In a 2025 survey, ** percent of respondents back a model where humans hold ************** of decision authority and AI contributes only by *********, whereas only *** percent support fully autonomous AI decisions.

Nearly all forms of decision-making can be enhanced considerably with the involvement of generative AI in the process. The greatest improvement in automation would be in management, where the automation potential grows by **** percent.

According to a survey that was conducted from February to March 2024 among managers of Prime-listed companies in Japan, *** percent of the respondents stated that the decision-making speed at their company increased greatly after the implementation of generative artificial intelligence (AI). More than ** percent of the respondents stated that the decision-making speed did not change, but was expected to improve in the future.

The AI Decision Making market is rapidly transforming the way industries operate, enabling businesses to leverage data-driven insights for enhanced decision-making capabilities. As organizations continue to recognize the importance of incorporating artificial intelligence into their operations, the demand for AI-dri

The uploaded dataset was used for the statistical analysis of the pre-print "Artificial Intelligence for EU Decision-Making: Effects on Citizens’ Perceptions of Input, Throughput and Output Legitimacy" (Permanent identifier: arXiv:2003.11320)

A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (1) independent human decision-making (HDM); (2) independent algorithmic decision-making (ADM) by AI-based systems; and (3) hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most democratic (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making.

The Decision-Making Intelligent Service market is experiencing robust growth, driven by the increasing need for data-driven insights across diverse sectors. The market's expansion is fueled by several factors, including the rising adoption of cloud-based solutions, the proliferation of big data, and the growing demand for improved operational efficiency and strategic decision-making. Businesses are increasingly relying on these services to gain a competitive edge by analyzing vast datasets to identify trends, predict future outcomes, and optimize resource allocation. The enterprise segment is a major contributor to market growth, with large organizations investing heavily in advanced analytics platforms to streamline complex processes and make informed business decisions. While on-premises solutions still hold a significant share, the shift towards cloud-based deployments is accelerating due to scalability, cost-effectiveness, and ease of access. Technological advancements, such as the development of more sophisticated AI algorithms and machine learning models, are further enhancing the capabilities of these services, leading to more accurate predictions and improved decision-making outcomes. Competition in the market is intense, with established players like IBM, SAS, Oracle, and Microsoft vying for market share alongside emerging companies specializing in niche applications. Despite the significant growth potential, certain restraints are influencing the market's trajectory. High implementation costs, especially for complex enterprise solutions, and the need for specialized skills to effectively utilize these services can act as barriers to entry for some organizations. Data security and privacy concerns also pose challenges, as organizations must ensure the confidentiality and integrity of sensitive data used for decision-making. However, ongoing advancements in data security technologies and the development of user-friendly interfaces are mitigating these challenges, making these services increasingly accessible to a wider range of users. The market is expected to witness continued expansion over the forecast period (2025-2033), with a projected CAGR significantly exceeding the average technology sector growth. Geographic expansion, particularly in rapidly developing economies, will contribute to this expansion, as organizations in these regions adopt advanced analytics to optimize their operations and improve their competitiveness. North America and Europe are currently leading the market, but Asia-Pacific is expected to experience rapid growth over the next decade.

The Intelligent Decision Making (IDM) solutions market, valued at $1125 million in 2025, is poised for robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 6.7% from 2025 to 2033. This growth is fueled by several key drivers. The increasing volume and velocity of data generated across various industries necessitate advanced analytical tools for timely and effective decision-making. Businesses are increasingly adopting cloud-based IDM solutions for enhanced scalability, accessibility, and cost-effectiveness. Furthermore, the rising adoption of artificial intelligence (AI) and machine learning (ML) within these solutions is automating insights generation and predictive analytics, leading to improved operational efficiency and strategic planning. Competitive pressures are also driving adoption, as organizations strive to gain a competitive edge through data-driven decision-making. Key players like IBM, Microsoft, Salesforce, Oracle, QlikTech, Palantir, and TIBCO Software are actively shaping the market through continuous innovation and strategic partnerships. The market's segmentation, while not explicitly detailed, likely includes solutions categorized by deployment (cloud, on-premise, hybrid), industry vertical (finance, healthcare, retail, etc.), and functionality (predictive analytics, business intelligence, data visualization). Growth will likely be uneven across these segments, with cloud-based solutions and AI-driven analytics experiencing particularly strong demand. While potential restraints such as high implementation costs and the need for skilled professionals to manage these complex systems exist, the overall market outlook remains positive, driven by the undeniable value proposition of data-driven decision-making in today's competitive landscape. The forecast period (2025-2033) anticipates sustained growth, with significant market expansion predicted across various geographic regions, reflecting a global trend towards data-centric business strategies.

