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Global AI Market Projections and the Rise of Agentic AI in 2025 and Beyond

General Report June 5, 2025
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TABLE OF CONTENTS

  1. Global AI Market Growth Outlook
  2. Sector-Specific AI Market Projections
  3. Emergence of Agentic AI in the Enterprise
  4. Cybersecurity Implications of Agentic AI
  5. Challenges and Considerations for Scaling AI
  6. Key Industry Players and Strategic Movements
  7. Conclusion

1. Summary

  • As of June 5, 2025, the global artificial intelligence (AI) market is experiencing unparalleled growth, with projections indicating an increase from an estimated USD 371.7 billion in 2025 to multi-trillion-dollar valuations by the early 2030s. This robust expansion is underpinned by dramatic advancements in generative and agentic AI, with essential sectors including healthcare, customer service, banking & finance, and aerospace & defense navigating their paths towards transformative adoption. A notable aspect of this growth is the compound annual growth rate (CAGR), which varies significantly across different forecasts—from a striking 30.6% indicated by MarketsandMarkets™ to a more conservative 19.20% anticipated in an alternate projection. Such divergences highlight both the rapid but potentially volatile trajectory that the AI market is likely to undergo, as adoption rates are influenced by factors including regulatory environments and sector-specific demand. The diverse applications of AI present a multifaceted landscape for stakeholders. For customer service, AI is set to grow from USD 12.06 billion in 2024 to approximately USD 47.82 billion by 2030, driven by the integration of AI-powered chatbots and virtual assistants enhancing operational efficiency. Similarly, in banking and finance, generative AI's expected leap from USD 1.29 billion in 2024 to USD 21.57 billion by 2034 is largely attributed to the need for improved data security and fraud detection mechanisms. Healthcare's AI market is projected to soar from USD 36.96 billion in 2025 to USD 613.81 billion by 2034, propelled by the growing volume of healthcare data and a push towards personalized medicine. Moreover, the aerospace and defense sectors are integrating AI technologies for operational efficiency and enhanced security measures—demonstrating widespread confidence in AI's transformative potential. However, this landscape is not without its challenges. Enterprises recognize looming obstacles such as talent shortages, regulatory scrutiny, and the pressing need for robust cybersecurity measures. Issues surrounding data infrastructures, adherence to regulatory frameworks, and the risks posed by the emergence of agentic AI necessitate a well-thought-out approach by organizations in harnessing AI's capabilities. The integration of agentic AI, particularly, demands a mindful balance between innovation and the associated risks, as decision-making autonomy and contextual adaptability introduce complex new dimensions to cybersecurity and operational governance.

2. Global AI Market Growth Outlook

  • 2-1. Overall Market Size and Growth Trajectory

  • As of June 5, 2025, the global artificial intelligence (AI) market is poised for tremendous growth, driven by advancements in technology and increasing demand across various sectors. According to a recent report by MarketsandMarkets™ published on May 27, 2025, the market is projected to escalate from an estimated USD 371.71 billion in 2025 to approximately USD 2, 407.02 billion by 2032, reflecting a compound annual growth rate (CAGR) of 30.6%. This surge is attributed to several factors, including the widespread adoption of autonomous AI systems, enhancements in deep learning capabilities, and improved computing resources that facilitate large-scale data processing. Furthermore, in another projection reported on June 1, 2025, the AI market is expected to expand at a steadier pace, achieving a valuation of around USD 3, 680.47 billion by 2034, with a CAGR of 19.20% from the year 2025. This indicates a substantial bifurcation in predictions, emphasizing a rapid yet volatile growth trajectory for AI technologies, likely influenced by varying sector-specific adoption rates and regulatory environments across regions. Key segments driving this growth include software solutions, particularly in automation and analytics, and services linked to AI implementation and consulting. The trends show a clear shift towards integrating AI into real-time applications, especially in manufacturing, healthcare, and client-facing industries.

