The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

The market for artificial intelligence grew beyond *** billion U.S. dollars in 2025, a considerable jump of nearly ** billion compared to 2023. This staggering growth is expected to continue, with the market racing past the trillion U.S. dollar mark in 2031. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together, these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on various factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

AI Productivity Tool Market Outlook

The global AI productivity tool market size was valued at $2.8 billion in 2023 and is projected to reach $12.6 billion by 2032, growing at a robust CAGR of 18.3%. The primary growth factor for this market is the increasing integration of AI technologies in workplace environments to enhance efficiency and streamline operations across various sectors.

One of the significant growth factors for the AI productivity tool market is the rapid advancement in AI and machine learning technologies. The evolution of natural language processing (NLP) and machine learning algorithms has enabled the development of sophisticated tools that can perform complex tasks, such as project management, time tracking, communication, and data analysis, more efficiently than human counterparts. These advancements have made AI productivity tools more accessible and practical for businesses of all sizes, driving market growth.

Another crucial factor contributing to the market's expansion is the increasing demand for remote working solutions. The COVID-19 pandemic has accelerated the trend toward remote work, prompting organizations to adopt AI-driven productivity tools to maintain and even improve employee efficiency and collaboration despite physical distances. This shift has created a fertile ground for the proliferation of AI productivity tools that facilitate seamless communication, project management, and data analysis, further driving the market's growth.

Furthermore, the rising focus on data-driven decision-making across industries is fueling the demand for AI productivity tools. Businesses are increasingly leveraging AI tools to analyze large volumes of data to gain actionable insights that can improve operational efficiency, customer satisfaction, and overall business performance. The ability of AI productivity tools to provide real-time data analysis and predictive analytics is making them indispensable for organizations striving for a competitive edge in the market.

From a regional perspective, North America currently leads the AI productivity tool market, primarily due to the presence of major technology companies and early adoption of advanced technologies. The region's strong technological infrastructure and favorable government policies supporting innovation are significant drivers. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing digital transformation initiatives and rising investments in AI technologies by both private and public sectors.

Component Analysis

The AI productivity tool market is segmented into software, hardware, and services. The software segment is the most dominant, given the need for sophisticated algorithms and platforms capable of processing complex tasks. AI productivity software includes various applications such as project management, time tracking, communication and collaboration, and data analysis tools. These software solutions are designed to enhance productivity by automating routine tasks and providing intelligent insights, thereby allowing employees to focus on higher-value activities.

In the hardware segment, the focus is on high-performance computing systems and storage solutions that can support AI applications. The growing demand for AI-powered devices such as smart assistants and enterprise-grade AI servers is propelling the hardware market. As AI applications become more complex, the need for robust hardware that can handle large datasets and advanced algorithms continues to grow. This segment is crucial for ensuring the seamless operation of AI productivity tools.

The services segment comprises consulting, implementation, and maintenance services. As organizations adopt AI productivity tools, they often require guidance on how to integrate these tools into their existing workflows and IT infrastructure. Consulting services play a vital role in helping businesses understand the potential benefits and challenges associated with AI implementation. Additionally, ongoing maintenance services ensure that AI tools operate efficiently and stay up-to-date with the latest advancements in AI technology.

Overall, the component analysis highlights the importance of a holistic approach in the AI productivity tool market, where software, hardware, and services collectively contribute to the effective deployment and utilization of AI technologies. Each component is interdependent, and advancements in one area often drive innovation and growth in

Snapshot img

Artificial Intelligence Text Generator Market Size 2024-2028

The artificial intelligence (AI) text generator market size is forecast to increase by USD 908.2 million at a CAGR of 21.22% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One of these trends is the increasing popularity of AI generators in various sectors, including education for e-learning applications. Another trend is the growing importance of speech-to-text technology, which is becoming increasingly essential for improving productivity and accessibility. However, data privacy and security concerns remain a challenge for the market, as generators process and store vast amounts of sensitive information. It is crucial for market participants to address these concerns through strong data security measures and transparent data handling practices to ensure customer trust and compliance with regulations. Overall, the AI generator market is poised for continued growth as it offers significant benefits in terms of efficiency, accuracy, and accessibility.

What will be the Size of the Artificial Intelligence (AI) Text Generator Market During the Forecast Period?