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

Artificial Intelligence in Big Data Analysis Market Outlook

The global market size for artificial intelligence in big data analysis was valued at approximately $45 billion in 2023 and is projected to reach around $210 billion by 2032, growing at a remarkable CAGR of 18.7% during the forecast period. This phenomenal growth is driven by the increasing adoption of AI technologies across various sectors to analyze vast datasets, derive actionable insights, and make data-driven decisions.

The first significant growth factor for this market is the exponential increase in data generation from various sources such as social media, IoT devices, and business transactions. Organizations are increasingly leveraging AI technologies to sift through these massive datasets, identify patterns, and make informed decisions. The integration of AI with big data analytics provides enhanced predictive capabilities, enabling businesses to foresee market trends and consumer behaviors, thereby gaining a competitive edge.

Another critical factor contributing to the growth of AI in the big data analysis market is the rising demand for personalized customer experiences. Companies, especially in the retail and e-commerce sectors, are utilizing AI algorithms to analyze consumer data and deliver personalized recommendations, targeted advertising, and improved customer service. This not only enhances customer satisfaction but also boosts sales and customer retention rates.

Additionally, advancements in AI technologies, such as machine learning, natural language processing, and computer vision, are further propelling market growth. These technologies enable more sophisticated data analysis, allowing organizations to automate complex processes, improve operational efficiency, and reduce costs. The combination of AI and big data analytics is proving to be a powerful tool for gaining deeper insights and driving innovation across various industries.

From a regional perspective, North America holds a significant share of the AI in big data analysis market, owing to the presence of major technology companies and high adoption rates of advanced technologies. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in AI and big data technologies, and the growing need for data-driven decision-making processes.

Component Analysis

The AI in big data analysis market is segmented by components into software, hardware, and services. The software segment encompasses AI platforms and analytics tools that facilitate data analysis and decision-making. The hardware segment includes the computational infrastructure required to process large volumes of data, such as servers, GPUs, and storage devices. The services segment involves consulting, integration, and support services that assist organizations in implementing and optimizing AI and big data solutions.

The software segment is anticipated to hold the largest share of the market, driven by the continuous development of advanced AI algorithms and analytics tools. These solutions enable organizations to process and analyze large datasets efficiently, providing valuable insights that drive strategic decisions. The demand for AI-powered analytics software is particularly high in sectors such as finance, healthcare, and retail, where data plays a critical role in operations.

On the hardware front, the increasing need for high-performance computing to handle complex data analysis tasks is boosting the demand for powerful servers and GPUs. Companies are investing in robust hardware infrastructure to support AI and big data applications, ensuring seamless data processing and analysis. The rise of edge computing is also contributing to the growth of the hardware segment, as organizations seek to process data closer to the source.

The services segment is expected to grow at a significant rate, driven by the need for expertise in implementing and managing AI and big data solutions. Consulting services help organizations develop effective strategies for leveraging AI and big data, while integration services ensure seamless deployment of these technologies. Support services provide ongoing maintenance and optimization, ensuring that AI and big data solutions deliver maximum value.

Overall, the combination of software, hardware, and services forms a comprehensive ecosystem that supports the deployment and utilization of AI in big data analys

Introduction

AI In Business Statistics: In today’s world, global businesses are rapidly transforming with Artificial Intelligence (AI), and as a result, overall operations are becoming easier in almost every industry. From automating regular tasks to enabling smarter decision-making, AI is also helping companies boost efficiency, increase productivity, reduce costs, enhance software development, improve customer experiences, and increase overall output.

This article includes several statistical analyses from different sources that elaborate on the overall AI-enhancing business sectors, along with their advantages and disadvantages.

The global Decision Making AI market size was valued at USD 27440 million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The increasing adoption of AI technologies across various industries, the rising demand for data-driven decision-making, and the growing awareness of the benefits of AI in optimizing operations are key factors driving the market growth. The adoption of Decision Making AI is primarily driven by its ability to automate complex decision-making processes, improve accuracy and efficiency, and provide real-time insights. The increasing availability of data and advancements in machine learning algorithms have further accelerated the adoption of these solutions. Key market players include Baidu, Alibaba Group, Huawei, Tencent, 4paradigm, SenseTime, Google, IBM, Oracle, Microsoft, Clarifai, Paretos, Metaphacts, and Diwo.ai. The market is segmented by type (platform-centric, non-platform centric) and application (decision support, decision augmentation, decision automation). North America and Asia Pacific are the major regions contributing to the market growth. With a valuation of over $500 million, decision-making AI is revolutionizing how enterprises leverage data to make informed decisions.