  • 2-2. Comparative Forecasts through 2032 and 2034

  • Comparative forecasts illustrate significant variability in predicted growth across different sources and timelines. The MarketsandMarkets™ report outlines a substantial near-term growth, estimating that by 2032, the AI market could realize a valuation of up to USD 2, 407.02 billion, showcasing a aggressive CAGR of 30.6%. Conversely, the secondary report anticipates a more tempered, sustainable growth model, culminating in an estimated worth of USD 3, 680.47 billion by 2034, with a lesser CAGR of 19.20% over a longer horizon. Analyzing these forecasts sheds light on the differing perspectives regarding market dynamics. The high-growth scenario posited by MarketsandMarkets indicates a rapid influx of AI applications in sectors such as automation, AI-as-a-Service (AIaaS), and innovative applications within deep learning and neural networks, particularly as organizations seek real-time, intelligent solutions to stay competitive. On the other hand, the slower growth expectation suggests a more cautious, evaluated approach to AI adoption, highlighting potential regulatory challenges and the need for industries to align their learning curves with rapidly evolving technology landscapes. As companies increasingly strive to integrate AI within their infrastructure, strategic investments, adequate regulatory compliance, and workforce reskilling will be pivotal to steering favorable results in either scenario.

3. Sector-Specific AI Market Projections

  • 3-1. AI in Customer Service and Workflow Automation

  • As of June 2025, the AI for customer service market is poised for significant expansion, projected to grow from USD 12.06 billion in 2024 to approximately USD 47.82 billion by 2030, representing a remarkable compound annual growth rate (CAGR) of 25.8%. This growth is attributed to the increasing adoption of AI-powered chatbots and virtual assistants, which are transforming customer support by enhancing operational efficiency and providing personalized customer experiences. Major drivers of this growth include improved customer engagement through omnichannel self-service options and the optimization of agent efficiency through AI integration. However, the industry faces challenges, such as the need to address potential job displacements and mitigate threats posed by deepfake technology. The Asia Pacific region is expected to witness the highest growth rate, fueled by rapid technology adoption and robust consumer demand for improved services.

  • 3-2. Generative AI in Banking & Finance

  • The market for generative AI in the banking and finance sector is set to experience significant growth, projected to leap from USD 1.29 billion in 2024 to approximately USD 21.57 billion by 2034, at a remarkable CAGR of 31.64%. This growth trajectory is driven by heightened demand for data security, personalized financial services, and advanced fraud detection mechanisms. Key factors contributing to this upward trend include economic expansion and digital transformation initiatives across the industry. Despite existing challenges, such as the shortage of skilled AI professionals, the market is expected to thrive, particularly in natural language processing and cloud-based deployment models, which are projected to dominate the landscape. North America currently leads in market share, though South America and Africa are anticipated to emerge as the fastest-growing regions.

  • 3-3. AI Applications in Healthcare

  • The artificial intelligence market in healthcare is on a trajectory towards exceptional growth, with projections indicating a rise from USD 36.96 billion in 2025 to around USD 613.81 billion by 2034, presenting a CAGR of 38.5%. This remarkable expansion is fueled by the increasing volume of healthcare data, the push for personalized medicine, and the need for operational efficiencies. AI applications are transforming various aspects of healthcare, including diagnostics, clinical trials, and drug discovery, thereby enhancing patient care and reducing costs. The market evidence indicates that AI can significantly improve diagnostic accuracy and streamline administrative tasks. Notably, the U.S. is positioned as a leader in AI adoption within healthcare, backed by substantial investment and regulatory support aimed at promoting innovation and improving patient outcomes.

  • 3-4. AI Adoption in Aerospace & Defense

  • The aerospace and defense sectors are increasingly integrating AI technologies, driven by demands for enhanced operational efficiency and improved mission outcomes. Predictions indicate a robust market expansion in AI applications specific to these sectors over the coming years. Factors contributing to this growth include advancements in autonomous systems, predictive maintenance, and AI-driven data analytics, which are essential for optimizing performance and security in increasingly complex operational environments. As of June 2025, significant investments are being made towards developing AI-based solutions that ensure readiness and adaptability in flight systems and defense technologies, although challenges related to regulatory frameworks and integration with existing systems persist.

