The market size change in the 'Machine Learning' segment of the artificial intelligence market worldwide was modeled to be 202.94 percent in 2025. Between 2021 and 2025, the market size change rose by 153.32 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size change is forecast to decline by 188.86 percentage points from 2025 to 2032, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Machine Learning.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

The market size in the 'Natural Language Processing' segment of the artificial intelligence market worldwide was modeled to amount to 120.73 billion U.S. dollars in 2025. Between 2020 and 2025, the market size rose by 99.45 billion U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size will steadily rise by 251.48 billion U.S. dollars over the period from 2025 to 2032, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Natural Language Processing.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

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.

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

The market size change in the 'Autonomous & Sensor Technology' segment of the artificial intelligence market worldwide was modeled to be 91.05 percent in 2025. Between 2021 and 2025, the market size change rose by 39.44 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend. The market size change is forecast to decline by 79.45 percentage points from 2025 to 2032, fluctuating as it trends downward.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Autonomous & Sensor Technology.

Comprehensive global artificial intelligence market data including market size, adoption rates, investment trends, and projections from 2017 to 2030.

snapshot-tab-pane Artificial Intelligence (AI) In Education Sector Market Size 2026-2030The artificial intelligence (ai) in education sector market size is valued to increase by USD 3.37 billion, at a CAGR of 45% from 2025 to 2030. Escalating demand for personalized and adaptive learning experiences will drive the artificial intelligence (ai) in education sector market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 41.7% growth during the forecast period.By End-user - Higher education segment was valued at USD 285.7 million in 2024By Learning Method - Learner model segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 3.86 billionMarket Future Opportunities: USD 3.37 billionCAGR from 2025 to 2030 : 45%Market SummaryThe artificial intelligence in education sector is experiencing profound expansion as institutions pivot toward intelligent infrastructure to enhance scalability and engagement. A key driver is the move from standardized instruction to hyper-personalized education, where machine learning algorithms and natural language processing create adaptive learning environments. This transition is supported by the broad adoption of cloud-based platforms, which facilitate data-driven instruction.A significant trend is the evolution from basic generative models to sophisticated agentic AI workflows that automate administrative tasks like grading and scheduling, allowing educators to focus on mentorship. For instance, a university system can deploy predictive analytics to analyze student performance metrics, identifying at-risk individuals and enabling timely interventions to reduce dropout rates.However, challenges such as data sovereignty and the need for robust AI ethics frameworks persist, requiring careful management to ensure equitable and secure implementation across diverse educational settings.What will be the Size of the Artificial Intelligence (AI) In Education Sector Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Artificial Intelligence (AI) In Education Sector Market Segmented?The artificial intelligence (ai) in education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.End-userHigher educationK-12Learning methodLearner modelPedagogical modelDomain modelComponentSolutionsServicesApplicationLearning platform and virtual facilitatorsIntelligent tutoring system (ITS)Smart contentFraud and risk managementOthersGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceAPACChinaIndiaJapanSouth AmericaBrazilArgentinaMiddle East and AfricaSaudi ArabiaUAESouth AfricaRest of World (ROW)By End-user InsightsThe higher education segment is estimated to witness significant growth during the forecast period.The higher education segment is at the forefront of the artificial intelligence in education sector, driven by strategic integration across all operational facets.A notable shift from pilot programs to systemic adoption is evident, with institution-wide implementation of AI surging to 66%. Universities are leveraging generative models and AI-powered course recommendations to develop specialized teaching materials and intelligent tutoring systems.The use of digital twin learners and predictive analytics has become central to institutional strategy, enabling proactive student interventions.This move is supported by advanced curriculum architecture and sophisticated pedagogical models that cater to a diverse student body, focusing on AI-driven skill mapping to prepare graduates for a landscape dominated by automated systems and machine learning algorithms, which is a part of digital transformation in education. Get Free SampleThe Higher education segment was valued at USD 285.7 million in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 41.7% 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. See How Artificial Intelligence (AI) In Education Sector Market Demand is Rising in North America Get Free SampleThe geographic landscape of the artificial intelligence in education sector is led by North America, where 86% of educational organizations have adopted generative artificial intelligence for administrative and instructional enhancement.This region's dominance is supported by a mature technological infrastructure and substantial private sector investment in AI-e

The UK artificial intelligence market size reached USD 3.3 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 20.5 Billion by 2033, exhibiting a growth rate (CAGR) of 22.56% during 2025-2033. The growing implementation of policies, funding programs, and strategic initiatives by governing agencies, increasing adoption of AI technologies across various sectors, and rapid development of AI infrastructure are some of the factors propelling the growth of the market.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on solution, technology, function, and end-use.

