Generative AI Market by Component (Service, Software), Type (Autoregressive Models, Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs)), Deployment Models, Application, Industry Vertical - Global Forecast 2025-2032
Description
The Generative AI Market was valued at USD 18.43 billion in 2024 and is projected to grow to USD 21.86 billion in 2025, with a CAGR of 19.32%, reaching USD 75.78 billion by 2032.
A strategic introduction that frames generative AI as a catalyst for enterprise transformation, governance challenges, and competitive differentiation
Generative AI is no longer a laboratory curiosity; it is a strategic technology vector reshaping product roadmaps, customer experiences, and operational models across sectors. This introduction situates the technology within the broader enterprise agenda, highlighting how advances in model architectures, data availability, and compute economics have converged to accelerate practical deployments beyond proof-of-concept stages. Leaders are now grappling with an expanding set of opportunities that range from automated content creation to advanced decision support, each demanding coherent strategy, governance, and cross-functional execution.
As organizations progress from experimentation to systematic adoption, integration challenges emerge: aligning legacy systems with modern model-driven pipelines, defining appropriate risk tolerances, and developing measurable governance controls. Crucially, success depends less on isolated technical pilots and more on the ability to operationalize models in production, embed them in business processes, and measure value against strategic objectives. Therefore, the opening perspective emphasizes the imperative for executive engagement, cross-functional capability building, and an iterative approach to scaling generative AI initiatives while managing emerging ethical, legal, and operational risks.
A focused analysis of transformative shifts in technology, regulation, workforce skills, and business models driven by generative AI across industries
Recent months have witnessed a cascade of transformative shifts that are redefining the generative AI landscape at pace. First, the rapid maturation of transformer-based and related architectures has expanded the envelope of what models can generate reliably, enabling richer, multimodal applications that combine text, vision, and structured data. Concurrently, supply chain and compute trends have prompted organizations to rethink procurement, favoring hybrid deployment patterns that optimize cost, latency, and data residency. These technical shifts intersect with regulatory developments that are beginning to codify acceptable uses, explainability requirements, and data handling standards, raising the bar for operational compliance and auditability.
Workforce dynamics are changing as well, with demand for ML engineers, prompt engineers, and machine teaching specialists outpacing traditional roles; organizations are responding by reskilling internal talent and forming selective partnerships to complement gaps. Business models are evolving as companies embed generative capabilities into customer touchpoints and automate content workflows, which in turn reshapes value chains and competitive differentiation. Taken together, these shifts require leaders to adopt adaptive governance, invest in modular platforms that support model interchangeability, and cultivate an ecosystem strategy that balances in-house capability building with targeted vendor partnerships.
A rigorous assessment of the cumulative effects of United States tariffs in 2025 on AI supply chains, procurement strategies, and international collaborations
The imposition of United States tariffs in 2025 introduces a new layer of complexity for organizations reliant on global supply chains, specialized hardware, and cross-border services that underpin generative AI deployments. Increased duties on key components can alter procurement calculus for accelerators, data center equipment, and specialized software stacks, prompting organizations to reassess vendor relationships and total cost of ownership. In response, procurement teams are likely to intensify supplier diversification efforts and pursue localized sourcing or assembly options to mitigate exposure to tariff volatility.
Beyond direct procurement impacts, tariffs influence partnership architecture and the economics of cloud versus on-premise deployments. Organizations with stringent latency, privacy, or sovereignty requirements may accelerate investments in local infrastructure or cloud regions, while those seeking to control costs may explore alternative suppliers or optimize workload placement to minimize tariff impacts. These dynamics will also shape negotiation strategies with vendors, where contract clauses and pass-through pricing mechanisms become more salient. In sum, the tariff environment demands dynamic procurement playbooks, scenario planning for supply chain disruptions, and closer collaboration between legal, finance, and technology teams to preserve program momentum while managing incremental costs and compliance obligations.
A strategic breakdown of market segments encompassing components, model architectures, deployment approaches, application domains, and industry vertical dynamics
Understanding segmentation is critical to translating technology capability into business outcomes, and the market should be analyzed across multiple dimensions that reveal deployment patterns, technical trade-offs, and use-case alignment. From a component perspective, organizations differentiate between services and software, where managed services can accelerate time to value while software licenses and platforms support deeper customization and control over model behavior. When considering model types, the landscape spans autoregressive approaches, generative adversarial networks, recurrent neural networks, transformer architectures, and variational autoencoders, each bringing distinct strengths for tasks such as sequence generation, image synthesis, temporal prediction, contextual understanding, and latent representation learning respectively.