4. Emergence of Agentic AI in the Enterprise

  • 4-1. Defining Agentic AI and Its Distinction from Generative Models

  • Agentic AI represents a significant evolution in artificial intelligence, distinguishing itself from generative AI by its inherent autonomy and contextual adaptability. Generative AI primarily focuses on creating content, utilizing algorithms to generate text, images, or other media based on input data. In contrast, agentic AI systems are designed to operate independently in real-world environments, making decisions, adapting to changing circumstances, and executing tasks with minimal human intervention. Unlike generative models which are often bound to specific functions, agentic AI incorporates a broader set of capabilities, enabling it to reason and plan based on dynamic conditions around it.

  • As organizations increasingly recognize the benefits of agentic AI, the integration of these autonomous systems into enterprise operations is being seen not only as a technological advancement but as a critical infrastructure shift. According to recent reports, agentic AI is projected to grow from $5.1 billion in 2025 to more than $47 billion by 2030, highlighting the strategic importance and rapid adoption of this technology across industries. With such promising projections, forward-looking executives are already planning integrations that capitalize on the technology’s capacity to improve efficiencies in operations, IT management, and customer engagement.

  • 4-2. Enterprise Use Cases Across Industries

  • The application of agentic AI is becoming increasingly diverse, with significant deployments observed across multiple sectors. In the retail industry, companies are utilizing agentic AI to optimize customer experiences through personalized recommendations and efficient inventory management. For instance, businesses like Alibaba and Amazon are implementing AI agents that can analyze consumer behavior patterns, manage stock dynamically, and execute targeted marketing strategies without substantial human oversight. These agents operate through a framework that allows them to learn continuously from their interactions, thereby enhancing operational effectiveness and customer satisfaction.

  • In the banking, financial services, and insurance (BFSI) sector, agentic AI is empowering institutions to streamline operations, detect fraud, and improve customer onboarding processes. For example, agentic systems are assessing risks associated with loan applications using localized data analysis, which allows for more rapid decision-making and enhanced service offerings. Additionally, companies such as Goldman Sachs and various fintech startups are pioneering the development of AI agents that assist with compliance, automate financial advisories, and enhance risk management. This systemic integration creates not only enhanced productivity but also fosters a culture of agility that is essential in today's fast-paced economic environment.

  • Moreover, the travel and hospitality sectors are also witnessing a transformation through the use of agentic AI. Initiatives such as the collaboration between various travel agencies to create AI-driven multitasking booking assistants demonstrate the capability of these agents to provide fully personalized travel planning experiences. These advancements position agentic AI as a crucial tool for operational efficiency, allowing businesses to automate complex customer queries and enhance overall service delivery.

  • 4-3. Projected Growth and Adoption Timelines

  • As enterprises continue to explore the capabilities of agentic AI, projections indicate a significant shift in adoption timelines over the next few years. Current survey data reveals that about 51% of organizations are actively investigating the utility of AI agents, while 37% are piloting these systems. Notably, 21% of leaders expect full integration within the next twelve months, reflecting an urgency to leverage AI for operational improvements.

  • Looking ahead, Gartner forecasts that by 2028, a staggering 33% of all enterprise software applications will incorporate agentic AI capabilities, a clear signal that the technology will be a cornerstone of digital transformation. As companies navigate these transitions, foundational elements such as robust data infrastructures and strategic governance frameworks will become critical to ensure successful implementation and mitigate risks associated with autonomous agent behaviors. Thus, the road to widespread agentic AI adoption is not merely about technological acquisition, but also about fostering an organizational culture that embraces continuous learning and adaptation.