Request Free Sample

The market is experiencing significant growth as businesses and organizations seek to automate content creation across various industries. Driven by technological advancements in machine learning (ML) and natural language processing, AI generators are increasingly being adopted for downstream applications in sectors such as education, manufacturing, and e-commerce. 
Moreover, these systems enable the creation of personalized content for global audiences in multiple languages, providing a competitive edge for businesses in an interconnected Internet economy. However, responsible AI practices are crucial to mitigate risks associated with biased content, misinformation, misuse, and potential misrepresentation.

How is this Artificial Intelligence (AI) Text Generator Industry segmented and which is the largest segment?

The artificial intelligence (AI) text generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Service


Application

  Text to text
  Speech to text
  Image/video to text


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) text generators have gained significant traction in various industries due to their efficiency and cost-effectiveness in content creation. These solutions utilize machine learning algorithms, such as Deep Neural Networks, to analyze and learn from vast datasets of human-written text. By predicting the most probable word or sequence of words based on patterns and relationships identified In the training data, AIgenerators produce personalized content for multiple languages and global audiences. The application spans across industries, including education, manufacturing, e-commerce, and entertainment & media. In the education industry, AI generators assist in creating personalized learning materials.

Get a glance at the Artificial Intelligence (AI) Text Generator Industry report of share of various segments Request Free Sample

The solution segment was valued at USD 184.50 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market holds the largest share in the market, driven by the region's technological advancements and increasing adoption of AI in various industries. AI text generators are increasingly utilized for content creation, customer service, virtual assistants, and chatbots, catering to the growing demand for high-quality, personalized content in sectors such as e-commerce and digital marketing. Moreover, the presence of tech giants like Google, Microsoft, and Amazon in North America, who are investing significantly in AI and machine learning, further fuels market growth. AI generators employ Machine Learning algorithms, Deep Neural Networks, and Natural Language Processing to generate content in multiple languages for global audiences.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and c

Generative Artificial Intelligence (AI) models such as OpenAI’s ChatGPT have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research. However, these tools are in the early stages of development and can be easily misused or misunderstood. In this paper, we give an overview of the development of Generative AI. Specifically, we explore ChatGPT’s ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research. By investigating responses to structured prompts, we highlight the benefits and limitations of the results. Our study indicates that the current version of ChatGPT performs well for structured tasks, such as translating code from one language to another and explaining well-known concepts but struggles with more nuanced tasks, such as explaining less widely known terms and creating code from scratch. We find that using new AI tools may help practitioners, educators, and researchers to be more efficient and productive. However, in their current stages of development, some results are misleading and wrong. Overall, the use of generative AI models in SPC must be properly validated and used in conjunction with other methods to ensure accurate results.

Our most comprehensive database of AI models, containing over 800 models that are state of the art, highly cited, or otherwise historically notable. It tracks key factors driving machine learning progress and includes over 300 training compute estimates.

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 (AI) in Supply Chain and Logistics Market Outlook

The Artificial Intelligence (AI) in Supply Chain and Logistics market is currently witnessing robust growth, with a market size valued at USD 5.2 billion in 2023, and it is projected to reach USD 15.7 billion by 2032, reflecting a strong compound annual growth rate (CAGR) of 13.2% over the forecast period. This expansion is driven by the increasing adoption of AI technologies to streamline operations, enhance efficiency, and improve decision-making processes in supply chain and logistics, which are crucial in today’s fast-paced economic environment. The relentless push for automation and precision in supply chain operations is further fueling the growth of AI in this sector, as businesses seek to leverage technology to remain competitive and meet rising consumer expectations.

One of the major growth factors in this market is the growing demand for transparency and efficiency in supply chain operations. As global trade continues to expand, the need for more efficient and transparent supply chain management has become increasingly critical. AI technologies are playing a pivotal role in meeting these demands by providing advanced analytical capabilities, machine learning algorithms, and real-time data processing, which enable companies to gain deeper insights into their operations. This leads to improved inventory management, reduced operational costs, and enhanced customer satisfaction, all of which are essential for maintaining competitiveness in the global market.

