AI Agents Market by Agent System (Multi Agent System, Single Agent System), Type (Build-Your-Own Agents, Ready-to-Deploy Agents), Interaction Mode, Technology, Deployment Type, Enterprise Size, Industry - Global Forecast 2026-2032
Description
The AI Agents Market was valued at USD 7.12 billion in 2025 and is projected to grow to USD 8.81 billion in 2026, with a CAGR of 24.95%, reaching USD 33.89 billion by 2032.
Contextual framing of AI agent advancements and organizational readiness to translate technological capability into strategic operational advantage
The evolution of artificial intelligence agents is redefining how organizations automate complex workflows, personalize interactions, and augment human expertise across operations. This executive summary frames the strategic contours of that change, synthesizing technological maturation, enterprise adoption patterns, and governance maturity into a coherent narrative intended for senior decision-makers.
Beginning with capability sets and delivery modalities, the analysis traces how agent architectures and interaction modes are shaping integration approaches across cloud and on-premise deployments. It highlights the interplay between technical infrastructure and organizational processes, emphasizing the need for alignment among data strategy, security posture, and talent frameworks. Importantly, this introduction establishes the lens used across the report: a pragmatic balance between innovation potential and operational readiness, with an emphasis on where leaders should prioritize investment and risk mitigation to achieve sustainable advantage.
How architectural, interaction and deployment shifts in AI agents are reshaping enterprise operating models and vendor engagement dynamics
AI agents are driving a set of transformative shifts that reconfigure industry boundaries, operational models, and competitive dynamics. Architecturally, multi-agent collaboration patterns are emerging alongside single-agent deployments, enabling distributed decisioning and complex task orchestration. At the same time, the distinction between build-your-own agent strategies and ready-to-deploy solutions is influencing procurement cycles, vendor relationships, and the balance between customization and speed-to-deployment.
Interaction paradigms are also evolving: background agents are increasingly used for continuous automation and monitoring, while surface agents deliver customer-facing experiences that require robust conversational and contextual capabilities. Advances in core technologies-computer vision, deep learning, traditional machine learning, and natural language processing-are expanding the range of viable use cases, and hybrid deployment models across cloud and on-premise environments are enabling both scale and security. These shifts collectively demand new governance models, updated skills portfolios, and cross-functional operating models to realize the full potential of agent-driven automation.
Assessment of how recent United States tariff adjustments are cumulatively altering procurement strategies, supply chain resilience and deployment architecture decisions
Recent trade policy actions and tariff adjustments originating from the United States have introduced a layer of macroeconomic and operational complexity for organizations deploying AI agents. Supply chain recalibrations, changes in component sourcing, and variations in the cost base for hardware-dependent systems alter procurement strategies and the comparative economics of cloud versus on-premise deployments.
Beyond direct procurement effects, tariff dynamics influence partner selection and the regional distribution of development and operational resources. Organizations are increasingly factoring import cost variability into vendor evaluation, contract terms, and contingency planning. This environment has also accelerated interest in localizing critical development and testing capabilities to mitigate exposure to cross-border frictions. Ultimately, the cumulative impact has shifted the calculus for technology architecture decisions, with greater attention paid to modularity, vendor diversification, and the resilience of the broader supply ecosystem.
Integrated segmentation insights revealing how agent system, type, interaction mode, technology, deployment, enterprise scale, and industry-specific nuances drive differentiated adoption paths
Segmentation analysis reveals distinct demand drivers and operational requirements across agent architectures, deployment approaches, interaction modes, technology stacks, enterprise sizes, and industry verticals. Based on agent system distinctions, the landscape separates into Multi Agent System and Single Agent System deployments, where multi-agent architectures favor orchestration and emergent coordination while single-agent implementations emphasize focused task automation and lower integration overhead. Based on type, organizations choose between Build-Your-Own Agents and Ready-to-Deploy Agents, with the former offering deep customization and the latter delivering rapid time-to-value.