5. Cybersecurity Implications of Agentic AI

  • 5-1. New Threat Vectors Introduced by Autonomous Agents

  • The rise of agentic AI, which operates autonomously and conducts tasks without human oversight, introduces a plethora of new cybersecurity threats. As described in a recent article, agentic AI systems can autonomously gather data, make decisions, and execute commands, which can complicate traditional security measures. This capability presents unique vulnerabilities, such as adversarial attacks and data poisoning, where malicious actors can manipulate AI systems for harmful purposes. Moreover, the complexity of agentic AI operations makes it challenging for organizations to secure their infrastructures. Example threats include 'shadow AI' usage—where unauthorized AI systems operate outside of established corporate protocols—leading to increased risk exposure. Cisco's 2025 Cybersecurity Readiness Index highlights that 90% of organizations face heightened risks from unmanaged devices, indicating that the landscape for cyber threats is evolving faster than corresponding defenses are developing.

  • 5-2. Organizational Readiness and Cisco’s 2025 Index

  • According to Cisco’s 2025 Cybersecurity Readiness Index, only 7% of organizations in India have reached a mature level of cybersecurity readiness, reflecting a critical need for improvement amidst the rapid rise of AI technologies. This index illustrates a global trend wherein only 4% of organizations achieve a mature cybersecurity status, signaling that many companies are ill-equipped to contend with sophisticated AI-driven threats. The report underscores significant gaps in visibility into AI usage within organizations; notably, 45% of respondents reported lacking insight into shadow AI deployments, with 43% of IT teams unaware of generative AI usage among employees. As enterprises adopt these advanced technologies, proactive measures regarding cybersecurity governance, employee training, and continuous monitoring must become integral components of their digital transformation strategies.

  • 5-3. Network Security Challenges and Best Practices

  • Agentic AI introduces substantial network security challenges, primarily due to its operational nature that requires extensive data access and connectivity across varied platforms. As articulated in a report from Unite.AI, agentic AI agents gather data from diverse environments, including cloud and on-premises, which complicates the safeguarding of sensitive information. To address these challenges, organizations are advised to adopt a comprehensive security strategy at multiple operational stages of agentic AI development. This includes enhancing network connectivity with end-to-end encryption during data collection, implementing robust decision-making models aligned with security protocols, and ensuring observability and traceability in AI actions. Best practices also emphasize training employees on AI-related threats and continuously monitoring systems to mitigate risks posed by autonomous AI agents.

6. Challenges and Considerations for Scaling AI

  • 6-1. Data Infrastructure and Integration Obstacles

  • One of the primary challenges enterprises face when scaling AI technologies is the need for robust data infrastructure. The complexity of AI systems often necessitates the integration of diverse data sources, but many organizations struggle with data silos that hinder the seamless flow of information. Effective AI models require access to high-quality, well-structured data—variables that are frequently absent in existing systems. In recent years, regulatory scrutiny and advancements in data privacy have also intensified pressures on data management practices. Companies must ensure compliance with evolving regulations while still providing access to data necessary for AI training. This often requires significant investments in both technology and human resources to develop integrated data frameworks. Moreover, as organizations transition to increasingly AI-driven workflows, they experience challenges related to legacy systems. Many businesses still rely on outdated infrastructures that hinder the implementation of new AI solutions. Therefore, overcoming these integration obstacles is critical for successful AI scaling, necessitating a holistic approach to data governance and infrastructure development.

  • 6-2. Talent Shortages and Workforce Transformation

  • As of June 2025, the labor market reflects a significant ongoing trend of talent shortages in AI-related fields. Companies are finding it increasingly difficult to recruit professionals with the requisite skills in machine learning, data science, and AI ethics. Reports indicate that there is a disparity between the demand for AI talent and the current availability, which poses a tangible barrier to scaling AI solutions effectively. Organizations are responding to this challenge by investing in workforce transformation initiatives. For instance, companies are beginning to retrain existing employees to increase their digital and AI capabilities. This retraining can enhance their value in the workplace and create a more adaptable workforce. The case of Klarna exemplifies a business that, despite cutting its workforce due to AI implementation, recognizes the need to rehire strategically for roles that enhance human–AI collaboration. In addition, fostering an organizational culture that embraces AI learning and development will be pivotal for future success. The growing trend of business professionals learning programming and data analytics highlights a shift towards empowering existing personnel with the skills needed to work alongside AI—thus bridging the talent gap effectively.