This repository contains all necessary data and code for IUI 2025 paper "Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant". It is also available on Github: https://github.com/delftcrowd/IUI2025_ConvXAI. In our study, we provided different XAI interfaces (XAI dashboard and conversational XAI interfaces with different setup). We collected their collaborative decision making data with AI systems. These data can be useful for people who are interested in human-AI decision making.

Introduction

Artificial Intelligence Statistics: Artificial intelligence (AI) has rapidly become a disruptive force, revolutionizing industries and transforming the way businesses operate and interact with their customers. By analyzing massive amounts of data and performing tasks typically managed by humans, AI is driving advancements in sectors like healthcare, finance, automotive, and retail.

Key technologies, including machine learning, natural language processing, and robotics, are driving this evolution, leading to more streamlined operations, improved decision-making, and enhanced personalisation.

As AI continues to advance, its capacity to address complex global issues, streamline processes, and open new business opportunities is becoming increasingly apparent, solidifying its role as a cornerstone of future technological development.

This statistic shows the acceptance of using artificial intelligence as a basis for decision making among individuals in Sweden in 2018, by actor. According to the survey, ** percent would be comfortable with media services such as Spotify or Netflix using artificial intelligence for making decisions about them, e.g. in order to give personalized recommendations.

This dataset contains user behavior data collected during an empirical study investigating how human decision-makers interact with AI systems when dealing with complex and uncertain tasks. The study involved a trip-planning task where participants were asked to find the optimal trip plan for a given scenario, taking into account factors such as budget, travel duration, and mode of transportation.

The intelligent decision-making (IDM) market is experiencing robust growth, projected to reach $118 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 15.4% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing availability and affordability of advanced analytical technologies, including artificial intelligence (AI) and machine learning (ML), empower organizations across various sectors to process vast datasets and extract actionable insights for better decision-making. Secondly, the growing demand for real-time data analytics and predictive modeling is fueling the adoption of IDM solutions, particularly in time-sensitive industries like finance and logistics. Furthermore, the increasing pressure on businesses to optimize operations, reduce costs, and enhance customer experiences is driving investment in IDM technologies. The Finance, Retail, and Government sectors are currently leading the adoption, but significant growth is anticipated in the Manufacturing and National Defense industries as they increasingly leverage data-driven strategies for enhanced efficiency and security. Key players like FICO, SAS Institute Inc., and emerging technology companies are shaping the market landscape through innovation in both functional modules and comprehensive solutions. The market segmentation reveals a diverse landscape with significant opportunities across applications and types. While functional modules focusing on specific decision-making processes are prevalent, comprehensive solutions integrating multiple functionalities are gaining traction due to their ability to provide holistic insights. Regionally, North America and Europe are currently dominant due to early adoption and technological advancements. However, rapid digitalization in Asia-Pacific, particularly in China and India, promises substantial future growth. The presence of robust technological infrastructure and a large pool of skilled professionals are contributing factors. While data privacy and security concerns represent a potential restraint, the industry is actively developing solutions that address these challenges while maximizing the benefits of data-driven decision-making. The forecast period of 2025-2033 is expected to witness significant innovation and expansion, with the market poised for considerable growth across various sectors and regions.

Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes. Determining what information AI should provide to aid humans is vital, a concept underscored by explainable AI's efforts to justify AI predictions. However, how the information is presented, e.g., the sequence of recommendations and solicitation of interpretations, is equally crucial as complex interactions may emerge between humans and AI. While empirical studies have evaluated human-AI dynamics across domains, a common vocabulary for human-AI interaction protocols is lacking. To promote more deliberate consideration of interaction designs, we introduce a taxonomy of interaction patterns that delineate various modes of human-AI interactivity. We summarize the results of a systematic review of AI-assisted decision making literature and identify trends and opportunities in existing interactions across application domains from 105 articles. We find that current interactions are dominated by simplistic collaboration paradigms, leading to little support for truly interactive functionality. Our taxonomy offers a tool to understand interactivity with AI in decision-making and foster interaction designs for achieving clear communication, trustworthiness, and collaboration.

in this article, a number оf issues affecting the structure оf decisiоn-making based оn artificial intelligence algоrithms in secоndary schооls are cоnsidered and definitiоns оf the main cоncepts оf the tоpic are given. At the same time, we can see the main stages оf decisiоn-making based оn artificial intelligence algоrithms, teachers and students оf secоndary schооls make decisiоns.

