
For years, Artificial Intelligence (AI) systems have assisted humans by automating repetitive tasks and providing insights based on data. The industry is now gearing up for a shift in technology where machines take an active role in decision-making processes. This is where Agentic AI comes in, enabling systems to independently plan, decide, and act to achieve specific goals.
Agentic AI systems are designed to imitate the decision-making processes of humans. They assess goals, analyse environments, and take actions without requiring constant human oversight. These systems rely on complex algorithms and real-time data to function. For example, they can evaluate supply chain operations, identify bottlenecks, and implement solutions without human intervention. This approach contrasts with traditional AI, which requires manual input to adapt to changes.

Applications of Agentic AI in Business

- Project Management:
One area where agentic AI holds promise is in project management. By integrating with existing enterprise software, these systems can allocate resources, set deadlines, and monitor progress. The result is a more streamlined workflow that adapts to changing priorities. Businesses benefit from reduced delays and better resource utilisation.
- Retail and Supply Chain:
In retail, agentic AI can forecast demand, manage inventory, and even coordinate logistics. These capabilities align with the growing need for agility in supply chain management.
- Customer Service:
Customer service is another domain undergoing transformation. Agentic AI systems can personalise interactions, resolve queries, and recommend products. Unlike chatbots that operate on scripted responses, agentic systems learn from interactions, improving with time. This capability enhances customer satisfaction while reducing the workload for human agents.
- Finance:
The financial sector is also exploring agentic AI. These systems can analyse market trends, execute trades, and manage portfolios autonomously. They consider risk factors, historical data, and current events, making decisions that align with investment goals. Such applications promise to optimise financial operations and increase returns.
Challenges in Implementing Agentic AI
- Data Privacy and Ethics:
Implementing agentic AI is not without challenges. Businesses must address issues related to data privacy, system reliability, and ethical considerations. For instance, autonomous decision-making raises questions about accountability. If an AI system makes a poor decision, who is responsible? Addressing such concerns requires robust governance frameworks and transparent operational protocols.
- Technological Infrastructure:
The technological infrastructure supporting agentic AI is another area requiring attention. High computational power, advanced machine learning models, and reliable data sources are essential. Businesses must invest in these areas to unlock the full potential of agentic AI.
Collaboration between developers, businesses, and policymakers is key to overcoming these challenges. Developers need to focus on creating systems that are transparent and explainable. Businesses should adopt strategies that align AI goals with organisational objectives. Policymakers must establish guidelines that promote innovation while ensuring public safety and ethical use.
At the 3rd Elets Digital Native Summit, Gaurav Duggal, the Senior Vice President & Head of Data Analytics at Jio Platform Limited said, “I feel GenAI, along with Agentic AI, is going to revolutionise industries across the board. We will see a significant reduction in redundant work, replaced by generative content.”
Businesses are starting to see how agentic AI can do more than just improve technology. These systems can change job roles, boost efficiency, and help companies handle tough problems. To use this technology well, companies need to plan carefully, solve ethical issues, and build strong systems to support it. While it might disrupt the way things work now, it also opens up chances to grow and improve. Companies that start learning and using agentic AI today will be better prepared for a future where technology makes decisions and takes action on its own. This isn’t just about new tools; it’s about changing how work gets done.