How Generative-AI "Agents" Can Accelerate Enterprise Business Transformation

By Phane Mane

According to the latest McKinsey Global Survey on AI adoption, nearly 65 percent of respondents reported that their organizations regularly use generative AI—almost double the percentage from a survey just 10 months earlier. This surge in AI adoption is mirrored in recent SEC filings analysis by Arize, which found that “1 in 5 Fortune 500 companies now mention generative AI or large language models (LLMs) in their annual financial reports.”

While this is promising, most enterprise AI projects being deployed to production still focus on "question and answering" (Q&A) through chat interfaces, a capability popularized by OpenAI’s ChatGPT. Some companies are developing business-facing applications using LLMs via APIs, offering more user-friendly interfaces, but these remain in the minority and cannot often perform autonomous actions.

Despite their limitations, such Q&A-focused systems can be highly impactful. For instance, they can enable customer service teams to handle a surge in inquiries, assist project managers by summarizing key materials for stakeholders, help health insurance providers improve the speed and accuracy of medical claim responses, and enable researchers to rapidly summarize data, thereby accelerating R&D.

However, the future of LLMs lies beyond answering questions; it’s about taking action.

Introducing Generative AI Agents

An AI agent is a system or application that not only provides information but also autonomously achieves goals by making decisions and executing sub-tasks with available tools. In essence, agents go beyond the passive role of current LLM-powered systems, transforming them into active participants in executing tasks.

This goal-driven capability, where AI operates with minimal human intervention, is referred to as an "agentic" system.

How Do Generative AI Agents Work?

A generative AI agent leverages one or more LLMs to interpret instructions and utilize various tools—such as enterprise software systems via APIs—to complete tasks. Unlike traditional LLMs, which are limited to their training data, agentic technology creates subtasks, adapts to user requirements, and recalls past interactions. This allows for efficient task execution without constant human oversight.

Technically speaking, generative AI agents rely on two core processes: evaluation and planning, as well as tool use. In the evaluation and planning phase, agents break down complex tasks into smaller steps, assess progress iteratively, and adjust their approach as needed to ensure accuracy. Techniques such as Chain-of-Thought (CoT), Reason and Act (ReAct), and Prompt Decomposition enhance their strategic thinking.

On the other hand, reasoning through tool use involves the agent interacting with its environment by selecting the appropriate tools and methods, such as Retrieval-Augmented Generation (RAG), to effectively execute sub-tasks and achieve desired outcomes.

Types of Generative AI Agents

AI agents are typically created with specific purposes in mind; some examples include:

Conversational Agents: dialogue-based interactions such as retail customer support

Workflow Agents: automate repetitive backend tasks like ticket generation and approvals

Healthcare Agents: streamline patient management and automate prescription processes

Regardless of the use case, multimodal agents—those capable of processing inputs across language, vision, audio, and video—can handle text-based operations, such as chatbots, or combine visual and language data for tasks like image classification. They can simultaneously process text, images, and audio, making them ideal for complex interactions like virtual assistants that support voice, text, and visual data inputs.

Why Should Enterprises Use Generative AI Agents?

Enterprises can significantly improve their efficiency by deploying purpose-built generative AI agents. For example, a Customer Service Representative (CSR) Agent could manage complex workflows such as reading and categorizing customer emails, processing incoming requests, generating relevant responses, and creating tickets in platforms like Jira. Similarly, a Project Management Workflow Agent could automate status updates and generate reports based on team progress, freeing project managers to focus on higher-level strategic tasks.

This approach minimizes human intervention, shortens response times, and enables employees to prioritize more important tasks, ultimately streamlining operations and accelerating business transformation across the enterprise.

Real-Life Applications of Generative AI Agents

Consider an example to illustrate the resourcefulness of generative AI agents in a task involving multiple steps, decisions, and tools. Suppose you’re planning a trip from Boston to Paris during the Christmas holidays. Instead of simply searching for flights online, a "Travel Agent" AI could:

• Pull real-time data from sites like Expedia and Kayak
• Use deterministic capabilities of LLMs to compare flight options and prices
• Check Paris weather forecasts to suggest the best travel dates
• Book a flight securely using stored details and process the payment
• Find and reserve a hotel based on your preferences
• Create an itinerary with activities and dining options, incorporating travel times
• Adjust reservations in case of delays and make alternative arrangements as needed

In the healthcare industry, generative AI agents are already driving significant improvements. For example, York, Pa.-based WellSpan Health recently introduced a generative AI agent to assist with documentation, automate administrative tasks, and support clinical decision-making. This reduces the workload on healthcare staff, allowing them to focus more on patient care. AI tools are also enhancing telehealth services, providing virtual health support, and enabling faster, more efficient responses to patient needs, thereby optimizing the patient journey and streamlining operational workflows.

Conclusion

Generative AI agents are transforming enterprise operations by automating complex, multi-step tasks and supporting enhanced decision-making. With their advanced tool-calling capabilities and seamless integration with external systems, these agents drive efficiency, streamline workflows, and reduce manual intervention. By harnessing these intelligent systems, organizations can optimize operations, scale more rapidly, and stay competitive in an increasingly digital landscape—positioning AI agents as key drivers of modern business transformation.

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