As a retail advisor specializing in AI, I’ve observed countless retailers struggle with the same fundamental challenge: balancing exceptional customer experiences with operational efficiency. For years, the industry has relied on incremental improvements—website optimizations, basic personalization tactics, and other piecemeal solutions to increase cart values and reduce abandonment. While these changes have driven continuous gains, we’re now standing at the precipice of something far more transformative in 2025: the mainstream adoption of AI agents.
E-commerce is notoriously challenging—razor-thin margins, fierce competition, and ever-rising customer expectations create a perfect storm that demands excellence on both customer-facing and operational fronts. Experimental AI approaches that focus on “business results” masquerading as customer experience improvements are no longer sufficient. It’s time to truly put the customer first, and that’s where AI agents will be a game changer.
AI agents’ ability to genuinely mimic human behavior is changing the game for many industries, but especially retail. By combining advanced large language models (LLMs) with multimodal capabilities (processing text, images, audio, and video), these systems can function with a level of sophistication that extends far beyond customer service interactions. From revolutionizing returns management to optimizing inventory and streamlining operations, these AI solutions are transforming how retailers approach fundamental business challenges.
Not Just Another Chatbot
Let me be clear: the frustrating, limited chatbots that have populated retail websites (or any consumer-facing website for that matter) for years are a thing of the past.
AI agents represent something fundamentally different—they’re not constrained by predefined questions and answers. Instead, they understand context and intent to dynamically engage with customers, embodying your brand voice and values while providing personalized assistance at scale.
This is the realization of what retailers have long aspired to create: digital sales assistants that can replicate the best aspects of in-store shopping experiences for millions of customers simultaneously. With real-time access to inventory, fulfillment data, and customer history, AI agents can provide contextualized recommendations that drive conversion while reducing friction. The evolution of search was a prominent theme at recent industry events like Shoptalk. Shopper expectations are moving beyond traditional product search and recommendation engines to image-based and eventually “agentic search” capabilities.
Consider this scenario: A customer inputs, “I’m attending a beach wedding and love Jaclyn’s style from White Lotus—what should I wear?” Rather than returning a simple list of products, an AI agent can curate complete outfit options with complementary accessories, considering the occasion, style reference, and the customer’s preferences and purchase history.
This capability taps into cultural moments—like the fashion influence of TikTok and popular shows like White Lotus—and translates them into personalized shopping experiences that traditional search simply cannot deliver. The agent understands context, interprets style references, and assembles cohesive recommendations that span multiple product categories.
The Human Element: Critical for Success
Industry leaders like Salesforce describe AI agents as part of the “third wave of the AI revolution,” while Deloitte projects that 25% of businesses using generative AI will adopt AI agents by the end of 2025, climbing to 50% by 2027. These predictions underscore the momentum building behind this technology.
However, I cannot emphasize enough that successful implementation requires a thoughtful approach to human oversight. The retailers who will gain the most from AI agents are those who view them as augmentations to their existing workforce, not replacements.
This humans-in-the-loop (HITL) approach recognizes that even the most sophisticated AI requires human expertise throughout its lifecycle:
- Development and Training: Humans define the operational scope based on real business objectives and customer needs along with the fundamental training requirements.. Much like teaching a child, AI agents need explicit instruction on steps humans take for granted. Subject matter experts must break down processes into minute details—such as how format shipping addresses for their specific order management system or which database contains specific information—that experienced staff navigate intuitively.
- Deployment and Monitoring: Human oversight validates that AI agents are accurately navigating complex situations like returns, knowing when to make exceptions for customer satisfaction versus fraudulent returns.
- Continuous Improvement: Human experts provide feedback to refine AI performance and ensure alignment with brand values and quality standards.
Take return processing as an example. Major retailers like Amazon and Walmart are already leveraging AI to enhance return experiences while maintaining policy compliance. The AI agent can handle routine returns efficiently, but human staff verify performance in complex cases to preserve brand integrity and customer relationships. Behind the scenes, these systems rely on institutional knowledge accumulated over years—knowledge that must be carefully translated for AI systems to function effectively.
Practical Applications Beyond Customer Service
While traditional AI has been making strides in data analysis across retail operations, truly agentic AI takes this a crucial step further by autonomously executing multi-system workflows. In inventory management, agentic AI doesn’t just predict stock requirements—it actively identifies when one location is running low on inventory and independently accesses logistics systems to initiate stock transfers from surplus locations. This represents the key distinction: where standard AI offers insights, agentic AI takes procedural action.
In supply chain operations, these agents don’t merely monitor logistics networks and suggest alternative routing—they directly execute rerouting decisions when disruptions occur, communicating with carriers, updating delivery schedules, and adjusting warehouse operations across multiple systems without human intervention. The merchandising function similarly benefits when agentic AI moves beyond pattern analysis to actually implementing product placement changes, triggering promotional adjustments, or modifying digital merchandising displays based on real-time performance data.
The retail landscape is seeing various implementations of this technology. For instance, Target and Home Depot have introduced in-store virtual assistants that arm employees with comprehensive information to enhance customer experiences. These implementations represent early steps toward the broader potential of agentic AI, where the true value emerges when these agents navigate independently between separate systems to complete end-to-end processes that previously required multiple human touchpoints.
Implementation Timeline and Expectations
For retailers considering AI agent implementation, a structured phased approach is recommended. The process should begin by identifying high-impact use cases—focusing on areas creating the most customer friction or operational inefficiency. While starting with specific, limited functions is prudent, architecture should be designed with broader future integration capabilities. Throughout implementation, prioritizing human oversight remains essential, with clear processes established for human experts to train, monitor, and continuously refine AI agent performance.
Following this approach, retailers can expect to deliver transformative experiences as early as the 2025 holiday shopping season—a traditionally high-pressure period where AI assistance can significantly enhance both customer satisfaction and operational performance metrics.
The Competitive Advantage
E-commerce platforms that strategically implement AI agents, focusing on specific problem-solving while maintaining appropriate human oversight, will secure significant competitive advantages. These include deeper personalization capabilities that move beyond basic customer segmentation to truly individualized experiences, and reduced friction through seamless interactions that eliminate barriers to purchase. Enhanced customer loyalty will develop through consistent, high-quality experiences that build emotional connections, all while improving operational efficiency through streamlined processes that enhance margins without compromising quality standards.
The retail landscape of 2025 and beyond will be defined by those who successfully harness AI agents as collaborative tools that extend human capabilities rather than replace them. The most successful implementations will be those that recognize that while AI can handle tremendous complexity, human insight remains essential for maintaining brand integrity and building authentic customer relationships.
For retailers ready to embrace this approach, the benefits will be substantial—not incremental improvements but transformative advances in how they engage customers and operate their businesses. The future of retail isn’t automated; it’s augmented by the powerful combination of AI capabilities and human expertise working in concert to deliver exceptional shopping experiences.
Read more about the future of Agentic AI in Retail and E-Commerce via Sama’s e-book which you can download here.
