion. Recent developments include: May 2024 - IBM and Salesforce have unveiled an enhanced strategic partnership with a primary goal of advancing the utilization of artificial intelligence (AI) and data integration. This is to be achieved through the synergies of IBM's Watsonx AI and Data Platform and Salesforce's Einstein 1 Platform. The collaboration is designed to provide customers with increased flexibility in deploying AI and data solutions, empowering teams to integrate data-driven decisions into their workflows seamlessly., April 2024 - Microsoft Corp. and The Coca-Cola Company have announced a strategic partnership spanning five years. The primary goal of this collaboration is to align Coca-Cola's technology strategy throughout its operations, integrate cutting-edge technologies, and foster innovation and efficiency on a worldwide level. Notably, Coca-Cola has committed USD 1.1 billion to harness Microsoft Cloud's advanced AI features. This move underscores Coca-Cola's dedication to a technology-driven strategy, with Microsoft Cloud positioned as its central global hub for cloud services and AI.. Key drivers for this market are: Increasing Demand for Predictive Analytics Solutions, Massive Growth in Data Generation due to Technological Advancements; Growth in Adoption of Cloud-based Applications and Services; Rising Demand for Enhanced Consumer Experience. Potential restraints include: Increasing Demand for Predictive Analytics Solutions, Massive Growth in Data Generation due to Technological Advancements; Growth in Adoption of Cloud-based Applications and Services; Rising Demand for Enhanced Consumer Experience. Notable trends are: Growth in Adoption of Cloud-based Applications and Services is Expected to Drives the Market Growth.

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.

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.

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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...

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.