This article originally appeared on Express Computer.

Gartner has named Agentic AI a top tech trend for 2025. Salesforce is set to transform business automation with its AI-driven agents, and Nvidia has announced five groundbreaking AI agents poised to redefine industries. These are not isolated developments; they represent a larger movement toward self-governing systems that are no longer just assistants but autonomous decision-makers.

What Does ‘Agentic’ Really Mean?

For years, analytics in business has been largely reactive, offering insights on past data, and assisting or supporting decision-making going forward. Agentic AI represents a fundamental shift. Unlike traditional automation, which follows predefined rules, these AI systems evolve continuously, learning from internal data and interactions, market trends, and operational data. They have the ability to reason. And they have the ability to ‘predict’ what may happen. Once the decision is taken, they also automate the execution of such tasks.  This is not just a technological evolution—it is a paradigm shift in enterprise automation.

Agentic AI can be understood in two key forms. Generative AI agents create new content, whether in the form of text, images, or designs, and have become a powerful tool in creatives and customer engagement processes. Predictive AI agents, on the other hand, function as decision-makers, analysing patterns, predicting what will happen, and automate entire workflows.

However, the success of these AI-driven systems depends on one fundamental element – data.

There Are No Agents Without Data

While many companies rush to adopt AI, few recognise that an AI agent is only as effective as the data it is trained on. Brands must first ensure their data infrastructure is unified, cleaned, and continuously monitored. If the labelling of the data is not perfect, the agent will not work. The key to unlocking the true potential of Agentic AI lies in robust data labeling, constant pipeline monitoring, and a unified data ecosystem.

Agentic AI in eCommerce

Nowhere is the impact of Agentic AI more evident than in eCommerce. From marketing and personalisation to operational agility, AI-driven agents are transforming how brands engage with customers and manage operations. In marketing, AI agents are no longer just analysing past performance; they are dynamically optimising campaigns in real time. Instead of relying on predefined strategies, these agents learn from consumer behavior, modifying messaging, and reallocating budgets instantly to maximise returns. Traditional marketing cycles that once took weeks to refine are now being optimised every second.

The shift is equally transformative in customer experience. Personalisation has long been a key differentiator in eCommerce, but Agentic AI takes it to an entirely new level. Unlike conventional AI models that recommend products based on past purchases, AI agents actively analyse browsing behavior, inventory trends, and external market signals to craft highly individualised shopping experiences. They do not just suggest products; they anticipate demand, adjust pricing dynamically, and trigger personalised promotions before the customer even realises they need them. For instance, during India’s festive shopping season, an AI agent can not only recommend culturally relevant outfits but also dynamically adjust discounts and stock levels in response to demand spikes.

Beyond customer engagement, Agentic AI is redefining operational agility. Traditional supply chain management is fraught with inefficiencies—delays, overstocking, and demand fluctuations often lead to lost revenue and increased costs. AI agents are changing this by autonomously predicting demand shifts, adjusting inventory orders, and rerouting shipments in real time. They minimise waste, reduce excess inventory, and ensure that businesses maintain seamless operations even during unpredictable market conditions.

What’s Next for Agentic AI in Business?

The real question is not whether businesses should adopt Agentic AI but how they can do so effectively. This is not a fleeting trend; it is the foundation of the next phase of enterprise automation. However, deploying AI agents without a clear strategy will yield limited results. Companies must first focus on strengthening their data infrastructure, integrating AI ethically into decision-making processes, and fostering a culture of continuous learning. AI systems are not static—they evolve. Brands that fail to invest in ongoing optimisation will fall behind, while those that proactively refine their AI strategies will unlock long-term competitive advantages.

Agentic AI is leveling the playing field for small and medium enterprises. In the past, AI-driven automation was largely accessible to tech giants with deep pockets. Today, businesses of all sizes can leverage AI agents to enhance customer engagement, streamline operations, and scale efficiently. The focus is shifting from competing on price alone to competing on experience, speed, and intelligence.

The eCommerce industry is entering a new era. This is not just another wave of digital transformation—it is the dawn of intelligent, autonomous, and adaptable enterprise operations. The question is no longer whether AI can assist businesses—it is whether businesses are ready to let AI act.

Authored By Prem Bhatia, Co-Founder and CEO, of Graas.