Agentic AI Explained: Autonomy That's Reshaping How We Work

In an interview with CiOL, Dr. Pavan Gurazada, Senior Faculty at Great Learning and Adjunct Lecturer at Northwestern University, shares insights on the transformative nature of agentic AI, highlighting its autonomous learning and execution of complex tasks with minimal intervention, ultimately driving significant productivity gains for businesses by automating decisions and freeing employees for strategic work. The discussion further explores how this technology is reshaping business functions, the role of Large Language Models (LLM) in enabling task automation, and organizational strategies for effectively implementing agentic AI alongside their workforce to streamline processes. Pavan also talks about how mid-sized enterprises can successfully embrace agentic AI. Excerpts.

Agentic AI is a significant evolution beyond traditional AI paradigms. In your view, what are the key differences between agentic AI and conventional AI models, particularly in terms of autonomy, decision-making, and its ability to drive business transformation? 

Agentic AI marks a notable advancement over traditional AI systems. While older models primarily react to user input and follow predefined rules, agentic AI can reason, make autonomous decisions, and adapt to changing circumstances. Unlike conventional systems, which function more like input-output machines bound by specific tasks, agentic AI is engineered as a cohesive system aimed at achieving broader goals. It is capable of undertaking complex tasks with minimal supervision and managing dynamic situations effectively.

This capability is primarily driven by large language models (LLMs) that can reason and interact with various tools. These LLMs enable agentic AI to not only comprehend and generate language but also to determine what actions are necessary to achieve its objectives. In this way, agentic AI resembles an autonomous system that proactively identifies the next steps rather than just following given instructions.

For businesses, this shift is transformative. Agentic AI goes beyond merely automating repetitive tasks; it redefines workflows and decision-making processes. It can seamlessly navigate between different systems, make real-time decisions, and adjust to shifts in the environment, empowering organizations to operate more efficiently and adaptively. This change elevates AI from being a mere tool to becoming a truly autonomous partner, fundamentally reshaping business practices and driving strategic progress.  

Developing Large Language Models (LLMs) is a complex task. How do you see LLMs contributing to the automation of decisions and tasks, and what role do they play in optimizing workflows, and where does the human-in-the-loop kick in, in an agentic environment? 

LLMs play a huge role in automating both decisions and tasks by acting as a centralized brain that connects different business systems. They’re great at handling repetitive tasks, streamlining workflows, and offering smart recommendations—cutting down on the need for constant human intervention. This is especially useful in fast-paced business environments where accuracy and speed are crucial. When things get more complex, LLMs can also help with decision-making. They analyze large sets of data, spot patterns, and even predict outcomes. For example, in finance, they can assist with risk analysis and suggest investment strategies. In healthcare, they can help by summarizing patient records and recommending treatments, all of which improves decision-making speed and quality. 

What makes them even more powerful is when they’re trained on custom data from within the company, like operational logs or internal manuals. This customization helps reduce errors and ensures that the automation is specific to the company’s needs. LLMs can seamlessly connect with existing enterprise systems, like CRMs or ERPs, making tasks like report generation or predictive maintenance faster and more precise. But while LLMs can do a lot, the human-in-the-loop (HITL) is still really important. For more complex, sensitive, or unclear tasks, humans step in to review the AI’s output, correct mistakes, and ensure everything stays on track. In systems where LLMs are acting as agents, people oversee the AI, handle exceptions, and fine-tune the models, ensuring that decisions are not just automated but also ethical and aligned with the company’s values.  

Can you demonstrate with real-world examples where agentic AI has significantly driven productivity improvements for organizations, especially in streamlining repetitive or data-heavy tasks? 

Agentic AI is bringing autonomy, adaptability, and real-time decision-making into the core of businesses, leading to substantial productivity improvements. One prominent use case is in handling e-commerce customer inquiries. Companies like Shopify are leveraging agentic AI to automate customer support interactions, allowing for rapid responses to inquiries and resolution of issues without needing extensive human intervention.