Artificial Intelligence (AI) in Sports Analytics Market Outlook

According to our latest research, the global Artificial Intelligence (AI) in Sports Analytics market size reached USD 2.8 billion in 2024. The market is expected to grow at a robust CAGR of 28.6% during the forecast period, reaching approximately USD 25.3 billion by 2033. This remarkable growth is being fueled by the increasing adoption of AI-driven solutions for data-driven decision-making, enhanced player performance analysis, and the rising demand for personalized fan experiences across sports organizations worldwide.

One of the primary growth factors for the AI in Sports Analytics market is the exponential increase in data generated from various sporting activities, including player statistics, match footage, and biometric data. The ability of AI algorithms to process and analyze large volumes of diverse data in real time is revolutionizing how teams and coaches approach training, strategy formulation, and in-game decisions. Advanced machine learning models are enabling sports organizations to extract actionable insights that were previously unattainable, leading to improved player performance, reduced injury risks, and optimized team management. As sports become increasingly competitive, the reliance on AI-powered analytics tools is expected to intensify, further driving market expansion.

Another significant driver is the growing emphasis on fan engagement and media innovation. Sports organizations are leveraging AI to deliver personalized content, interactive experiences, and real-time statistics to fans through digital platforms and broadcast media. AI-powered systems can analyze viewer preferences, social media interactions, and historical data to tailor content and advertisements, enhancing fan loyalty and opening new revenue streams. The integration of AI in broadcasting also enables automated highlight generation, advanced commentary, and immersive viewing experiences, which are reshaping the sports entertainment landscape and contributing to the rapid adoption of AI-based analytics solutions.

The increasing collaboration between technology providers and sports entities is further accelerating the market’s growth trajectory. Partnerships between AI software developers, sports analytics firms, and professional sports teams are resulting in the development of customized solutions tailored to specific sports and organizational needs. Investments in research and development, coupled with the proliferation of cloud computing and IoT devices, are making AI-powered analytics more accessible and cost-effective. As a result, even mid-tier and amateur sports organizations are beginning to adopt these technologies, broadening the market’s addressable base and fueling sustained growth.

From a regional perspective, North America currently dominates the AI in Sports Analytics market, accounting for the largest share in 2024, thanks to the presence of leading sports franchises, advanced technological infrastructure, and high investment in sports technology. However, Europe and the Asia Pacific regions are rapidly emerging as key growth markets, driven by increasing sports commercialization, digital transformation initiatives, and the rising popularity of sports such as football, cricket, and basketball. The Middle East & Africa and Latin America are also witnessing growing adoption, albeit at a relatively slower pace, due to increasing investments in sports infrastructure and the proliferation of digital platforms.

Component Analysis

The Component segment of the AI in Sports Analytics market is bifurcated into Software and Services. Software solutions constitute the backbone of AI-driven analytics, encompassing platforms for data collection, processing, visualization, and predictive modeling. These platforms are being widely adopted by sports teams and associations for tasks such as performance tracking, tactical analysis, and injury prevention. The demand for ad

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This research expanded on offenders' decisions whether or not to offend by having explored a range of alternatives within the "not offending" category, using a framework derived from the concept of crime displacement. Decision trees were employed to analyze the multi-staged decision-making processes of criminals who are blocked from offending due to a situational crime control or prevention measure. The researchers were interested in determining how offenders evaluated displacement options as available alternatives. The data were collected through face-to-face interviews with 200 adult offenders, either in jail or on probation under the authority of the Texas Department of Criminal Justice, from 14 counties. Qualitative data collected as part of this study's methodology are not included as part of the data collection at this time. Three datasets are included as part of this collection: NIJ-2013-3454_Part1_Participants.sav (200 cases, 9 variables) NIJ-2013-3454_Part2_MeasuresSurvey.sav (2415 cases, 6 variables) NIJ-2013-3454_Part3_Vignettes.sav (1248 cases, 10 variables) Demographic variables included: age, gender, race, and ethnicity.