Artificial intelligence (AI) has long been a transformative force in retail, from product recommendations to inventory management. But as AI capabilities evolve, a new concept is taking center stage: agentic AI. Unlike traditional AI models that rely on human input to function, agentic AI operates with greater autonomy, making decisions, initiating actions, and dynamically adapting to new information without constant oversight. This capability has profound implications for brick-and-mortar retailers, who are increasingly looking to bridge the gap between digital and in-store experiences.

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What Is Agentic AI?

To understand agentic AI’s significance, it’s important to distinguish it from AI agents. AI agents are specific programs designed to perform set tasks, like chatbots answering customer queries or recommendation engines suggesting products based on browsing history. Agentic AI, on the other hand, goes beyond predefined tasks by learning, adapting, and executing decisions in real time. Think of AI agents as helpful assistants following instructions, while agentic AI acts more like a proactive strategist who can anticipate needs and take action independently.

Forbes recently reported that while the term “agents” (or “agentic”) in the context of AI is sometimes overused to the point of becoming moot, there “seems to be an industry-wide consensus on the fact that unlike generative AI — which focuses on generating texts, images, videos, and audios at scale — AI agents are designed to take action, making decisions and executing tasks with increasing levels of autonomy.”

One of the key concerns surrounding this newest iteration of AI is its incredible energy demand. AI models, particularly those involved in real-time decision-making, require significant processing power. This means that companies that cannot optimize their AI infrastructure risk unsustainable operational costs. For agentic AI to truly find its stride at scale, it would have to be more energy-efficient, thus lowering operational costs and aligning with environmental goals. Nevertheless, it’s a critical conversation to be had: is agentic AI relevant to retail?

Agentic AI In Physical Stores

In brick-and-mortar retail, this technology is transforming the shopper journey. Today's consumers expect seamless, personalized experiences that combine digital convenience with in-store engagement. AI-powered technology helps achieve this by analyzing shopper behavior, optimizing store layouts, and predicting which products will perform best in specific locations. 

Imagine an AI-supported (and supportive) system that tracks foot traffic patterns, adjusts digital marketing displays in response to changing demand, and notifies staff when a high-value customer enters the store. These capabilities enable retailers to create a more dynamic and responsive shopping environment that meets customer expectations while driving higher sales. For instance, a McKinsey report found that retailers utilizing AI-driven personalization experienced sales increases of up to 10% and significantly boosted customer satisfaction.

In this report, McKinsey establishes AI-driven personalization as a key tenet for peak performance:

“[Category leaders] invest in rapid activation capabilities powered by advanced analytics. Leaders develop at-scale content creation and AI-driven decisioning capabilities so they can respond to customer signals in real-time. They leverage predictive analytics and models to determine what content and messages to serve which customers (for example, propensity models, or predictive next-best-action algorithms). They also establish robust measurement processes that track the impact of customer interventions and feed that information back to their systems and teams. These processes help them deliver the right content through the right channels at the right moments in a consumer’s journey.”

How Agentic AI Powers Omnichannel Retail

Agentic AI plays a crucial role in omnichannel retail. Retailers have long sought to unify online and offline shopping experiences, but the challenge lies in creating a truly connected ecosystem. With agentic AI, retailers can bridge this gap more effectively. For example, an AI system can analyze online search behavior, cross-reference it with in-store purchase patterns, and automatically adjust stock levels or promotional strategies accordingly. A shopper browsing a retailer’s website for running shoes might receive a personalized discount notification when they walk into a nearby store. 

AI-driven systems can predict peak shopping hours and allocate staff accordingly. This helps reduce wait times and improve customer satisfaction. For example, a case study from Walmart demonstrated that AI-driven demand forecasting reduced out-of-stock items by 30%, ensuring that customers found the products they needed when visiting stores.

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Traffic Data Is Essential For Innovation

For retailers, staying ahead of these innovations is no longer optional. Consumer expectations are shifting rapidly, and those who fail to adapt risk falling behind competitors who leverage AI-driven insights for better decision-making.

Accurate traffic data is invaluable for several reasons:

1. It enables better staff optimization. Retailers can ensure they have the right number of employees on the floor at the right times, improving customer service while controlling labor costs. 

2. It enhances demand forecasting. By analyzing historical foot traffic trends alongside external factors like weather and local events, AI can predict sales patterns more accurately, allowing retailers to manage inventory more effectively. 

3. These insights inform smarter merchandising decisions. Knowing where shoppers spend the most time enables retailers to position high-margin products strategically, increasing the likelihood of purchase.

However, these benefits hinge on one critical factor: the accuracy of the underlying data. AI models, no matter how advanced, can only be as effective as the information they are trained on. Data is fundamental to the performance of AI, but it also poses the biggest challenge for agentic AI. The effectiveness of AI agents relies heavily on the quality of their training data. Without high-quality, domain-specific data, they struggle to operate effectively in industry-specific environments.

“Our recent CDO report found that 43% of businesses cite data quality, completeness and readiness as their biggest obstacle in deploying AI initiatives,” said Amit Walia, CEO of Informatica, in a recent publication. He also emphasized that “without high-quality, domain-specific data, even the most advanced AI models will fall short.”

Inaccurate traffic counting, for example, leads to flawed predictions, inefficient staffing, and missed revenue opportunities. This is why investing in high-quality, in-store analytics solutions is essential. With precise store insights, retailers can ensure that their agentic AI systems are making the best possible decisions, ultimately driving growth and efficiency across their operations.

The future of retail depends on data-driven intelligence, and agentic AI represents the next step in this evolution. By adopting these technologies, retailers can create more adaptive, customer-centric experiences while also optimizing their operations in ways that were previously unimaginable. The key is to establish a strong foundation through accurate data collection and develop AI-driven strategies that improve both the shopping experience and business performance. In an industry with tight margins and intense competition, leveraging agentic AI is not just a competitive advantage; it is essential.

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