Deployment choices further segment activity between cloud-hosted solutions, which offer elasticity and reduced operational overhead, and on-premise installations that deliver tighter control over data residency and latency. Application-wise, generative AI is applied across chatbots and intelligent virtual assistants, content generation, predictive analytics, and robotics and automation, with each application demanding tailored integration, evaluation metrics, and governance controls. Finally, industry verticals-ranging from automotive and transportation to gaming, healthcare, IT and telecommunications, manufacturing, media and entertainment, and retail-exhibit unique regulatory constraints, data characteristics, and value levers that drive differentiated adoption pathways. By synthesizing these segmentation vectors, leaders can more precisely match technical approaches to business objectives and risk appetites, prioritizing investments that yield immediate operational benefits while preserving strategic optionality.
A regional overview of generative AI adoption contrasting regulatory frameworks, talent pools, infrastructure readiness, and strategic positioning across regions
Regional dynamics materially influence the pace and character of generative AI adoption, and comparing geographies provides clarity on regulatory, talent, and infrastructure factors that affect strategic planning. In the Americas, innovation hubs are leveraging robust venture activity and cloud infrastructure to scale early commercial deployments, while regulatory attention emphasizes privacy and consumer protection in selected jurisdictions. This environment encourages rapid product-led growth along with heightened scrutiny on responsible use and transparency.
In Europe, the Middle East and Africa, regulatory frameworks and data protection norms are shaping cautious but deliberate adoption; organizations often emphasize explainability, data minimization, and cross-border data transfer mechanisms, resulting in a preference for solutions that support strong governance and audit trails. Talent mobility and public-private initiatives in the region are advancing localized research and enterprise uptake, particularly in sectors with public interest considerations such as healthcare and public services. The Asia-Pacific region exhibits a mix of aggressive national AI strategies, substantial investment in compute and edge infrastructure, and fast-growing commercial use cases across retail, telecommunications, and manufacturing. Together, these regional profiles suggest that a differentiated go-to-market approach-tailored to regulatory expectations, talent availability, and infrastructure maturity-is essential for organizations seeking to capture value at scale across multiple geographies.
An incisive review of the leaders shaping generative AI through innovation, partnerships, product differentiation, and operational strategies across the ecosystem
Leading companies are shaping the generative AI ecosystem through a combination of platform offerings, model innovation, strategic partnerships, and go-to-market positioning. Market leaders tend to invest in modular platforms that enable rapid experimentation while preserving the ability to swap or fine-tune models as requirements evolve. Others differentiate through verticalized solutions that embed domain knowledge, making deployment faster and outputs more reliable for regulated industries. Partnerships between technology providers, cloud operators, and system integrators are increasingly common, allowing companies to combine specialized IP with global delivery capabilities.
Competitive advantage also arises from operational rigor: firms that establish repeatable model deployment pipelines, robust evaluation frameworks, and clear governance policies accelerate time to value while mitigating risk. Strategic M&A and partnerships play a role in closing capability gaps, especially where unique data assets or industry expertise confer differentiation. Ultimately, the companies that succeed will be those that couple technical innovation with disciplined product management, transparent governance, and pragmatic commercialization strategies that align with buyer needs and regulatory realities.
Actionable guidance for industry leaders to drive responsible adoption, optimize talent and procurement, and fortify ecosystem collaboration in the generative AI era
Industry leaders must pursue a set of practical actions to capture opportunity while managing risk; the first priority is to define a clear value framework that ties generative AI initiatives to measurable business outcomes and accountability. This requires executive sponsorship combined with cross-functional governance that includes legal, compliance, data science, and product teams. Equally important is talent strategy: leaders should invest in reskilling programs, create hybrid teams that blend domain experts with machine learning practitioners, and consider selective outsourcing to accelerate capability gaps.
From a procurement and architecture perspective, organizations should adopt flexible contracts and modular platform designs that support model portability and cost optimization. Responsible adoption should be operationalized through policies for data provenance, model monitoring, explainability, and bias mitigation, supplemented by regular audits and impact assessments. Finally, ecosystem engagement is essential: organizations should cultivate partnerships that provide access to specialized models, domain data, and integration expertise, while participating in industry consortia to inform standards and interoperability. Together, these actions form a pragmatic roadmap for leaders seeking to scale generative AI in a controlled, business-oriented manner.