Based on interaction mode, solutions are categorized into Background Agents and Surface Agents; background agents prioritize continuous back-office automation and monitoring whereas surface agents prioritize user-facing conversational or multimodal experiences. Based on technology, the agent ecosystem leverages Computer Vision, Deep Learning, Machine Learning, and Natural Language Processing, each contributing distinct capabilities and data requirements. Based on deployment type, choices between Cloud and On-Premise reflect trade-offs in scalability, latency, and data residency. Based on enterprise size, Large Enterprises and Small & Medium Enterprises show differing risk tolerances, integration complexity, and procurement cadence. Based on industry, adoption patterns differ across Automotive, Banking, Financial Services & Insurance, Healthcare & Lifesciences, IT & Telecommunication, Media & Entertainment, and Retail & E-Commerce, with Healthcare & Lifesciences further segmented into Hospitals & Clinics, Pharmaceuticals & Drug Discovery, and Telemedicine, and Media & Entertainment further segmented into Film & Television, Gaming & eSports, and Music & Streaming Services, each vertical bringing specific regulatory, latency, and data-sensitivity constraints that shape solution requirements.
Regional dynamics and geopolitical considerations shaping differentiated AI agent adoption patterns and go-to-market strategies across major world regions
Regional dynamics create divergent opportunity spaces and operational constraints that influence how organizations prioritize agent capabilities and governance. In the Americas, a strong focus on enterprise-scale automation, cloud-native deployments, and regulatory scrutiny around data privacy shapes procurement and integration priorities, driving investments in vendor partnerships and in-house engineering talent to sustain competitive differentiation. In Europe, Middle East & Africa, organizations balance regulatory compliance, sovereignty concerns, and localized innovation ecosystems, leading to hybrid deployment approaches and heightened emphasis on explainability and privacy-preserving techniques. In Asia-Pacific, accelerated adoption cycles, diverse market maturity levels, and concentrated talent hubs drive experimentation with both consumer-facing surface agents and industrial automation use cases, often favoring rapid iterative deployments and vendor ecosystems that support regional language and cultural contexts.
Across these regions, differences in infrastructure maturity, procurement models, and regulatory frameworks result in distinct go-to-market approaches for solution providers. Regional strategies must therefore account for localized compliance requirements, partner ecosystems, and operational latency considerations to ensure both technical feasibility and commercial viability.
Competitive company characteristics and strategic differentiators that determine leadership in the AI agents ecosystem and enterprise procurement decisions
Leading companies operating in the AI agents landscape are distinguished by their ability to integrate core model capabilities with robust engineering, clear governance frameworks, and extensive partner ecosystems. Key firms demonstrate strengths in modular architectures that allow rapid customization, comprehensive developer tooling that accelerates integration, and committed investments in safety, explainability, and lifecycle management practices. In addition, firms that balance proprietary model innovation with accessible APIs and enterprise-grade deployment options often secure broader market traction across both large enterprises and smaller organizations seeking turnkey solutions.
Partnership strategies, ecosystem development, and service capabilities differentiate competitive positioning, with successful players offering strong professional services, integration support, and co-innovation pathways. Companies that emphasize interoperability, standardized security controls, and transparent operating models build higher trust with regulated industries and long-cycle procurement processes. Finally, sustained investment in domain-specific adapters and prebuilt workflows tailored to key verticals enhances the immediate applicability of solutions and shortens time-to-value for enterprise clients.
Actionable enterprise recommendations to scale AI agents securely, align projects to business outcomes, and strengthen operational resilience across deployments
Industry leaders should move from exploratory pilots to structured programs that scale agent deployment while embedding strong governance and change management. Establish clear objectives for each initiative that align with measurable business outcomes, and create modular roadmaps that allow rapid iteration without compromising security or compliance. Prioritize interoperability and open standards to avoid vendor lock-in, and design procurements to include service-level expectations for model performance, update cadence, and incident response.
Invest in cross-functional capability building that pairs domain experts with ML engineering, data ops, and security teams to ensure solutions are operationally sustainable. Adopt a risk-first approach to sensitive data handling by implementing privacy-enhancing techniques and continuous monitoring of model behavior. Finally, incorporate supplier diversification and contingency plans into sourcing strategies to maintain continuity in the face of supply chain or policy disruptions, and allocate resources for post-deployment observability to continuously optimize agent behavior and user experience.
Methodological approach combining primary interviews, technical reviews, and case study triangulation to produce rigorous actionable insights for enterprise decision-makers
This research synthesizes primary stakeholder interviews, qualitative case analyses, and secondary technical literature to build an evidence-based narrative on agent adoption and operationalization. Primary inputs include structured interviews with technology leaders, architects, and procurement professionals to surface adoption barriers, success factors, and integration patterns. These qualitative insights are complemented by technical reviews of architectural trends, algorithmic capabilities, and deployment modalities to ensure the analysis captures both strategic and engineering perspectives.
