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The Rise of Agentic AI

Publish Date: November 2025

Author: Phil Marshall, PhD

We are now living in an AI era, where intelligent systems are becoming increasingly integrated into everyday workflows and decision-making processes. In this context, Agentic AI has emerged as a new class of systems that can sense what is happening, interpret information, and take action toward a goal with only limited human involvement. Unlike traditional AI, which focuses on pattern recognition or predictive outputs, agentic AI can plan ahead, break tasks into subtasks, adapt to new conditions, collaborate with other systems, and learn continuously. These agents operate in dynamic, fast-changing environments across enterprise workflows, industrial automation, and digital knowledge work, continually making real-time decisions to advance their objectives. As organizations push beyond passive analytics toward proactive automation, agentic AI represents a foundational shift to goal-driven, self-directed intelligence.

Opportunities Abound

Agentic AI is emerging as organizations move beyond insight-driven analytics toward systems that can act and deliver outcomes autonomously. Advances in large language models now enable reasoning, planning, and multi-step adaptation, making it possible for AI agents to participate directly in real workflows. At the same time, enterprises are under pressure to automate complex processes, scale efficiently, and maintain continuous operations. This positions agentic AI as a digital workforce that can collaborate across software systems through APIs and workflow integrations. With development frameworks maturing and a growing emphasis on accelerating execution, agentic AI has become a strategic priority for organizations seeking greater speed, efficiency, and competitive advantage.

Digital native companies and cloud-based software firms are currently the most aggressive adopters of agentic AI, since they already work with automated workflows, APIs, and rapid development cycles. Service-intensive sectors such as financial services, telecoms, consulting, and business process outsourcing are also moving quickly because agentic AI can act as a scalable digital workforce to support analysis and customer operations. Industries with complex operations, such as manufacturing, logistics, and utilities, are exploring agents for scheduling, maintenance, and real-time coordination; however, their adoption tends to be a gradual process. Highly regulated sectors are adopting agentic AI more cautiously, focusing first on internal workflows where risks and controls are manageable. In general, organizations with modern data systems and automation experience advance fastest, while those reliant on legacy processes move more slowly.

But Not Without Challenges

Agentic AI also introduces significant challenges. Because these systems act autonomously, reliability and controllability become critical concerns, as agents can take unintended actions, misinterpret instructions, or produce incorrect steps when reasoning about unfamiliar situations. Ensuring transparency, auditability, and alignment with organizational policies is complex, particularly when agents interact with multiple systems and real data. Many enterprises lack the governance structures to monitor and validate agent behavior at scale. Security and access control also become more sensitive, since agentic AI often requires elevated permissions to perform tasks. As a result, successful deployment requires strong guardrails, controlled testing environments, oversight mechanisms, and cultural adaptation to strike a balance between autonomy, safety, and trust.

Massive Growth Expectations

Despite the challenges, global spending on agentic AI is set to accelerate rapidly over the next several years as organizations transition from insight-driven AI to systems that can autonomously execute tasks and deliver outcomes. We estimate that global expenditures on agentic AI will rise from approximately USD 8.0 billion in 2025 to USD 39.4 billion in 2030, reflecting a compound annual growth rate (CAGR) of 37.5%. This growth is driven by the expanding adoption of AI-enabled workflow orchestration, digital workforce augmentation, and autonomous process automation across service-intensive and operations-heavy sectors. As development frameworks mature and integration with enterprise software ecosystems becomes more seamless, agentic AI is expected to play an increasingly central role in digital transformation and operational efficiency strategies worldwide.

GLOBAL AGENTIC AI FORECAST bil. USD 2025202620272028202920308.016.024.032.040.08.010.4314.721.0529.3739.32

Forecast Sensitivity Analysis

Our current forecast for the Agentic AI market reflects our best assessment of the current most likely trajectory, drawing on known market activity, anticipated expansion, and historical precedents from comparable adoption scenarios. As the market develops and becomes better understood, these assumptions may evolve. Sensitivity analysis is crucial when business models and business results depend on forecast outcomes, particularly when longer-term horizons (e.g., 10 years) are required for financial modeling and investment decisions. The sensitivity of the Agentic AI forecast is illustrated below with the 2025 market size ranging from $6.0 to $10.0 billion, with five- or ten-year CAGRs between 20% and 50%. Additionally, multiple adoption profiles are used to illustrate the impact of different annual growth rates across the forecast horizon.

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37%

8.0 bUSD

Summary

Agentic AI represents a shift from traditional insight-driven analytics to AI systems that can autonomously plan, make decisions, and carry out tasks with minimal human involvement. Adoption is being led by digital-native firms and service-intensive industries, where automated workflows and integration across software systems already exist. Sectors with complex operational needs are experimenting, while highly regulated industries are advancing more cautiously. Although the opportunities are significant, agentic AI introduces challenges related to reliability, safety, governance, and access control. Global spending on agentic AI is expected to grow rapidly, rising from USD 8.0 billion in 2025 to USD 39.4 billion in 2030. As development frameworks mature and organizations improve their automation readiness, agentic AI will play a central role in digital transformation and operational efficiency initiatives.

Action Items

  • Assess Workflow Readiness: Identify processes that are structured, repetitive, and data-rich, where agentic AI can be piloted with low operational risk.

  • Establish Governance Structures: Define oversight mechanisms, audit controls, and safety guardrails to ensure reliable and transparent agent behavior.

  • Integrate with Existing Systems: Prioritize API readiness, data accessibility, and platform interoperability to support agentic automation.

  • Pilot in Internal Use Cases First: Begin with controlled environments such as internal reporting, workflow routing, or service desk operations before scaling.

  • Build Cultural Confidence: Train teams to work effectively with AI agents, reinforcing that these tools augment rather than replace skilled roles.

  • Monitor Market Developments: Track emerging frameworks, vendor capabilities, and best practices as the market grows and standards evolve.