Another significant driver of market growth is the integration of AI with the Internet of Things (IoT) and big data analytics. IoT devices generate a massive amount of data that, when analyzed using AI technologies, can provide valuable insights into supply chain operations. These insights facilitate better demand forecasting, predictive maintenance, and optimized route planning, which help in reducing delays, minimizing costs, and improving overall operational efficiency. The synergy between AI and IoT, along with the increasing availability of big data, is therefore a crucial factor propelling the growth of AI in supply chain and logistics.

The rising need for enhanced customer experience is also contributing to the growth of AI in the supply chain and logistics market. Consumers now expect faster delivery times, accurate tracking, and flexible delivery options. AI solutions enable companies to meet these expectations by optimizing logistics operations, reducing errors, and providing real-time tracking information. Moreover, AI-powered chatbots and virtual assistants are being used to enhance customer service by providing instant responses to customer queries, thereby improving customer satisfaction and loyalty.

Regionally, the Asia Pacific market is expected to witness significant growth due to the rapid industrialization and increasing adoption of AI technologies in countries like China, Japan, and India. The presence of a large number of manufacturing units and the increasing trend of e-commerce in this region are further driving the demand for AI in supply chain and logistics. In North America, the market is driven by the strong presence of key players and the early adoption of advanced technologies. Europe is also witnessing steady growth, with companies investing in AI solutions to optimize their supply chain operations and improve efficiency.

Component Analysis

The AI in Supply Chain and Logistics market is segmented into software, hardware, and services, each playing a critical role in the integration and functioning of AI technologies within this sector. The software segment is projected to hold a significant share of the market due to the increasing demand for AI-driven solutions that can handle complex data analytics, demand forecasting, and supply chain optimization. Software solutions are crucial for implementing machine learning algorithms, natural language processing, and predictive analytics, which are essential for enhancing decision-making processes in logistics operations. Companies are increasingly investing in software development to create customized AI solutions that cater to specific supply chain needs, thereby driving the growth of this segment.

In addition to software, the hardware segment is also experiencing steady growth, although at a slower pace compared to software. Hardware components such as sensors, servers, and storage devices form the backbone of AI systems, providing the necessary infrastructure for data collection, processing, and storage. As AI and IoT technologies become more intertwined

The Generative AI Market size was valued at USD 43.87 USD Billion in 2023 and is projected to reach USD 453.28 USD Billion by 2032, exhibiting a CAGR of 39.6 % during the forecast period. The market's expansion is driven by the increasing adoption of AI in various industries, the growing demand for personalized experiences, and the advancement of machine learning and deep learning technologies. Generative AI is a form of AI technology that come with the capability to generate content in several of forms such us that include text, images, audio data, and artificial data. In the latest trend of the use of generative AI, fingertip friendly interfaces that allow for the creation of top-quality text design, and videos in a brief time of only seconds have been the leading cause of the hype around it. The AI technology called Generative AI employs a variety of techniques that its development is still being improved. Fundamentally, AI foundation models are based on training on a wide spate of unlabelled data that can be used for many tasks; working primarily on specific areas where additional fine-tuning finds its place. Over-simplifying the process, huge amounts of maths and computer power get used to develop AI models. Nevertheless, at its core, it is the predictions amplified. Generative AI relies on deep learning models – sophisticated machine learning models that work as neural networks and learn and take decisions just the human minds do. Such models are based on the detection and emission of codes of complex relationships or patterns in huge information volumes and that data is used to respond to users' original speech requests or questions with native language replies or new content. Recent developments include: June 2023: Salesforce launched two generative artificial intelligence (AI) products for commerce experience and customized consumers –Commerce GPT and Marketing GPT. The Marketing GPT model leverages data from Salesforce's real-time data cloud platform to generate more innovative audience segments, personalized emails, and marketing strategies., June 2023: Accenture and Microsoft are teaming up to help companies primarily transform their businesses by harnessing the power of generative AI accelerated by the cloud. It helps customers find the right way to build and extend technology in their business responsibly., May 2023: SAP SE partnered with Microsoft to help customers solve their fundamental business challenges with the latest enterprise-ready innovations. This integration will enable new experiences to improve how businesses attract, retain and qualify their employees. , April 2023: Amazon Web Services, Inc. launched a global generative AI accelerator for startups. The company’s Generative AI Accelerator offers access to impactful AI tools and models, machine learning stack optimization, customized go-to-market strategies, and more., March 2023: Adobe and NVIDIA have partnered to join the growth of generative AI and additional advanced creative workflows. Adobe and NVIDIA will innovate advanced AI models with new generations aiming at tight integration into the applications that significant developers and marketers use. . Key drivers for this market are: Growing Necessity to Create a Virtual World in the Metaverse to Drive the Market. Potential restraints include: Risks Related to Data Breaches and Sensitive Information to Hinder Market Growth . Notable trends are: Rising Awareness about Conversational AI to Transform the Market Outlook .

