Agentic AI represents the next step in AI: an intelligent system capable of autonomous decision-making, adaptive problem-solving, and real-time optimization without consistent human input. Unlike traditional AI, which supports predefined tasks, agentic AI can reason, act, and learn proactively, offering organizations unprecedented levels of efficiency, scalability, and strategic foresight. This Viewpoint explores the shift from generative AI (GenAI) to agentic systems and what it means for business models, operations, and strategy.
GENAI TO AGENTIC AI
AI is advancing at an extraordinary pace, transforming the way businesses function and compete. GenAI tools like ChatGPT have unlocked new levels of productivity — generating content, assisting with research, and supporting decision-making — but these systems are reactive. They require constant human input and operate within narrow, predefined workflows.
Agentic AI represents a paradigm shift from passive support to proactive autonomy. Unlike GenAI, which responds to prompts and follows static rules, agentic AI systems act with goal-directed intelligence. They initiate tasks independently, orchestrate tools across platforms, adapt in real time, and continuously improve through memory and learning. By building on past interactions, agentic AI refines its behavior over time, something traditional large language models (LLMs) and machine learning (ML) models do not.
Agentic AI also brings capabilities like reasoning, long-term planning, and dynamic environmental interaction. Instead of automating tasks, it tackles complex workflows that once required significant human oversight. The result is a leap in productivity, adaptability, and business value. Consider the domain of software development. GenAI can assist with code snippets or debugging suggestions, but an agentic AI system can operate autonomously. It understands high-level goals, diagnoses performance issues, identifies and modifies the relevant code, deploys changes via GitHub, runs automated tests, adapts based on the outcomes, and learns from each iteration with no manual intervention required.
The agentic AI applied market is expected to reach US $45 billion by 2030 (see Figure 1).
Figure 1. Agentic AI global market size
EVOLUTION OF AGENTIC AI
This shift redefines AI’s role in the enterprise — moving from reactive assistant to strategic collaborator. Figure 2 captures the evolution from GenAI tools like ChatGPT to truly autonomous agentic systems.
Figure 2. Evolution of agentic AI
Agentic AI shifts away from single, standalone agents toward interconnected networks of specialized agents working collaboratively called multi-agent systems (MASs). MAS architectures unlock three key advantages:
Collaborative problem-solving. Multiple agents can divide and conquer complex tasks.
Scalability. Systems grow seamlessly by adding or updating individual agents.
Adaptability. Modular design allows rapid response to changing business needs.
As Figure 3 illustrates more fully, the evolution of AI moves from basic systems with no intelligence, through GenAI that creates content based on human prompts, to AI agents that act independently toward specific goals. Future deployments will involve MASs that collaborate and coordinate actions across tasks and platforms with the ultimate goal of self-optimizing agents. These autonomous, adaptive systems learn from experience, improve over time, and handle complex workflows without human oversight, representing the pinnacle of proactive, intelligent automation.
Figure 3. Types of agentic AI
TYPES OF AGENTIC AI
Agentic AI comes in two main forms — horizontal and vertical — each tailored to different business needs:
Horizontal agents are generalists, built to operate across a wide range of functions and domains. They offer broad adaptability and are ideal for tasks like content generation, research, and coordination across diverse workflows. However, their flexibility often requires significant customization and integration effort. Powerful in general-purpose scenarios, they are likely to struggle with tasks that demand deep domain expertise.
Vertical agents are specialists. They’re designed for specific industries, functions, or processes, leveraging domain-specific data and tools to deliver accurate, compliant, high-impact outcomes. Their focus allows for faster time to value but limits their versatility and scalability.
Hybrid approaches are emerging as a compelling middle ground, pairing horizontal agents for broad, cross-functional tasks with vertical agents that specialize in domain-specific work. This best-of-both-worlds strategy allows organizations to combine flexibility with precision, using horizontal agents to handle generalized workflows and vertical agents to address high-value, niche applications.
IDENTIFYING THE RIGHT AGENT USE CASE
AI agents are most valuable in situations where workflows are complex, repetitive, data-rich, and require autonomy or coordination across multiple systems. Ideal use cases share a few key characteristics:
They involve clearly defined goals but use dynamic paths to reach them.
They depend on real-time data, contextual understanding, or multi-step decision-making.
They place a high burden on human time due to manual monitoring, coordination, or intervention.
They benefit from learning over time to improve performance and adapt to change.
To identify strong candidates for agentic AI, organizations should:
Map processes that are rules-driven but too variable for simple automation.
Look for areas where human input is frequent but low in strategic value.
Prioritize functions that are scalable and cross-functional (e.g., operations, support, procurement).
Evaluate tasks that involve external tools, APIs, or knowledge sources.
In short, the best use cases for AI agents are not those involving executing commands. They’re cases requiring ongoing judgment, coordination, and adaptation — areas where traditional automation falls short.
Horizontal functions
Customer support: From tickets to conversations
Help desks are overwhelmed.
AI workflows triage tickets.
Agentic AI handles live, multi-turn conversations, detects frustration in tone, escalates appropriately, and learns from each interaction.
HR: Intelligent talent acquisition
Hiring is slow and inconsistent.
AI tools sort resumes and book interviews.
Agentic AI sources candidates, conducts initial screening, and adapts hiring criteria based on long-term performance data.
Marketing: Campaigns that evolve themselves
Low ROI often stems from static segmentation.