A concise explanation of research methodology combining targeted qualitative interviews, primary data collection, secondary synthesis, and analytical frameworks
The research approach combines qualitative and quantitative methods to produce rigorous, decision-oriented findings. Primary inputs include structured interviews with senior technology and business leaders, product and engineering practitioners, procurement specialists, and relevant policy experts to capture diverse perspectives on operational challenges and strategic priorities. Secondary synthesis draws on peer-reviewed literature, industry white papers, conference proceedings, and vendor documentation to contextualize primary insights and identify emergent patterns across sectors.
Analytical frameworks are applied to assess technology readiness, governance maturity, and deployment risks, enabling a consistent comparison across companies and use cases. Case studies illustrate practical deployment scenarios, highlighting implementation choices, success factors, and lessons learned. Throughout the process, triangulation is used to validate findings, combining multiple sources and methods to reduce bias and increase confidence in the conclusions. Transparency in methodology and source attribution ensures that conclusions are reproducible and actionable for decision-makers who require evidence-based guidance for strategy and procurement.
A concise and conclusive closing synthesis that underscores long-term strategic priorities, ethical guardrails, and the role of leadership in AI adoption
In closing, organizations that approach generative AI with strategic clarity, disciplined execution, and strong governance will unlock durable advantages while minimizing downside exposure. Long-term priorities include embedding model management into standard IT and DevOps practices, prioritizing data quality and lineage, and developing transparent evaluation metrics that align with business goals. Ethical guardrails and regulatory compliance must be viewed as design constraints rather than afterthoughts, and leadership should communicate clear principles and accountability mechanisms to maintain trust among stakeholders.
The collective picture suggests that AI adoption will continue to be uneven across sectors and regions, but the underlying drivers-technical progress, ecosystem maturation, and regulatory evolution-are converging to make generative capabilities an operational imperative. Leaders who balance rapid experimentation with enterprise-grade controls, invest in talent and partnerships, and maintain flexible, modular architectures will be best positioned to convert technological potential into sustained business value. This synthesis underscores the need for integrated strategies that align technology choices with organizational structures, incentives, and risk appetite.
Note: PDF & Excel + Online Access - 1 Year
A strategic introduction that frames generative AI as a catalyst for enterprise transformation, governance challenges, and competitive differentiation
Generative AI is no longer a laboratory curiosity; it is a strategic technology vector reshaping product roadmaps, customer experiences, and operational models across sectors. This introduction situates the technology within the broader enterprise agenda, highlighting how advances in model architectures, data availability, and compute economics have converged to accelerate practical deployments beyond proof-of-concept stages. Leaders are now grappling with an expanding set of opportunities that range from automated content creation to advanced decision support, each demanding coherent strategy, governance, and cross-functional execution.
As organizations progress from experimentation to systematic adoption, integration challenges emerge: aligning legacy systems with modern model-driven pipelines, defining appropriate risk tolerances, and developing measurable governance controls. Crucially, success depends less on isolated technical pilots and more on the ability to operationalize models in production, embed them in business processes, and measure value against strategic objectives. Therefore, the opening perspective emphasizes the imperative for executive engagement, cross-functional capability building, and an iterative approach to scaling generative AI initiatives while managing emerging ethical, legal, and operational risks.
A focused analysis of transformative shifts in technology, regulation, workforce skills, and business models driven by generative AI across industries
Recent months have witnessed a cascade of transformative shifts that are redefining the generative AI landscape at pace. First, the rapid maturation of transformer-based and related architectures has expanded the envelope of what models can generate reliably, enabling richer, multimodal applications that combine text, vision, and structured data. Concurrently, supply chain and compute trends have prompted organizations to rethink procurement, favoring hybrid deployment patterns that optimize cost, latency, and data residency. These technical shifts intersect with regulatory developments that are beginning to codify acceptable uses, explainability requirements, and data handling standards, raising the bar for operational compliance and auditability.
Workforce dynamics are changing as well, with demand for ML engineers, prompt engineers, and machine teaching specialists outpacing traditional roles; organizations are responding by reskilling internal talent and forming selective partnerships to complement gaps. Business models are evolving as companies embed generative capabilities into customer touchpoints and automate content workflows, which in turn reshapes value chains and competitive differentiation. Taken together, these shifts require leaders to adopt adaptive governance, invest in modular platforms that support model interchangeability, and cultivate an ecosystem strategy that balances in-house capability building with targeted vendor partnerships.