The methodology triangulates these inputs through thematic coding and cross-validation to identify recurring patterns and divergent practices. Where appropriate, case studies are used to illustrate practical implementation approaches, governance constructs, and measurable operational outcomes. Throughout, care is taken to contextualize findings within prevailing regulatory environments and supply chain conditions, ensuring recommendations are actionable for organizations at varying stages of maturity.
Strategic conclusion emphasizing the imperative for modular architectures, governance, and cross-functional capability building to operationalize AI agents successfully
The collective evidence underscores that AI agents are transitioning from experimental pilots to strategic operational capabilities, but realizing that transition requires deliberate architecture, governance, and talent investments. Organizations that adopt modular design principles, emphasize interoperability, and deploy clear governance constructs will be better positioned to capture business value while managing risk. Moreover, regional and industry-specific constraints necessitate tailored approaches rather than one-size-fits-all solutions, and supply chain or policy disruptions further highlight the importance of vendor diversification and localized capability development.
Moving forward, leaders must prioritize building observability into their agent lifecycles, invest in cross-functional teams to maintain operational resilience, and align initiatives with measurable business outcomes. By doing so, enterprises can shift from ad hoc experimentation to systematic capability building that scales securely and sustainably across functions and geographies.
Note: PDF & Excel + Online Access - 1 Year
Contextual framing of AI agent advancements and organizational readiness to translate technological capability into strategic operational advantage
The evolution of artificial intelligence agents is redefining how organizations automate complex workflows, personalize interactions, and augment human expertise across operations. This executive summary frames the strategic contours of that change, synthesizing technological maturation, enterprise adoption patterns, and governance maturity into a coherent narrative intended for senior decision-makers.
Beginning with capability sets and delivery modalities, the analysis traces how agent architectures and interaction modes are shaping integration approaches across cloud and on-premise deployments. It highlights the interplay between technical infrastructure and organizational processes, emphasizing the need for alignment among data strategy, security posture, and talent frameworks. Importantly, this introduction establishes the lens used across the report: a pragmatic balance between innovation potential and operational readiness, with an emphasis on where leaders should prioritize investment and risk mitigation to achieve sustainable advantage.
How architectural, interaction and deployment shifts in AI agents are reshaping enterprise operating models and vendor engagement dynamics
AI agents are driving a set of transformative shifts that reconfigure industry boundaries, operational models, and competitive dynamics. Architecturally, multi-agent collaboration patterns are emerging alongside single-agent deployments, enabling distributed decisioning and complex task orchestration. At the same time, the distinction between build-your-own agent strategies and ready-to-deploy solutions is influencing procurement cycles, vendor relationships, and the balance between customization and speed-to-deployment.
Interaction paradigms are also evolving: background agents are increasingly used for continuous automation and monitoring, while surface agents deliver customer-facing experiences that require robust conversational and contextual capabilities. Advances in core technologies-computer vision, deep learning, traditional machine learning, and natural language processing-are expanding the range of viable use cases, and hybrid deployment models across cloud and on-premise environments are enabling both scale and security. These shifts collectively demand new governance models, updated skills portfolios, and cross-functional operating models to realize the full potential of agent-driven automation.
Assessment of how recent United States tariff adjustments are cumulatively altering procurement strategies, supply chain resilience and deployment architecture decisions
Recent trade policy actions and tariff adjustments originating from the United States have introduced a layer of macroeconomic and operational complexity for organizations deploying AI agents. Supply chain recalibrations, changes in component sourcing, and variations in the cost base for hardware-dependent systems alter procurement strategies and the comparative economics of cloud versus on-premise deployments.
Beyond direct procurement effects, tariff dynamics influence partner selection and the regional distribution of development and operational resources. Organizations are increasingly factoring import cost variability into vendor evaluation, contract terms, and contingency planning. This environment has also accelerated interest in localizing critical development and testing capabilities to mitigate exposure to cross-border frictions. Ultimately, the cumulative impact has shifted the calculus for technology architecture decisions, with greater attention paid to modularity, vendor diversification, and the resilience of the broader supply ecosystem.