A worldwide survey carried out in 2024 showed that Boomers are the most concerned about the use of personal data when shopping online. 60 percent of them avoided sharing personal details because they did not trust data privacy with AI technologies.

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...

Executive Summary: Artificial intelligence (AI) is a transformative technology that holds promise for tremendous societal and economic benefit. AI has the potential to revolutionize how we live, work, learn, discover, and communicate. AI research can further our national priorities, including increased economic prosperity, improved educational opportunities and quality of life, and enhanced national and homeland security. Because of these potential benefits, the U.S. government has invested in AI research for many years. Yet, as with any significant technology in which the Federal government has interest, there are not only tremendous opportunities but also a number of considerations that must be taken into account in guiding the overall direction of Federally-funded R&D in AI. On May 3, 2016,the Administration announced the formation of a new NSTC Subcommittee on Machine Learning and Artificial intelligence, to help coordinate Federal activity in AI.1 This Subcommittee, on June 15, 2016, directed the Subcommittee on Networking and Information Technology Research and Development (NITRD) to create a National Artificial Intelligence Research and Development Strategic Plan. A NITRD Task Force on Artificial Intelligence was then formed to define the Federal strategic priorities for AI R&D, with particular attention on areas that industry is unlikely to address. This National Artificial Intelligence R&D Strategic Plan establishes a set of objectives for Federallyfunded AI research, both research occurring within the government as well as Federally-funded research occurring outside of government, such as in academia. The ultimate goal of this research is to produce new AI knowledge and technologies that provide a range of positive benefits to society, while minimizing the negative impacts. To achieve this goal, this AI R&D Strategic Plan identifies the following priorities for Federally-funded AI research: Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve optimal performance. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. . Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. The AI R&D Strategic Plan closes with two recommendations: Recommendation 1: Develop an AI R&D implementation framework to identify S&T opportunities and support effective coordination of AI R&D investments, consistent with Strategies 1-6 of this plan. Recommendation 2: Study the national landscape for creating and sustaining a healthy AI R&D workforce, consistent with Strategy 7 of this plan.

Snapshot img

Cloud Artificial Intelligence (AI) Market Size 2024-2028

The cloud artificial intelligence (ai) market size is forecast to increase by USD 12.61 billion, at a CAGR of 24.1% between 2023 and 2028.

The market is experiencing significant growth, driven by the emergence of technologically advanced devices and the increasing adoption of 5G and mobile penetration. These advancements enable faster and more efficient data processing, leading to increased demand for cloud-based AI solutions. However, the market also faces challenges from open-source platforms, which offer free alternatives to proprietary AI offerings. Companies must navigate this competitive landscape by focusing on providing value-added services and maintaining a strong competitive edge through innovation and differentiation. To capitalize on market opportunities, organizations should explore applications in sectors such as healthcare, finance, and manufacturing, where AI can drive operational efficiency, enhance customer experiences, and generate new revenue streams. Effective strategic planning and a strong focus on data security will be crucial for businesses seeking to succeed in this dynamic and evolving market.

What will be the Size of the Cloud Artificial Intelligence (AI) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in machine learning (ML), computer vision, and natural language processing. Bias mitigation and responsible AI are increasingly prioritized, with knowledge graphs and explainable AI (XAI) playing crucial roles in ensuring transparency and trust. Agile development and AI ethics are integral to creating ethical and unbiased AI systems. ML models are being applied across various sectors, from fraud detection and sales forecasting to speech recognition and image recognition. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Deep learning (DL) and reinforcement learning are advancing rapidly, enabling more sophisticated AI applications. Semantic reasoning and predictive analytics are transforming decision making, while AI-powered chatbots and virtual assistants enhance customer service. Data labeling and model training are essential components of AI development, with API integration streamlining deployment and model training. Risk management and predictive analytics are critical for businesses seeking to mitigate potential threats and optimize operations. The ongoing unfolding of market activities reveals a dynamic landscape, with AI regulations and governance emerging as key considerations. Sentiment analysis and text analytics offer valuable insights into customer behavior and preferences. In the ever-evolving AI ecosystem, continuous innovation and adaptation are essential. The integration of various AI technologies and applications will shape the future of business and society.