Military Artificial Intelligence (AI) Market size was estimated at USD 13.24 Billion in 2024 and is projected to reach USD 35.54 Billion by 2032, growing at a CAGR of 14.49% from 2026 to 2032.Military Artificial Intelligence (AI) Market DriversThe Rise of Modern Warfare and Geopolitical Tensions: The global security landscape is becoming more complex, characterized by asymmetrical threats, hybrid warfare, and rising geopolitical tensions. This has led to an urgent need for militaries to adopt advanced technologies that can provide a decisive edge. AI is being integrated into military systems to address these challenges by enabling faster and more accurate decision-making on the battlefield, improving intelligence, and enhancing threat detection. The global push to modernize defense capabilities in response to these evolving threats is a primary driver of the military AI market.

By 2035, the Artificial Intelligence (AI) Market is projected to reach USD 3520.9 Bn, up from USD 244.3 Bn in 2025, at a CAGR of 30.6%.

AI statistics for B2B technology marketers — every figure traceable to the primary vendor or research-firm report. Coverage spans enterprise AI adoption, generative AI risk, AI-augmented threat detection, and AI infrastructure spending, reviewed by Nick Cavalancia.

The United States Artificial Intelligence (AI) Market worth USD 146,090 million in 2024 is growing at a CAGR of 24.10% to reach USD 533,643 million by 2030. IBM Corporation, Microsoft Corporation, Google LLC, Amazon Web Services, Inc. and NVIDIA Corporation are the major companies operating in this market.

The artificial intelligence market is estimated to grow from $273.6 billion currently to $5,267 billion by 2035, at a CAGR of 30.84% during the forecast period.

You can’t think about thinking without thinking about thinking about something.

A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)

This controlled vocabulary of keywords related to the field of Artificial Intelligence (AI) was built by SIRIS Academic in collaboration with ART-ER (the R&I and sustainable development in-house agency of the Emilia-Romagna region in Italy) and the Generalitat de Catalunya (the regional government of Catalonia, Spain), in order to identify AI research, development and innovation activities. The work was carried out by consulting domain experts' advice and it was ultimately applied to inform regional strategies on AI and research and innovation policy.

The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of AI.

The present effort was carried out because, despite the high number of contributions and technological developments in the field of AI, there is no closed or static vocabulary of concepts that allows to unequivocally define the boundaries of what should be considered “an Artificial Intelligence intellectual product” (or what should not). Indeed, the literature presents different definitions of the domain, with visions that could be contradictory. AI encompasses today a wide variety of subdomains, ranging from general purpose areas such as learning and perception to more specific ones such as autonomous vehicle driving, theorem proving, or industrial process monitoring. AI synthesises and automates intellectual tasks, and is therefore potentially relevant to any area of human intellectual activity. In this sense, it is a genuinely universal and multidisciplinary field. AI draws upon disciplines as diverse as cybernetics, mathematics, philosophy, sociology and economics.

As a ground for the construction of the AI controlled vocabulary, an initial set of concepts was taken from different subdomains of the ACM Computing Classification System 2012, to define the boundaries of the AI domain. Notably, although some relevant AI subdomains have an independent category in the ACM taxonomy outside of AI, they have been included in the list of subdomains. In order to align the ACM taxonomical definition with the Catalan Strategy of AI, CATALONIA.AI, in version 1 of this resource the emerging area of AI Ethics was included in the vocabulary, while some other categories which are not relevant for the objectives were removed from the subdomains list. In the current version 2, the classification and the labels of the subdomains have been revised because of the evolution of the field. Some fields have been grouped in order to reduce the overlap between subdomains and to provide a taxonomy that makes more sense for the analysis of R&I ecosystems.

The different subdomains in the versions are presented in the following table:

Although a keyword rule-based approach suffers from the major shortcomings of not capturing all the lexical and linguistic variants of specific concepts nor the context of the words - namely, keyword-based approaches would miss relevant texts if the specific pattern is not matched during the search - the present vocabulary allowed us to obtain fairly good results, due to the specificity of the concepts describing the AI domain. Furthermore, an understandable and transparent controlled vocabulary allows a better control of the final results and the final definition of the domain borders. Also, a plain list of terms allows a much easier and interactive engagement of interested stakeholders with different degrees of knowledge (such as, for instance, domain experts, policy-makers and potential users) who can make use of vocabulary to retrieve pertinent literature or to enrich the resource itself.

The vocabulary has been built taking advantage of advanced language models and resources from knowledge datasets such as arXiv, DBpedia and Wikipedia. The resulting vocabulary comprises 833 keywords, and has been validated by experts from several universities in Emilia-Romagna and Catalonia.

The version 0.5 of this resource was developed by the SIRIS Academic in 2019 in collaboration with ART-ER, Emilia-Romagna (Quinquillá et al., 2020), the version 1 was the result of an update done in 2020 in collaboration with the Generalitat de Catalunya, and the current version (version 2) has resulted in 2021 from the collaboration with ART-ER and the integration of an additional set of keywords provided by the Artificial Intelligence and Intelligence Systems (AIIS) Laboratory of the CINI (Consorzio interuniversitario nazionale per l’informatica based in Rome, Italy).