Integrated segmentation insights revealing how agent system, type, interaction mode, technology, deployment, enterprise scale, and industry-specific nuances drive differentiated adoption paths
Segmentation analysis reveals distinct demand drivers and operational requirements across agent architectures, deployment approaches, interaction modes, technology stacks, enterprise sizes, and industry verticals. Based on agent system distinctions, the landscape separates into Multi Agent System and Single Agent System deployments, where multi-agent architectures favor orchestration and emergent coordination while single-agent implementations emphasize focused task automation and lower integration overhead. Based on type, organizations choose between Build-Your-Own Agents and Ready-to-Deploy Agents, with the former offering deep customization and the latter delivering rapid time-to-value.
Based on interaction mode, solutions are categorized into Background Agents and Surface Agents; background agents prioritize continuous back-office automation and monitoring whereas surface agents prioritize user-facing conversational or multimodal experiences. Based on technology, the agent ecosystem leverages Computer Vision, Deep Learning, Machine Learning, and Natural Language Processing, each contributing distinct capabilities and data requirements. Based on deployment type, choices between Cloud and On-Premise reflect trade-offs in scalability, latency, and data residency. Based on enterprise size, Large Enterprises and Small & Medium Enterprises show differing risk tolerances, integration complexity, and procurement cadence. Based on industry, adoption patterns differ across Automotive, Banking, Financial Services & Insurance, Healthcare & Lifesciences, IT & Telecommunication, Media & Entertainment, and Retail & E-Commerce, with Healthcare & Lifesciences further segmented into Hospitals & Clinics, Pharmaceuticals & Drug Discovery, and Telemedicine, and Media & Entertainment further segmented into Film & Television, Gaming & eSports, and Music & Streaming Services, each vertical bringing specific regulatory, latency, and data-sensitivity constraints that shape solution requirements.
Regional dynamics and geopolitical considerations shaping differentiated AI agent adoption patterns and go-to-market strategies across major world regions
Regional dynamics create divergent opportunity spaces and operational constraints that influence how organizations prioritize agent capabilities and governance. In the Americas, a strong focus on enterprise-scale automation, cloud-native deployments, and regulatory scrutiny around data privacy shapes procurement and integration priorities, driving investments in vendor partnerships and in-house engineering talent to sustain competitive differentiation. In Europe, Middle East & Africa, organizations balance regulatory compliance, sovereignty concerns, and localized innovation ecosystems, leading to hybrid deployment approaches and heightened emphasis on explainability and privacy-preserving techniques. In Asia-Pacific, accelerated adoption cycles, diverse market maturity levels, and concentrated talent hubs drive experimentation with both consumer-facing surface agents and industrial automation use cases, often favoring rapid iterative deployments and vendor ecosystems that support regional language and cultural contexts.
Across these regions, differences in infrastructure maturity, procurement models, and regulatory frameworks result in distinct go-to-market approaches for solution providers. Regional strategies must therefore account for localized compliance requirements, partner ecosystems, and operational latency considerations to ensure both technical feasibility and commercial viability.
Competitive company characteristics and strategic differentiators that determine leadership in the AI agents ecosystem and enterprise procurement decisions
Leading companies operating in the AI agents landscape are distinguished by their ability to integrate core model capabilities with robust engineering, clear governance frameworks, and extensive partner ecosystems. Key firms demonstrate strengths in modular architectures that allow rapid customization, comprehensive developer tooling that accelerates integration, and committed investments in safety, explainability, and lifecycle management practices. In addition, firms that balance proprietary model innovation with accessible APIs and enterprise-grade deployment options often secure broader market traction across both large enterprises and smaller organizations seeking turnkey solutions.
Partnership strategies, ecosystem development, and service capabilities differentiate competitive positioning, with successful players offering strong professional services, integration support, and co-innovation pathways. Companies that emphasize interoperability, standardized security controls, and transparent operating models build higher trust with regulated industries and long-cycle procurement processes. Finally, sustained investment in domain-specific adapters and prebuilt workflows tailored to key verticals enhances the immediate applicability of solutions and shortens time-to-value for enterprise clients.
Actionable enterprise recommendations to scale AI agents securely, align projects to business outcomes, and strengthen operational resilience across deployments
Industry leaders should move from exploratory pilots to structured programs that scale agent deployment while embedding strong governance and change management. Establish clear objectives for each initiative that align with measurable business outcomes, and create modular roadmaps that allow rapid iteration without compromising security or compliance. Prioritize interoperability and open standards to avoid vendor lock-in, and design procurements to include service-level expectations for model performance, update cadence, and incident response.
