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Since the introduction of ChatGPT in 2022, discussions around Large Language Models (LLMs) have taken center stage in the tech world. Yet, as interest in Generative Artificial Intelligence (Gen AI), known for its content generation capabilities, rises, a new player is emerging: Agentic Artificial Intelligence. Unlike Gen AI, which focuses on creating text, images, or code, agentic AI is designed for action. These AI agents can navigate complex processes, make autonomous decisions, and integrate effortlessly into enterprise systems.
Understanding agentic AI and its significance
Agentic AI represents a significant evolution in the realm of artificial intelligence, referring to autonomous software agents that leverage LLMs to perform structured tasks within business environments. These agents are particularly adept at interacting with enterprise applications, extracting vital data, classifying documents, resolving issues, and updating systems—all with minimal human intervention.
Unlike traditional chatbots or digital assistants that rely on continuous user prompts, agentic AI operates based on defined goals, processes, and feedback loops. They are designed to comprehend context, learn from experiences, make informed decisions, and execute workflows autonomously. This capability brings substantial business benefits, including enhanced accuracy, efficiency, and cost savings.
Real-world applications of agentic AI
Although still in its nascent stages, frameworks for agentic AI are gaining momentum in industries that are digitally advanced. Software developers, hyperscalers, and data specialists are actively working on creating AI agents. For instance, Crédit Agricole, a prominent bank, employs an AI agent to classify customer complaints. This agent integrates with internal systems, carries out data extraction and classification, and proposes responses, achieving a remarkable 95% accuracy rate while significantly reducing response times.
In another example, a German manufacturing company implemented an AI agent within its ERP and order processing systems. The solution, provided by Beam AI, accurately recognized incoming orders, extracted pertinent data, and automatically updated records. This resulted in a staggering 96% automation of order updates, an 89% reduction in manual processing time, and a 23% decrease in human errors.
The business value and challenges of agentic AI
The potential of agentic AI lies in its ability to streamline operations and make data-driven decisions without constant human oversight. However, the journey to widespread adoption is fraught with challenges. Concerns about performance issues can hinder the scaling of LLMs in critical applications, particularly due to risks of hallucination that may arise when agentic systems draw from multiple LLMs for task execution.
Security is another major consideration. A survey of 1,000 IT and business executives revealed that security vulnerabilities and AI-driven cyberattacks rank as the top two risks associated with implementing agentic AI. Since these agents require access to sensitive enterprise data and workflows, issues regarding data privacy, security, and regulatory compliance are paramount. Many enterprises prefer implementing agentic AI models in Virtual Private Clouds (VPCs) or on-premises environments, steering clear of third-party cloud services.
Monetizing agentic AI: strategies and market trends
As technology leaders explore ways to monetize agentic AI, innovative strategies are emerging. Companies looking to capitalize on these opportunities need to provide demonstrable value to enterprise customers. For instance, Microsoft offers Azure AI Agent services, which combine access to models, enterprise readiness, and built-in automation. Additionally, Robotic Process Automation (RPA) providers such as UiPath are integrating agentic capabilities into their process automation solutions, enabling businesses to deploy AI agents across various departments via a Software-as-a-Service (SaaS) model.
Telecom companies are also showing keen interest in agentic AI. For instance, SK Telecom collaborated with Perplexity to introduce “Aster,” an AI agent that serves as both a national solution and a white-labeled export. Deutsche Telekom and Google Cloud are working together on a multi-agent AI assistant, built on Gemini 2.0, to analyze network behavior and perform self-healing features.
Addressing the hurdles to adopting agentic AI
Despite the promise of agentic AI, several operational and cultural hurdles must be addressed. Many organizations still underestimate the importance of agentic AI, often relegating it to a lower priority in budgets and strategic planning. Factors such as risk aversion, siloed teams, and a lack of user-friendly tools for developers can impede its adoption. Moreover, C-suite executives often need convincing regarding the tangible benefits of agentic AI beyond mere automation.
Therefore, technology vendors must clearly communicate how agentic AI provides value—specifically, what tasks it automates and the efficiency gains compared to human workers. Questions about accuracy and efficiency should be central to outreach strategies.
Future prospects of agentic AI in business
As we look ahead, it’s apparent that agentic AI represents more than just a technical advancement; it signifies a new product category with considerable commercial potential. The ability to facilitate faster workflows, reduce operational costs, and enhance customer experiences is evident. However, success hinges on whether organizations can effectively navigate the complexities of integration, establish robust data governance frameworks, and prioritize necessary investments in infrastructure, like cloud computing and data management tools.
In conclusion, while agentic AI technology matures, the challenges it faces in terms of culture and governance are substantial. For agentic AI to achieve its full potential, leadership buy-in and strategic investment will be crucial. The future of enterprise automation may be on the brink of a significant shift, one that redefines how businesses approach efficiency and productivity in a rapidly evolving technological landscape.