A rigorous assessment of the cumulative effects of United States tariffs in 2025 on AI supply chains, procurement strategies, and international collaborations
The imposition of United States tariffs in 2025 introduces a new layer of complexity for organizations reliant on global supply chains, specialized hardware, and cross-border services that underpin generative AI deployments. Increased duties on key components can alter procurement calculus for accelerators, data center equipment, and specialized software stacks, prompting organizations to reassess vendor relationships and total cost of ownership. In response, procurement teams are likely to intensify supplier diversification efforts and pursue localized sourcing or assembly options to mitigate exposure to tariff volatility.
Beyond direct procurement impacts, tariffs influence partnership architecture and the economics of cloud versus on-premise deployments. Organizations with stringent latency, privacy, or sovereignty requirements may accelerate investments in local infrastructure or cloud regions, while those seeking to control costs may explore alternative suppliers or optimize workload placement to minimize tariff impacts. These dynamics will also shape negotiation strategies with vendors, where contract clauses and pass-through pricing mechanisms become more salient. In sum, the tariff environment demands dynamic procurement playbooks, scenario planning for supply chain disruptions, and closer collaboration between legal, finance, and technology teams to preserve program momentum while managing incremental costs and compliance obligations.
A strategic breakdown of market segments encompassing components, model architectures, deployment approaches, application domains, and industry vertical dynamics
Understanding segmentation is critical to translating technology capability into business outcomes, and the market should be analyzed across multiple dimensions that reveal deployment patterns, technical trade-offs, and use-case alignment. From a component perspective, organizations differentiate between services and software, where managed services can accelerate time to value while software licenses and platforms support deeper customization and control over model behavior. When considering model types, the landscape spans autoregressive approaches, generative adversarial networks, recurrent neural networks, transformer architectures, and variational autoencoders, each bringing distinct strengths for tasks such as sequence generation, image synthesis, temporal prediction, contextual understanding, and latent representation learning respectively.
Deployment choices further segment activity between cloud-hosted solutions, which offer elasticity and reduced operational overhead, and on-premise installations that deliver tighter control over data residency and latency. Application-wise, generative AI is applied across chatbots and intelligent virtual assistants, content generation, predictive analytics, and robotics and automation, with each application demanding tailored integration, evaluation metrics, and governance controls. Finally, industry verticals-ranging from automotive and transportation to gaming, healthcare, IT and telecommunications, manufacturing, media and entertainment, and retail-exhibit unique regulatory constraints, data characteristics, and value levers that drive differentiated adoption pathways. By synthesizing these segmentation vectors, leaders can more precisely match technical approaches to business objectives and risk appetites, prioritizing investments that yield immediate operational benefits while preserving strategic optionality.
A regional overview of generative AI adoption contrasting regulatory frameworks, talent pools, infrastructure readiness, and strategic positioning across regions
Regional dynamics materially influence the pace and character of generative AI adoption, and comparing geographies provides clarity on regulatory, talent, and infrastructure factors that affect strategic planning. In the Americas, innovation hubs are leveraging robust venture activity and cloud infrastructure to scale early commercial deployments, while regulatory attention emphasizes privacy and consumer protection in selected jurisdictions. This environment encourages rapid product-led growth along with heightened scrutiny on responsible use and transparency.
In Europe, the Middle East and Africa, regulatory frameworks and data protection norms are shaping cautious but deliberate adoption; organizations often emphasize explainability, data minimization, and cross-border data transfer mechanisms, resulting in a preference for solutions that support strong governance and audit trails. Talent mobility and public-private initiatives in the region are advancing localized research and enterprise uptake, particularly in sectors with public interest considerations such as healthcare and public services. The Asia-Pacific region exhibits a mix of aggressive national AI strategies, substantial investment in compute and edge infrastructure, and fast-growing commercial use cases across retail, telecommunications, and manufacturing. Together, these regional profiles suggest that a differentiated go-to-market approach-tailored to regulatory expectations, talent availability, and infrastructure maturity-is essential for organizations seeking to capture value at scale across multiple geographies.