How is this Cloud Artificial Intelligence (AI) Industry segmented?

The cloud artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ComponentSoftwareServicesGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.Artificial Intelligence (AI) software development is a significant area of innovation in the business world, with applications ranging from automating operations to personalizing service delivery and generating insights. AI technologies, such as machine learning (ML), deep learning (DL), computer vision, speech recognition, and natural language processing, are transforming industries. Responsible AI practices, including bias mitigation and explainable AI (XAI), are crucial for building trust and ensuring fairness in AI systems. Agile development methodologies facilitate the integration of AI capabilities into existing software. Data security and privacy are paramount in AI implementations. Cloud computing and edge computing provide flexible solutions for storing and processing sensitive data. AI regulations, such as those related to data privacy and security, are shaping the market. AI ethics are also a critical consideration, with transparency and accountability essential for building trust in AI systems. AI is revolutionizing various industries, from healthcare to finance and marketing. In healthcare, AI is used for predictive analytics, sales forecasting, and fraud detection, improving patient outcomes and operational efficiency. In finance, AI is used for risk management

Data Intelligence Platform Market Outlook

The global data intelligence platform market size was valued at approximately $10 billion in 2023, with an anticipated growth to reach $25.2 billion by 2032, growing at a robust CAGR of 11%. The market's growth is predominantly driven by the increasing demand for data-driven decision-making processes and the need for advanced analytics tools across various industries.

The surge in the adoption of data intelligence platforms is largely influenced by advancements in big data technologies and the growing importance of data governance and security. Organizations across sectors such as BFSI, healthcare, and retail are increasingly leveraging data intelligence solutions to enhance operational efficiency, personalize customer experiences, and drive strategic initiatives. The integration of AI and machine learning with data intelligence platforms has further fueled market growth by providing predictive insights and automation capabilities.

Another significant growth factor is the proliferation of cloud-based solutions, which offer scalability, cost-efficiency, and ease of deployment. Cloud-based data intelligence platforms allow organizations to handle large volumes of data and perform complex analytics without the need for extensive on-premises infrastructure. The shift towards cloud computing is also driven by the growing need for remote working capabilities and digital transformation initiatives, further propelling market expansion.

Moreover, regulatory compliance and the emphasis on data protection laws such as GDPR in Europe and CCPA in the United States have compelled organizations to adopt robust data intelligence solutions. These platforms help ensure that data management practices align with regulatory requirements, thereby mitigating risks and enhancing data security. The rising awareness of the importance of data integrity and privacy is expected to drive the adoption of data intelligence platforms across various sectors.

The emergence of AI-Driven Analytics Platform is revolutionizing the way organizations approach data intelligence. These platforms leverage artificial intelligence to automate complex data processes, providing businesses with real-time insights and predictive analytics. By integrating AI capabilities, companies can enhance their decision-making processes, optimize operations, and gain a competitive edge in the market. The ability to analyze vast amounts of data quickly and accurately allows organizations to identify trends, detect anomalies, and make informed decisions that drive business growth. As AI technology continues to evolve, the potential for AI-Driven Analytics Platforms to transform industries and unlock new opportunities is immense.

Regionally, North America dominates the data intelligence platform market, owing to the presence of leading technology providers and high adoption rates of advanced analytics solutions. The Asia Pacific region is also witnessing significant growth due to the rapid digitalization of enterprises and increased investments in data infrastructure. Europe, on the other hand, is experiencing steady growth driven by stringent data protection regulations and the increasing adoption of cloud-based solutions.

Component Analysis

The data intelligence platform market by component is bifurcated into software and services. The software segment holds a major share in the market, driven by the increased demand for advanced analytics, business intelligence tools, and data management solutions. Software components include various types of analytics platforms, data integration tools, and AI-driven data intelligence solutions. Organizations are investing heavily in these software solutions to gain real-time insights, enhance decision-making processes, and improve overall operational efficiency.

