Agentic AI and the Future of Omnichannel Retail: Insights from AgileIntel

In 2024, global e-commerce sales surpassed US$6.5 trillion; customers now interact with brands across an average of six touchpoints before purchasing. Yet, despite this omnichannel engagement, 70% of shoppers report inconsistent experiences across online and offline platforms. This highlights a significant challenge for retailers: delivering seamless, personalised experiences at scale. Agentic AI offers a transformative solution, allowing systems to make autonomous decisions, adapt to customer behaviour, and optimise operations in real-time. 

Currently, 73% of top-performing retailers utilise autonomous AI systems for core operations, and the retail agentic AI market is expected to reach US$40.5 billion by 2025. 

Agentic AI redefines omnichannel retail by enabling autonomous, goal-driven systems that sense, decide, and act in real time. Unlike traditional AI, which adheres to static rules, agentic AI orchestrates complex workflows across marketing, commerce, and customer service, transforming fragmented touchpoints into a unified, intelligent ecosystem. 

How Leading Retailers Are Using Agentic AI: Personalised Shopping Assistants 

E-commerce leaders like Amazon, based in Seattle, enhance customer experiences through AI-powered shopping assistants. These systems analyse browsing history, cart behaviour, and past purchases to provide tailored product recommendations. By understanding individual preferences, AI agents guide customers towards products they will most likely purchase. This personalised engagement boosts conversion rates and strengthens brand loyalty. 

Shopify reports that stores employing AI-driven personalisation experience a 25% increase in average order value and a 19% reduction in returns. BÉIS, a Shopify merchant based in Los Angeles, utilised AI to target customers during peak buying periods, achieving double-digit growth through behaviour-based personalisation. Sephora's Virtual Artist, developed by the Paris-based beauty retailer, uses AI to analyse skin tone, recommend products, and suggest complete beauty routines, creating a digital consultant experience that fosters long-term loyalty and enhances online engagement. 

Dynamic Pricing Strategies 

Fashion retailer Zalando, headquartered in Berlin, employs agentic AI to implement dynamic pricing models. The system continuously monitors sales trends, competitor prices, and inventory levels, adjusting prices in real time to maximise revenue. This strategy allows for rapid responses to market shifts while maintaining competitiveness. AI-powered dynamic pricing ensures that products remain attractive, balancing profitability with customer satisfaction.  

Starbucks adopts a similar approach to personalisation in pricing and promotions. Its AI-driven marketing platform customises offers for over 16 million users, generating US$2.56 billion in annual mobile order revenue. By considering variables such as purchase history, location, and weather, the system continuously learns and adapts recommendations in real time rather than relying on static rules. 

Intelligent Inventory Management 

Inventory management remains one of retail's most complex challenges. Agentic AI revolutionises this area through automation and predictive analytics, minimising inefficiencies and ensuring product availability. 

Fashion retailer H&M leverages AI to analyse social media trends and regional preferences, accurately predicting fashion demand. The company reports a 30% reduction in overstock and a 40% decrease in stockouts, demonstrating how predictive AI models can align inventory with consumer demand while reducing waste.  Kroger, a U.S.-based grocery retailer, uses AI-driven predictive inventory systems and autonomous shelf sensors to monitor stock levels, identify replenishment needs, and optimise supply chain operations. These AI applications improve product availability, reduce inventory costs, and support more responsive and efficient store operations.

Seamless Omnichannel Fulfilment 

Agentic AI also enhances fulfilment by synchronising digital and physical operations. AI systems can track inventory across warehouses, stores, and online platforms, ensuring accurate visibility and faster delivery. This integration allows customers to enjoy consistent home delivery experiences or online in-store pickup. 

Retailers employing agentic AI in logistics benefit from reduced shipping times, lower last-mile costs, and fewer order inaccuracies. The technology's ability to adapt quickly enables retailers to manage seasonal peaks more effectively, ensuring high service quality even during demand surges. 

Proactive Customer Service 

Modern shoppers expect timely and proactive support across all touchpoints. Agentic AI can identify potential issues, such as delayed shipments or product dissatisfaction, and act before the customer reaches out. 

Sephora's AI chat systems proactively assist customers with order updates and personalised suggestions, while Starbucks' platform tailors real-time messages based on customer behaviour. These interventions enhance customer satisfaction and alleviate pressure on service teams. By combining predictive analytics with conversational intelligence, retailers can transform service from reactive to proactive, turning potential frustrations into brand-building opportunities. 

Implementing Agentic AI: A Strategic Approach 

Adopting agentic AI requires a clear, structured roadmap. Retailers should identify key areas where AI can create a measurable impact, such as personalised marketing, inventory optimisation, and service automation. 

Building the proper data infrastructure is crucial. Retailers need clean, integrated datasets that feed into AI models capable of learning autonomously and making context-aware decisions. Training AI systems with diverse, high-quality data helps minimise bias and ensures recommendations remain accurate and inclusive. 

Clear objectives keep AI deployment focused. Whether the aim is to improve conversion rates, increase retention, or streamline logistics, measurable KPIs are essential to track ROI and refine models over time. 

Ethical governance must accompany technological advancements. Retailers should communicate how data is collected and used, ensure transparency in AI-driven decisions, and implement safeguards against bias. Responsible use of AI builds trust and positions the brand as a reliable market leader. 

Challenges and Considerations 

Despite its promise, implementing agentic AI presents challenges. Data security and privacy compliance remain top priorities, particularly as retailers handle increasing volumes of customer information. Integrating AI into legacy systems may require significant infrastructure and investment in staff training. 

Moreover, human oversight remains vital. While agentic AI can make autonomous decisions, humans must guide its strategy, interpret nuanced outcomes, and uphold ethical standards. Successful adoption relies on the synergy between machine precision and human judgment. 

Conclusion: Redefining the Future of Retail 

Agentic AI is redefining how retailers operate by driving personalisation, predictive decision-making, and operational agility at scale. From Kroger’s AI-driven inventory systems to Sephora’s AI-powered virtual consultant, the technology is reshaping every layer of the customer journey. Retailers that strategically invest in agentic AI can create seamless, adaptive, and data-driven omnichannel experiences while achieving measurable gains in efficiency and profitability.

The next phase of retail transformation will belong to businesses that blend intelligent autonomy with human insight, creating ecosystems that respond to customers and anticipate their needs.