An incisive review of the leaders shaping generative AI through innovation, partnerships, product differentiation, and operational strategies across the ecosystem
Leading companies are shaping the generative AI ecosystem through a combination of platform offerings, model innovation, strategic partnerships, and go-to-market positioning. Market leaders tend to invest in modular platforms that enable rapid experimentation while preserving the ability to swap or fine-tune models as requirements evolve. Others differentiate through verticalized solutions that embed domain knowledge, making deployment faster and outputs more reliable for regulated industries. Partnerships between technology providers, cloud operators, and system integrators are increasingly common, allowing companies to combine specialized IP with global delivery capabilities.
Competitive advantage also arises from operational rigor: firms that establish repeatable model deployment pipelines, robust evaluation frameworks, and clear governance policies accelerate time to value while mitigating risk. Strategic M&A and partnerships play a role in closing capability gaps, especially where unique data assets or industry expertise confer differentiation. Ultimately, the companies that succeed will be those that couple technical innovation with disciplined product management, transparent governance, and pragmatic commercialization strategies that align with buyer needs and regulatory realities.
Actionable guidance for industry leaders to drive responsible adoption, optimize talent and procurement, and fortify ecosystem collaboration in the generative AI era
Industry leaders must pursue a set of practical actions to capture opportunity while managing risk; the first priority is to define a clear value framework that ties generative AI initiatives to measurable business outcomes and accountability. This requires executive sponsorship combined with cross-functional governance that includes legal, compliance, data science, and product teams. Equally important is talent strategy: leaders should invest in reskilling programs, create hybrid teams that blend domain experts with machine learning practitioners, and consider selective outsourcing to accelerate capability gaps.
From a procurement and architecture perspective, organizations should adopt flexible contracts and modular platform designs that support model portability and cost optimization. Responsible adoption should be operationalized through policies for data provenance, model monitoring, explainability, and bias mitigation, supplemented by regular audits and impact assessments. Finally, ecosystem engagement is essential: organizations should cultivate partnerships that provide access to specialized models, domain data, and integration expertise, while participating in industry consortia to inform standards and interoperability. Together, these actions form a pragmatic roadmap for leaders seeking to scale generative AI in a controlled, business-oriented manner.
A concise explanation of research methodology combining targeted qualitative interviews, primary data collection, secondary synthesis, and analytical frameworks
The research approach combines qualitative and quantitative methods to produce rigorous, decision-oriented findings. Primary inputs include structured interviews with senior technology and business leaders, product and engineering practitioners, procurement specialists, and relevant policy experts to capture diverse perspectives on operational challenges and strategic priorities. Secondary synthesis draws on peer-reviewed literature, industry white papers, conference proceedings, and vendor documentation to contextualize primary insights and identify emergent patterns across sectors.
Analytical frameworks are applied to assess technology readiness, governance maturity, and deployment risks, enabling a consistent comparison across companies and use cases. Case studies illustrate practical deployment scenarios, highlighting implementation choices, success factors, and lessons learned. Throughout the process, triangulation is used to validate findings, combining multiple sources and methods to reduce bias and increase confidence in the conclusions. Transparency in methodology and source attribution ensures that conclusions are reproducible and actionable for decision-makers who require evidence-based guidance for strategy and procurement.
A concise and conclusive closing synthesis that underscores long-term strategic priorities, ethical guardrails, and the role of leadership in AI adoption
In closing, organizations that approach generative AI with strategic clarity, disciplined execution, and strong governance will unlock durable advantages while minimizing downside exposure. Long-term priorities include embedding model management into standard IT and DevOps practices, prioritizing data quality and lineage, and developing transparent evaluation metrics that align with business goals. Ethical guardrails and regulatory compliance must be viewed as design constraints rather than afterthoughts, and leadership should communicate clear principles and accountability mechanisms to maintain trust among stakeholders.