Within the software segment, AI and machine learning-based applications have seen significant traction. These applications enable predictive analytics, automate routine data processing tasks, and provide deeper insights into business trends and customer behaviors. The integration of AI has revolutionized data intelligence platforms by making them more intuitive, efficient, and capable of handling large datasets with ease. This trend is expected to continue, with more companies adopting AI-enabled software solutions to stay competitive.

On the other hand, the services segme

Artificial intelligence has taken over the app world, with thousands of apps integrating AI and the top AI app developers receiving hundred billion dollar valuations. Generative AI, in the form of...

The Full Data AI Smart Management and Control Cloud Platform market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and cloud computing across various industries. The market's expansion is fueled by the need for efficient data management, enhanced operational efficiency, and improved decision-making capabilities. Organizations are increasingly seeking cloud-based solutions to manage and analyze their vast datasets, leading to a high demand for platforms that integrate AI-powered analytics and automation. Key drivers include the rising volume of data generated across industries, the need for real-time insights, and the increasing sophistication of AI algorithms capable of handling complex data sets. The market is segmented by deployment model (public, private, hybrid), industry vertical (healthcare, finance, retail, manufacturing), and functionalities offered (data ingestion, processing, analytics, visualization, automation). While challenges remain in terms of data security, integration complexities, and the need for skilled professionals, the market is expected to maintain a strong Compound Annual Growth Rate (CAGR). The presence of established players like Microsoft, IBM, and Amazon Web Services, alongside emerging innovative companies, indicates a highly competitive but rapidly evolving landscape. This competition fosters innovation and drives down costs, making these solutions accessible to a wider range of organizations. The forecast period of 2025-2033 anticipates substantial growth within the Full Data AI Smart Management and Control Cloud Platform market. This growth will be fueled by continuous advancements in AI and machine learning technologies, leading to more sophisticated and efficient platform functionalities. Furthermore, increasing government regulations mandating data security and compliance will drive the adoption of robust and secure cloud-based platforms. Geographic expansion, especially in developing economies with rapidly digitizing sectors, will also contribute to market expansion. However, factors like the high initial investment required for implementation and the ongoing need for skilled personnel to manage and maintain these complex systems could potentially act as restraints. The competitive landscape is characterized by a blend of established technology giants and agile startups, resulting in a dynamic market with ongoing innovation and strategic partnerships. The market's success will hinge on the continuous development of user-friendly interfaces, improved data security measures, and the ability to integrate seamlessly with existing IT infrastructure.

Snapshot img

Conversational AI Market Size 2025-2029

The conversational ai market size is forecast to increase by USD 24.84 billion at a CAGR of 24.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the advancements in Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI) technologies. These technologies enable more sophisticated and human-like interactions between businesses and consumers, leading to increased customer engagement. However, resistance to using chatbots and concerns over data privacy and security remain challenges that market players must address. As more businesses seek to enhance their customer experiences and streamline operations, the demand for conversational AI solutions is expected to continue growing. Companies looking to capitalize on this market opportunity should focus on developing solutions that offer personalized interactions, seamless integration with existing systems, and robust security features. Additionally, partnerships and collaborations with industry leaders and innovative startups can help companies stay competitive and expand their offerings. Overall, the market presents significant opportunities for growth, with the potential to transform customer interactions and drive operational efficiencies.

What will be the Size of the Conversational AI Market during the forecast period?

Request Free SampleThe market is experiencing significant growth and innovation, with conversational agents and chatbots becoming increasingly integral to business operations. Bot development tools enable the creation of conversational ecosystems, while conversational AI platforms utilize semantic networks and language models to understand and respond to user queries. Conversational technology integration is a key trend, allowing for conversational assistants to streamline workflows and enhance user experience (UX). Moreover, conversational analytics dashboards provide valuable insights, enabling conversational reporting and data-driven decision-making. Knowledge graphs and conversational intelligence engines further enhance conversational capabilities, leading to a conversational revolution in various industries. The future of conversational AI lies in conversational automation frameworks, transformer networks, and continued conversational adoption. Businesses can leverage conversational trends and APIs to create engaging conversational experiences (CX) and improve customer interactions. Bot testing tools ensure the quality and performance of conversational assistants, while conversational UX design focuses on creating intuitive and user-friendly interfaces. As conversational technology continues to evolve, it will undoubtedly transform the way businesses engage with their customers and streamline internal processes.