The methodology for the construction of the controlled vocabulary is presented in the following steps:

1. An initial set of scientific publications was collected by retrieving the following records as a weakly-supervised (in the sense that records are linked to AI by their taxonomy and not by a manual label) dataset in the domain of Artificial Intelligence :

   1. Publications from Scopus with the keyword “Artificial Intelligence”

   2. Publications from arXiv in the category “Artificial Intelligence”

   3. Publications in relevant journals in the scientific domain of “Artificial Intelligence”

2. An automated algorithm was used to retrieve, from the APIs of DBpedia, a series of terms that have some categorical relationships (i.e. those that are indexed as “sub-categories of”, “equivalent to”, among other relations in DBpedia) with the Artificial Intelligence concept and with the AI categories in the ACM taxonomy. The DBpedia tree has been exploited down to the level 3, and the relevant categories have been manually selected (for instance: Classification algorithms, Machine learning or Evolutionary computation) and others were ignored (for instance: Artificial intelligence in fiction, Robots or History of artificial intelligence) because they were not relevant, or not specifically in the domain.

3. The keywords in publications in the dataset were extracted from the keyword sections and from the abstracts. The keywords with a higher TF-IDF, using an IDF matrix in the open domain, have been selected. The co-occurrence of keywords with categories in specific AI subdomain and a clusterization of the main keywords has been used for a categorization of the keywords at the thematic level.

4. This list of keywords tagged by thematic category has been manually revised, removing the non-pertinent keywords and changing the wrong categorizations by fields.

5. The weak-supervised dataset in the domain of Artificial Intelligence is used to train a Word2Vec (Mikolov et al., 2013) word embedding model (a machine learning model based on neural networks).

6. The terms’ list is then enriched by means of automatic methods, which are run in parallel:

   1. The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.

   2. Further, terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.

7. The final list produced by steps 2-6 is manually revised.

The definition of the vocabulary does not, per se, allow to identify STI contributions to AI: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated

snapshot-tab-pane Aerospace Artificial Intelligence (AI) Market Size 2026-2030The aerospace artificial intelligence (ai) market size is valued to increase by USD 9.87 billion, at a CAGR of 44.9% from 2025 to 2030. Rising demand for autonomous operations and uncrewed systems will drive the aerospace artificial intelligence (ai) market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 36% growth during the forecast period.By Component - Software segment was valued at USD 699.1 million in 2024By End-user - Defense and military segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 11.35 billionMarket Future Opportunities: USD 9.87 billionCAGR from 2025 to 2030 : 44.9%Market SummaryThe aerospace artificial intelligence (AI) market is undergoing a significant transformation, moving beyond theoretical applications to deep industrial integration. This evolution is driven by the imperative to enhance operational efficiency, safety, and capability in an increasingly complex environment. Key drivers include the rising demand for autonomous flight systems and uncrewed aerial vehicles in both defense and commercial sectors.Concurrently, trends such as the adoption of digital twin technology for predictive maintenance and AI-powered data analytics are reshaping manufacturing and fleet management.A core application involves using machine learning models to analyze real-time sensor data from an aircraft engine, allowing maintenance crews to predict component failures weeks in advance and schedule repairs proactively, thereby avoiding costly unplanned downtime and improving fleet readiness. However, progress is tempered by challenges, notably the stringent regulatory hurdles for certifying non-deterministic AI systems and persistent cybersecurity vulnerabilities.The industry's trajectory depends on balancing rapid technological innovation with the rigorous safety and security standards that govern aviation.What will be the Size of the Aerospace Artificial Intelligence (AI) Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Aerospace Artificial Intelligence (AI) Market Segmented?The aerospace artificial intelligence (ai) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.ComponentSoftwareHardwareServicesEnd-userDefense and militaryCommercial aviationAircraft manufacturersSpace explorationAirportsApplicationMachine learningNatural language processingComputer visionContext awareness computingGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyUKAPACChinaJapanIndiaMiddle East and AfricaSaudi ArabiaUAEIsraelSouth AmericaBrazilArgentinaRest of World (ROW)By Component InsightsThe software segment is estimated to witness significant growth during the forecast period.The software segment is the core intelligence layer, where predictive maintenance algorithms and AI analytics toolkits enable advanced operational capabilities. This segment encompasses autonomous flight systems and the complex, verifiable and certifiable code required for deployment in mission-critical environments.Through the adoption of software-defined aircraft architectures, operators are achieving new levels of flexibility and safety.For instance, the integration of specialized computer vision inspection software has been shown to reduce manual inspection times by over 40% on certain assembly lines.This shift toward intelligent software platforms is fundamental, transforming static hardware into dynamic, self-monitoring assets that facilitate proactive safety prediction, enhanced by robust aerospace digital twins and sophisticated asset performance optimization. Get Free SampleThe Software segment was valued at USD 699.1 million in 2024 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 36% 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. See How Aerospace Artificial Intelligence (AI) Market Demand is Rising in North America Get Free SampleThe market's geographic landscape is led by North America, which accounts for over 42% of activity, primarily driven by substantial defense investments in agentic AI and swarming technology.This region’s focus on tactical superiority through AI enabled mission systems contrasts with the APAC region, which is demonstrating the fastest growth.In APAC, the push is toward smart manufactu

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