The collective picture suggests that AI adoption will continue to be uneven across sectors and regions, but the underlying drivers-technical progress, ecosystem maturation, and regulatory evolution-are converging to make generative capabilities an operational imperative. Leaders who balance rapid experimentation with enterprise-grade controls, invest in talent and partnerships, and maintain flexible, modular architectures will be best positioned to convert technological potential into sustained business value. This synthesis underscores the need for integrated strategies that align technology choices with organizational structures, incentives, and risk appetite.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
194 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Segmentation & Coverage
- 1.3. Years Considered for the Study
- 1.4. Currency
- 1.5. Language
- 1.6. Stakeholders
- 2. Research Methodology
- 3. Executive Summary
- 4. Market Overview
- 5. Market Insights
- 5.1. Rapid commercialization of foundation models through turnkey API solutions transforming enterprise workflows
- 5.2. Integration of generative AI with augmented reality and virtual assistants for immersive customer experiences
- 5.3. Expansion of synthetic data generation to enhance privacy compliance and accelerate machine learning training
- 5.4. Evolution of AI governance frameworks addressing ethical bias and transparency in content generation
- 5.5. Proliferation of domain specific generative AI agents automating specialized tasks in healthcare and finance
- 5.6. Advancements in real time video synthesis enabling live personalized advertising and interactive media
- 5.7. Growth of open source generative AI ecosystems fostering collaborative model development and community innovation
- 5.8. Adoption of low code generative AI platforms empowering non technical users to create custom applications
- 5.9. Deployment of energy efficient model architectures reducing carbon footprint in large scale AI operations
- 5.10. Strategic partnerships between cloud providers and AI startups accelerating scalable generative model hosting
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. Generative AI Market, by Component
- 8.1. Service
- 8.2. Software
- 9. Generative AI Market, by Type
- 9.1. Autoregressive Models
- 9.2. Generative Adversarial Networks (GANs)
- 9.3. Recurrent Neural Networks (RNNs)
- 9.4. Transformer Models
- 9.5. Variational Autoencoders (VAEs)
- 10. Generative AI Market, by Deployment Models
- 10.1. Cloud Hosted
- 10.2. On-Premise
- 11. Generative AI Market, by Application
- 11.1. Chatbots & Intelligent Virtual Assistants
- 11.2. Content Generation
- 11.3. Predictive Analytics
- 11.4. Robotics & Automation
- 12. Generative AI Market, by Industry Vertical
- 12.1. Automotive & Transportation
- 12.2. Gaming
- 12.3. Healthcare
- 12.4. IT & Telecommunication
- 12.5. Manufacturing
- 12.6. Media & Entertainment
- 12.7. Retail
- 13. Generative AI Market, by Region
- 13.1. Americas
- 13.1.1. North America
- 13.1.2. Latin America
- 13.2. Europe, Middle East & Africa
- 13.2.1. Europe
- 13.2.2. Middle East
- 13.2.3. Africa
- 13.3. Asia-Pacific
- 14. Generative AI Market, by Group
- 14.1. ASEAN
- 14.2. GCC
- 14.3. European Union
- 14.4. BRICS
- 14.5. G7
- 14.6. NATO
- 15. Generative AI Market, by Country
- 15.1. United States
- 15.2. Canada
- 15.3. Mexico
- 15.4. Brazil
- 15.5. United Kingdom
- 15.6. Germany
- 15.7. France
- 15.8. Russia
- 15.9. Italy
- 15.10. Spain
- 15.11. China
- 15.12. India
- 15.13. Japan
- 15.14. Australia
- 15.15. South Korea
- 16. Competitive Landscape
- 16.1. Market Share Analysis, 2024
- 16.2. FPNV Positioning Matrix, 2024
- 16.3. Competitive Analysis
- 16.3.1. Accenture PLC
- 16.3.2. OpenAI OpCo, LLC
- 16.3.3. AI21 Labs Ltd.
- 16.3.4. Amazon Web Services, Inc.
- 16.3.5. Anthropic PBC
- 16.3.6. AssemblyAI, Inc.
- 16.3.7. Clarifai, Inc.
- 16.3.8. Databricks, Inc.
- 16.3.9. DeepL SE
- 16.3.10. Dialpad, Inc.
- 16.3.11. Genie AI Ltd.
- 16.3.12. Google LLC by Alphabet Inc.
- 16.3.13. Infosys Limited
- 16.3.14. International Business Machines Corporation
- 16.3.15. Lighttricks
- 16.3.16. Markovate, Inc.
- 16.3.17. Meta Platforms, Inc.
- 16.3.18. Microsoft Corporation
- 16.3.19. Midjourney, Inc.
- 16.3.20. Mostly AI Solutions MP GmbH
- 16.3.21. NTT DOCOMO, INC
- 16.3.22. Paige AI, Inc.
- 16.3.23. Persado, Inc.
- 16.3.24. Revery AI Inc.
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