Invest in cross-functional capability building that pairs domain experts with ML engineering, data ops, and security teams to ensure solutions are operationally sustainable. Adopt a risk-first approach to sensitive data handling by implementing privacy-enhancing techniques and continuous monitoring of model behavior. Finally, incorporate supplier diversification and contingency plans into sourcing strategies to maintain continuity in the face of supply chain or policy disruptions, and allocate resources for post-deployment observability to continuously optimize agent behavior and user experience.
Methodological approach combining primary interviews, technical reviews, and case study triangulation to produce rigorous actionable insights for enterprise decision-makers
This research synthesizes primary stakeholder interviews, qualitative case analyses, and secondary technical literature to build an evidence-based narrative on agent adoption and operationalization. Primary inputs include structured interviews with technology leaders, architects, and procurement professionals to surface adoption barriers, success factors, and integration patterns. These qualitative insights are complemented by technical reviews of architectural trends, algorithmic capabilities, and deployment modalities to ensure the analysis captures both strategic and engineering perspectives.
The methodology triangulates these inputs through thematic coding and cross-validation to identify recurring patterns and divergent practices. Where appropriate, case studies are used to illustrate practical implementation approaches, governance constructs, and measurable operational outcomes. Throughout, care is taken to contextualize findings within prevailing regulatory environments and supply chain conditions, ensuring recommendations are actionable for organizations at varying stages of maturity.
Strategic conclusion emphasizing the imperative for modular architectures, governance, and cross-functional capability building to operationalize AI agents successfully
The collective evidence underscores that AI agents are transitioning from experimental pilots to strategic operational capabilities, but realizing that transition requires deliberate architecture, governance, and talent investments. Organizations that adopt modular design principles, emphasize interoperability, and deploy clear governance constructs will be better positioned to capture business value while managing risk. Moreover, regional and industry-specific constraints necessitate tailored approaches rather than one-size-fits-all solutions, and supply chain or policy disruptions further highlight the importance of vendor diversification and localized capability development.
Moving forward, leaders must prioritize building observability into their agent lifecycles, invest in cross-functional teams to maintain operational resilience, and align initiatives with measurable business outcomes. By doing so, enterprises can shift from ad hoc experimentation to systematic capability building that scales securely and sustainably across functions and geographies.
Note: PDF & Excel + Online Access - 1 Year
Table of Contents
196 Pages
- 1. Preface
- 1.1. Objectives of the Study
- 1.2. Market Definition
- 1.3. Market Segmentation & Coverage
- 1.4. Years Considered for the Study
- 1.5. Currency Considered for the Study
- 1.6. Language Considered for the Study
- 1.7. Key Stakeholders
- 2. Research Methodology
- 2.1. Introduction
- 2.2. Research Design
- 2.2.1. Primary Research
- 2.2.2. Secondary Research
- 2.3. Research Framework
- 2.3.1. Qualitative Analysis
- 2.3.2. Quantitative Analysis
- 2.4. Market Size Estimation
- 2.4.1. Top-Down Approach
- 2.4.2. Bottom-Up Approach
- 2.5. Data Triangulation
- 2.6. Research Outcomes
- 2.7. Research Assumptions
- 2.8. Research Limitations
- 3. Executive Summary
- 3.1. Introduction
- 3.2. CXO Perspective
- 3.3. Market Size & Growth Trends
- 3.4. Market Share Analysis, 2025