How is this Conversational AI Industry segmented?

The conversational ai industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudTypeAI chatbotsVoice botsInteractive voice assistantsGenerative AI agentsMethodInternal enterprise systemsExternal communication channelsEnd-userBFSIRetail and e-commerceEducationMedia and entertainmentOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth Korea

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the realm of artificial intelligence (AI) deployment models, on-premises infrastructure has gained significant traction. This setup involves installing AI infrastructure within a business's premises, which often necessitates the use of high-performance computing (HPC) systems, occupying over 100 square meters. The primary reason for this trend is the heightened emphasis on data security. With on-premises AI infrastructure, businesses retain complete control over their hardware and software. This control appeals to numerous global clients, who demand stringent security measures for their data. Consequently, the adoption of on-premises AI infrastructure is on the rise. Human-computer interaction (HCI), dialogue management, intent classification, conversational analytics, and machine learning (ML) are integral components of AI infrastructure. These technologies enable advanced functionalities, such as conversational commerce, conversational retail, conversational healthcare, conversational design, conversational travel, and conversational optimization. As businesses continue to prioritize data security, the demand for on-premises AI infrastructure is expected to persist.

Request Free Sample

The On-premises segment was valued at USD 2.21 billion in 2019 and showed a gradual increase during the forecast period.

Re

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...

Abstract:

In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems. The major contributions have been materialized in the form of novel algorithms. Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms. Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.
Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research. Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.

General Information:

This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.

You may find details of this dataset from the original paper:

Sasho Nedelkoski, Ajay Kumar Mandapati, Jasmin Bogatinovski, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". [link very soon]

If you use the data, implementation, or any details of the paper, please cite!

The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth (all at the Zenodo link below). We provide two datasets, which differ on how the workload is executed. The openstack_multimodal_sequential_actions is generated via executing workload of sequential user requests. The openstack_multimodal_concurrent_actions is generated via executing workload of concurrent user requests.

The difference of the concurrent dataset is that:

Due to the heavy load on the control node, the metric data for wally113 (control node) is not representative and we excluded it.

Three rally actions are executed in parallel: boot_and_delete, create_and_delete_networks, create_and_delete_image, whereas for the sequential there were 5 actions executed.

The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.

Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods.

Our GitHub repository can be found at: https://github.com/SashoNedelkoski/multi-source-observability-dataset/

Introduction

The global AI in chip design market is poised for significant growth, with an estimated value expected to reach USD 27.6 billion by 2033, representing a robust Compound Annual Growth Rate (CAGR) of 31.4% from 2024 to 2033.

The integration of Artificial Intelligence (AI) in chip design is transforming the semiconductor industry, enabling more efficient and faster design processes. AI algorithms assist in optimizing chip layouts, predicting system performance, and automating tedious aspects of the design process, such as routing and placement. This incorporation of AI not only reduces the time-to-market for new chips but also enhances their performance and power efficiency.

The market for AI in chip design is experiencing significant growth, driven by the increasing demand for smarter, faster computing devices across various sectors including automotive, consumer electronics, and data centers. Market analysis indicates a rising trend in investments from major semiconductor companies and startups alike, focusing on developing AI-enhanced design tools and solutions.

The proliferation of IoT devices and the advent of 5G technology are further catalyzing the market expansion, as they require advanced chipsets that can handle extensive data processing at high speeds. As AI technology continues to evolve, its role in chip design is expected to become more pivotal, presenting lucrative opportunities for growth in this sector.

However, the AI in chip design market also faces notable challenges. High initial investment costs, complexity of AI algorithms, and the need for specialized skills pose barriers to entry. Additionally, concerns regarding data privacy and the ethical use of AI continue to loom, requiring robust regulatory frameworks.

Despite these challenges, there are substantial opportunities for new entrants. The rapid evolution of technologies such as IoT and 5G, coupled with the growing emphasis on AI capabilities in mobile and edge computing devices, opens up vast markets for innovative solutions in AI chip design. Newcomers with niche technological expertise or those who form strategic alliances with established players can potentially carve out significant positions in this dynamic landscape.