Another noteworthy application is in automated financial document curation. Financial services firms utilize agentic AI to streamline the extraction and analysis of data from financial documents, significantly reducing the time it takes for legal and financial professionals to review and compile information, thereby increasing overall efficiency.

Moreover, agentic AI can also enhance content generation at scale. It is now commonplace for consumer goods companies to deploy a crew of AI agents that can create and manage high volumes of content effectively. This allows businesses to maintain consistent messaging across channels while significantly reducing the manpower and time traditionally required for content production.

These real-world applications exemplify how agentic AI is transforming industries by streamlining repetitive, data-heavy tasks and enabling organizations to operate more effectively and efficiently.

How do you envision the collaboration between employees and agentic AI evolving, especially in terms of empowering employees to focus on more strategic tasks while AI handles the operational side? 

Agentic AI is revolutionizing workforce dynamics and reshaping our vision of the future workplace. Companies that are developing or implementing agentic AI consistently report productivity gains, time savings, and enhanced creative potential. By alleviating backlogs and handling redundant tasks, agentic AI enables employees to concentrate on higher-value activities that leverage human skills, creativity, and emotional intelligence. This collaboration between humans and AI not only enhances productivity but also fosters a more engaging and innovative work environment.

However, it is essential to recognize that, despite its high level of autonomy, the success of agentic AI relies on a balance between independence and human oversight. While these systems are capable of performing tasks autonomously, human involvement is crucial for ensuring alignment with organizational goals and ethical standards. There is a risk that the full automation of certain job functions could eliminate entry-level positions, leading to a workforce gap where fewer individuals gain the foundational experience essential for advancing to higher roles in the future. 

Therefore, it is vital to leverage agentic AI in a way that reduces workloads without undermining the skill development of employees. By striking this balance, organizations can ensure that the benefits of AI enhance rather than hinder the growth and capabilities of their workforce. 

What challenges do organizations face when implementing agentic AI, and how can they overcome these hurdles to ensure successful integration and long-term productivity gains – can you suggest few examples, particularly how mid-size enterprises can embrace agentic AI? 

Organizations face several challenges when implementing agentic AI, including technological, organizational, and cultural hurdles. One common issue is the balance between rapid deployment and the need for tailored solutions. For example, while many companies—such as several noted in Deloitte’s findings—prefer to purchase ready-made AI solutions for a quicker rollout, this approach can limit customization and long-term differentiation. Relying too heavily on external vendors may result in a system that does not perfectly align with an organization’s unique operational needs.

Mid-size enterprises often encounter additional obstacles like limited budgets, a scarcity of in-house AI experts, and inconsistent or poor-quality data. Resistance to change is also widespread, with employees sometimes feeling threatened by new technologies that can seem to overhaul traditional roles and workflows. Moreover, navigating regulatory and compliance issues can be daunting, especially when integrating AI into core business functions.

To overcome these challenges and achieve sustained productivity gains, businesses should consider these strategies:

• Adopt a phased implementation approach: Rather than a sweeping, company-wide rollout, start with pilot projects that address specific pain points. For instance, a mid-size e-commerce company could first deploy an AI-powered chatbot to handle customer inquiries efficiently, gather data on its performance, and then gradually expand to other functions. 
• Invest in skill development and data integrity: Upskilling the existing workforce is critical. Companies like Shopify have demonstrated success by combining technical training for their staff with robust data governance practices. This not only optimizes the agentic AI implementation but also ensures that employees remain engaged and capable of leveraging new tools to drive further innovation.
• Maintain human oversight and ethical governance: Even though agentic AI is built to operate autonomously, human oversight remains essential. Organizations can set up dedicated AI governance committees to ensure that the technology aligns with business goals and ethical standards. For example, a finance firm might use agentic AI for automated financial document curation, but then review the outputs through human experts to verify accuracy and compliance with regulations.
• Enable flexible integration with existing systems: Successful agentic AI systems are those that integrate seamlessly with current workflows. A content-driven company might deploy a crew of AI agents to generate scalable, high-quality content across multiple channels while relying on human editors to add nuance and context—balancing automation with creative oversight.