- 3.5. FPNV Positioning Matrix, 2025
- 3.6. New Revenue Opportunities
- 3.7. Next-Generation Business Models
- 3.8. Industry Roadmap
- 4. Market Overview
- 4.1. Introduction
- 4.2. Industry Ecosystem & Value Chain Analysis
- 4.2.1. Supply-Side Analysis
- 4.2.2. Demand-Side Analysis
- 4.2.3. Stakeholder Analysis
- 4.3. Porter’s Five Forces Analysis
- 4.4. PESTLE Analysis
- 4.5. Market Outlook
- 4.5.1. Near-Term Market Outlook (0–2 Years)
- 4.5.2. Medium-Term Market Outlook (3–5 Years)
- 4.5.3. Long-Term Market Outlook (5–10 Years)
- 4.6. Go-to-Market Strategy
- 5. Market Insights
- 5.1. Consumer Insights & End-User Perspective
- 5.2. Consumer Experience Benchmarking
- 5.3. Opportunity Mapping
- 5.4. Distribution Channel Analysis
- 5.5. Pricing Trend Analysis
- 5.6. Regulatory Compliance & Standards Framework
- 5.7. ESG & Sustainability Analysis
- 5.8. Disruption & Risk Scenarios
- 5.9. Return on Investment & Cost-Benefit Analysis
- 6. Cumulative Impact of United States Tariffs 2025
- 7. Cumulative Impact of Artificial Intelligence 2025
- 8. AI Agents Market, by Agent System
- 8.1. Multi Agent System
- 8.2. Single Agent System
- 9. AI Agents Market, by Type
- 9.1. Build-Your-Own Agents
- 9.2. Ready-to-Deploy Agents
- 10. AI Agents Market, by Interaction Mode
- 10.1. Background Agents
- 10.2. Surface Agents
- 11. AI Agents Market, by Technology
- 11.1. Computer Vision
- 11.2. Deep Learning
- 11.3. Machine Learning
- 11.4. Natural Language Processing (NLP)
- 12. AI Agents Market, by Deployment Type
- 12.1. Cloud
- 12.2. On-Premise
- 13. AI Agents Market, by Enterprise Size
- 13.1. Large Enterprises
- 13.2. Small & Medium Enterprises
- 14. AI Agents Market, by Industry
- 14.1. Automotive
- 14.2. Banking, Financial Services, & Insurance
- 14.3. Healthcare & Lifesciences
- 14.3.1. Hospitals & Clinics
- 14.3.2. Pharmaceuticals & Drug Discovery
- 14.3.3. Telemedicine
- 14.4. IT & Telecommunication
- 14.5. Media & Entertainment
- 14.5.1. Film & Television
- 14.5.2. Gaming & eSports
- 14.5.3. Music & Streaming Services
- 14.6. Retail & E-Commerce
- 15. AI Agents Market, by Region
- 15.1. Americas
- 15.1.1. North America
- 15.1.2. Latin America
- 15.2. Europe, Middle East & Africa
- 15.2.1. Europe
- 15.2.2. Middle East
- 15.2.3. Africa
- 15.3. Asia-Pacific
- 16. AI Agents Market, by Group
- 16.1. ASEAN
- 16.2. GCC
- 16.3. European Union
- 16.4. BRICS
- 16.5. G7
- 16.6. NATO
- 17. AI Agents Market, by Country
- 17.1. United States
- 17.2. Canada
- 17.3. Mexico
- 17.4. Brazil
- 17.5. United Kingdom
- 17.6. Germany
- 17.7. France
- 17.8. Russia
- 17.9. Italy
- 17.10. Spain
- 17.11. China
- 17.12. India
- 17.13. Japan
- 17.14. Australia
- 17.15. South Korea
- 18. United States AI Agents Market
- 19. China AI Agents Market
- 20. Competitive Landscape
- 20.1. Market Concentration Analysis, 2025
- 20.1.1. Concentration Ratio (CR)
- 20.1.2. Herfindahl Hirschman Index (HHI)
- 20.2. Recent Developments & Impact Analysis, 2025
- 20.3. Product Portfolio Analysis, 2025
- 20.4. Benchmarking Analysis, 2025
- 20.5. Alibaba Group Holding Limited
- 20.6. Amazon.com, Inc.
- 20.7. Amelia US LLC
- 20.8. Apple Inc.
- 20.9. AVA Labs, Inc.
- 20.10. Baidu, Inc.
- 20.11. Clinc, Inc.
- 20.12. Cognizant Technology Solutions Corporation
- 20.13. Engageware
- 20.14. Google LLC by Alphabet Inc.
- 20.15. Haptik Infotech Pvt. Ltd. by Reliance Industries Limited
- 20.16. IBM Corporation
- 20.17. Inbenta Technologies Inc.
- 20.18. Interactions LLC
- 20.19. Kasisto, Inc.
- 20.20. Kore.ai, Inc.
- 20.21. LINE Corporation
- 20.22. LivePerson, Inc.
- 20.23. Meta Platforms, Inc.
- 20.24. Microsoft Corporation
- 20.25. Nuance Communications, Inc.
- 20.26. OpenAI Inc.
- 20.27. Oracle Corporation
- 20.28. Pega Systems Inc.
- 20.29. Rasa Technologies GmbH
- 20.30. Samsung Electronics Co., Ltd.
- 20.31. SAP SE
- 20.32. SK Telecom Co., Ltd.
- 20.33. SoundHound, Inc.
- 20.34. Uber Technologies, Inc.
- 20.35. Verint Systems Inc.
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