AI tools recommend targeting and timing.
Agentic AI designs full-funnel campaigns, optimizes live performance, and reallocates budget in real time.
Sales: Precision outreach at scale
Sales reps waste time on cold leads.
AI enriches customer relationship management with behavioral data.
Agentic AI scores leads dynamically, sequences outreach, and adapts messages in real time based on live response signals.
Vertical applications
Healthcare: Smarter, faster clinical trials
Clinical research is plagued by inefficiencies, including manual data entry, slow recruitment, and high dropout rates.
Traditional AI automates form processing and screening.
Agentic AI matches patients to trials in real time, dynamically updates protocols based on site data, and ensures compliance across geographies — all with minimal human input.
Retail: Inventory systems that think ahead
Poor forecasting leads to overstocks or empty shelves.
AI workflows flag low stock and suggest replenishment.
Agentic AI forecasts demand, adjusts local inventory, and reroutes deliveries — autonomously and in real time, based on weather, events, and customer behavior.
Telecom: Proactive network management
Customer churn often starts with silent service issues.
AI tools detect faults and send alerts.
Agentic AI preemptively balances network load, resolves issues before users notice, and learns from every incident to prevent recurrences.
Media: Hyperpersonalized engagement
Generic content lowers engagement and loyalty.
AI workflows tag and index assets.
Agentic AI curates personalized feeds, adapts publishing cadence to user behavior, and dynamically shifts content strategy across channels.
Manufacturing: Zero-downtime production
Unexpected equipment failure costs millions.
AI tools predict wear and tear.
Agentic AI actively reconfigures workflows, schedules maintenance, and keeps production lines running without manual oversight.
DRIVING BUSINESS GROWTH WITH AGENTIC AI
Agentic AI is more than a tool for automation: it is a strategic driver of innovation and transformation. By improving operational efficiency, reducing costs, and unlocking new opportunities for creativity, agentic AI lets businesses shift their focus from routine processes to high-value, strategic initiatives. Organizations that successfully integrate AI agents can redirect human capital toward deep thought, problem-solving, and innovation, ultimately positioning themselves as industry leaders. The following are three ways agentic AI fuels business growth in a fast-paced, dynamic world:
Operational efficiency. One of the most immediate, tangible benefits of agentic AI is its ability to completely transform an organization’s efficiency. AI agents can streamline many processes that previously typically required much more human oversight and time, opening up opportunities for employees to focus on other priorities. The time saved by leveraging an AI agent to gather and compile data leaves humans with more time to think strategically about the results and to leverage the tool as a thought partner to develop a comprehensive strategy.
Cost reduction. Agentic AI can serve as a powerful lever for cost reduction, particularly through its ability to autonomously manage complex, dynamic systems like energy consumption. Traditional AI systems can optimize energy use based on pre-programmed rules, but agentic AI goes further by analyzing a range of real-time signals, learning from past patterns, and adapting its strategies over time. Agentic AI can deliver cost savings through continuous adaptation, strategic reasoning, and intelligent resource management while supporting broader sustainability goals.
Innovation catalyst. By minimizing manual overhead and improving resource efficiency, agentic AI lets businesses reinvest in R&D, customer experience, and business model innovation. Its proactive, goal-driven nature positions companies to lead rather than react, turning disruption into opportunity.
CHALLENGES & OPEN RESEARCH QUESTIONS
The potential of agentic AI is substantial, but several risks and challenges must be addressed:
Model logic and reliability. Agentic systems operate without fixed instructions, which can lead to variability and unpredictable outcomes. Ensuring consistent performance requires robust reasoning capabilities and continuous learning from real-world feedback.
Human-AI collaboration and governance. Striking the right balance between autonomy and oversight is essential. Transparent design, explainability, and access controls are critical to avoiding shadow agents and ensuring responsible deployment.
Data quality, security, and privacy. Agentic AI depends on diverse, real-time data inputs, increasing exposure to security risks, prompt injection, or compliance breaches. Data governance, including anonymization and segmentation, is vital.
Ethical design and societal impact. Without intentional safeguards, bias or misaligned incentives can lead to harmful behavior. Embedding ethics into decision logic is key to building public trust.
Adoption, ROI, and talent. Deploying agentic AI requires significant investment in infrastructure and talent. ROI may be slow initially, but the long-term potential to transform business operations could exceed that of GenAI.
Conclusion
PREPARING FOR AGENCY
Agentic AI offers transformative potential, but the right timing for implementation depends on strategic position. To move forward effectively, consider the following actions:
Assess payoff factor. For players where the payoff from adopting agentic is likely to be immediate and significant, rapid adoption can yield powerful results. For use cases that are less clear/emerging or where data infrastructure remains insufficient, a wait-and-see approach is advised.
Audit AI readiness. Assess technical maturity, integration potential, governance frameworks, and cultural fit before implementing and scaling agentic AI.
Define success metrics. Prioritize use cases with measurable ROI (e.g., cost, time) or qualitative outcomes (e.g., user feedback).
Build safeguards for visibility and intervention. Implement access control, real-time monitoring, audit trails, and shut-off mechanisms to mitigate the risk of shadow agents acting without visibility or accountability.
Whether or not early adoption indicators are present, the time to evaluate, prepare, and position for the future of agentic AI is now, allowing organizations to act with speed and confidence when the timing is right.