  • 6-3. Regulatory Scrutiny and Model Risk Management

  • The landscape of AI in enterprises is under increasing regulatory scrutiny as organizations become more reliant on AI models for decision-making processes. This scrutiny is driven by a need to manage potential biases and ensure transparent operations within AI systems. Regulatory frameworks are developing rapidly to address concerns related to fairness, accountability, and the ethical implications of AI actions. Currently, the global market for AI model risk management stands poised for substantial growth, with a projected increase to $13.6 billion by 2030. This anticipated growth underscores a heightened demand for advanced tools designed to proactively identify and mitigate risks associated with AI models. As seen in the latest reports, organizations are investing significantly in solutions that not only ensure compliance with regulatory requirements but also enhance the operational robustness of AI deployments. To effectively navigate this environment, enterprises must prioritize the establishment of frameworks that focus on model governance and risk assessment capabilities. They must balance the innovative potentials of AI with responsible deployment to maintain stakeholder trust and regulatory compliance, particularly in sensitive sectors like banking and healthcare where the implications of AI-driven decisions can be profound.

7. Key Industry Players and Strategic Movements

  • 7-1. Major Technology Leaders and Market Shares

  • As of June 2025, the global artificial intelligence market is firmly anchored by major technology leaders such as Microsoft, IBM, Google, Oracle, and NVIDIA, which continue to dominate market shares. Collectively, they represent a significant portion of the industry, contributing to an expected growth from USD 371.71 billion in 2025 to USD 2, 407.02 billion by 2032, at a compound annual growth rate (CAGR) of 30.6%. These leaders are intensely engaged in enhancing their AI capabilities through robust research and development initiatives, ensuring their solutions include cutting-edge technologies like deep learning and autonomous AI, which are crucial drivers of market expansion. Furthermore, the establishment of AI-as-a-Service (AIaaS) platforms is facilitating access to powerful AI tools for various enterprises, thereby democratizing the technology and enabling broader application across sectors such as healthcare, finance, and customer service.

  • 7-2. Recent Product Launches and Partnerships

  • The competitive landscape of the AI market has been significantly shaped by recent product launches and strategic partnerships among key players. For instance, the integration of AI into existing infrastructures has been solidified through partnerships between tech giants and industry-specific players. Microsoft’s advancements in AI for customer service and Salesforce’s utilization of generative AI to enhance CRM systems illustrate how recent collaborations are propelling technological capabilities forward. As reported, the AI in customer service market is anticipated to grow from USD 12.06 billion in 2024 to USD 47.82 billion by 2030, spotlighting the opportunities stemming from AI-driven innovations that foster increased engagement and operational efficiency. Moreover, the rise of hybrid models in customer interaction—combining AI capabilities with human insights—is redefining service paradigms, offering consumers seamless and personalized support.

  • 7-3. Regional Dynamics and Competitive Landscapes

  • The regional dynamics of the AI market reveal interesting competitive landscapes, particularly in regions such as Asia-Pacific, which is witnessing the highest CAGR in AI-powered customer service technologies. Countries like India and China are rapidly adopting AI solutions across various industries, driven by the sheer volume of consumer demands and digital transformation initiatives. The integration of AI into sectors such as retail and banking is enabling businesses to enhance customer interactions, thereby meeting the growing expectations for personalized services. In the U.S., a balanced and innovative regulatory approach continues to foster an environment ripe for AI advancements, while government investments and collaborative efforts between tech firms and academic institutions propel AI research and application forward, solidifying the U.S.’s leading position in the global AI landscape. This regional analysis underscores the differentiating factors that companies leverage to maintain competitive advantages in an increasingly interconnected and Technology-driven world.

Conclusion

  • The global AI market is on the cusp of transformative growth, characterized by compound annual growth rates between 19% and over 30% across various projections. A granular analysis of sector-specific trends reveals significant opportunities particularly in customer service, banking and finance, healthcare, and aerospace and defense. Each of these sectors is increasingly adopting tailored AI solutions designed to drive both efficiency and innovation. Nonetheless, this rapid advancement coincides with the rise of agentic AI technologies, which present a dual-edged sword. Enterprises must concurrently navigate the abundant opportunities while addressing rising cybersecurity threats, strengthening data governance frameworks, and ensuring compliance with evolving regulatory standards. To effectively leverage the burgeoning AI landscape, organizations should invest strategically in scalable infrastructure and foster the growth of cross-functional AI talent. An emphasis on employee retraining initiatives will not only bridge existing talent gaps but also cultivate an adaptive workforce capable of maximizing AI's potential. Moreover, proactive engagement with emerging policy frameworks will be essential for organizations aiming to integrate AI responsibly and ethically. Looking ahead, the interplay between generative and agentic AI is poised to redefine established business processes. This evolution will necessitate the formation of collaborative ecosystems among technology providers, regulators, and end-users, ensuring a responsible realization of AI's capabilities. Organizations that can navigate these complexities while simultaneously innovating stand to benefit substantially as AI becomes a vital driver of competitive advantage in the coming years.

Glossary

  • AI Market: Refers to the overall financial marketplace for artificial intelligence technologies, encompassing various sectors such as healthcare, customer service, and finance. As of June 5, 2025, the market is experiencing significant growth, projecting an increase from USD 371.7 billion in 2025 to multi-trillion dollar valuations by the early 2030s.
  • CAGR: Compound Annual Growth Rate (CAGR) is a statistical measure that describes the mean annual growth rate of an investment over a specified time period longer than one year. The AI market is showing projected CAGRs from 19% to 30.6%, indicating rapid but potentially volatile growth influenced by sector-specific demand and regulatory environments.
  • Generative AI: A subset of artificial intelligence focused on creating new content, such as text, images, or sounds, based on existing data inputs. This technology is driving innovation in various sectors but is distinct from agentic AI, which emphasizes autonomy and decision-making capabilities.
  • Agentic AI: A form of artificial intelligence characterized by its ability to operate autonomously in real-world environments, making decisions and adapting to changing circumstances without human intervention. The projection shows agentic AI is expected to grow from USD 5.1 billion in 2025 to over USD 47 billion by 2030.
  • Healthcare AI: Refers to the application of artificial intelligence technologies within the healthcare sector, aimed at improving patient care, enhancing operational efficiencies, and personalizing medical approaches. Projections indicate growth from USD 36.96 billion in 2025 to USD 613.81 billion by 2034.
  • Cybersecurity: The practice of protecting systems, networks, and programs from digital attacks. As of June 2025, the rise of agentic AI introduces new cybersecurity threats, making it vital for organizations to enhance their security frameworks to manage autonomous systems effectively.
  • Data Infrastructure: Refers to the underlying database systems and technologies that support the storage, organization, and management of data within organizations. The effectiveness of AI solutions is heavily reliant on robust data infrastructures to ensure seamless data access and quality, crucial for AI project success.
  • Digital Transformation: The process by which organizations integrate digital technology into all areas of their business, fundamentally changing how they operate and deliver value to customers. This transformation is critical for leveraging advanced AI technologies effectively.
  • Talent Shortages: Current challenges organizations face in recruiting skilled professionals necessary for the development and implementation of AI technologies. As of June 5, 2025, there is a notable gap between the demand for AI talent and its availability, hindering effective scaling of AI solutions.
  • AI-as-a-Service (AIaaS): A cloud-based service model where third-party providers offer AI capabilities to organizations, allowing them to leverage AI technologies without heavy investments in infrastructure. This model is facilitating broader access to AI tools across various sectors.
  • Regulatory Scrutiny: Increasing oversight and demands from authorities to ensure that AI technologies operate transparently and ethically. Organizations are under pressure to comply with evolving regulations to manage potential risks and biases related to AI systems.

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