Chasing Agentic Windmills

With retailers seduced by the lustrous orb of Agentic AI, we thought it would be useful to clear the air with a broad definition of the concept and describe how brands apply (or are building applications for) the technologies. At best, Agentic AI is a system’s ultimate thinking tool and takes on the aptitudes of LLM-powered AI in retail.

• Customer behavior
• Inventory flows
• Supply chain rhythms
• Pricing strategy
• Store operations
• Security oversight
• Digital interactions
• Loyalty patterns
• Customer support
• Weather activity impacts

Agentic AI Accelerates Retail Operations

Agentic AI upgrades its AI ancestors as it performs tasks autonomously within the retail funnel. New models blend with CRM and POS systems from discovery and checkout to post-purchase support. Agentic AI is still evolving and always improving as it operates at a higher level tackling these complicated tasks.

• Influencing discovery
• Optimizing inventory
• Mitigating supply chain disruptions
• Applying dynamic pricing
• Facilitating customer purchase activity capture, checkout, and attribution
• Leveraging chatbots built from large language models to interpret user input and generate adaptive responses
• Coordinating staff scheduling
• Identifying and mitigating dark pattern activity and crime
• Identifying points of failure
• Customizing loyalty incentives
• Resolving support issues
• Generating catalog, marketing, and advertising content
• Adapting based on outcomes
• Mitigating weather disruption costs

Omnichannel Evolution

The omnichannel customer experience stitched together stores, ecommerce, mobile, social, marketplaces, and call centers, but the customer moved among these channels as discrete silos. With Agentic AI, the promise is that the channels will dissolve. This near-future experience is emerging as a novel retail ecosystem that will treat every touchpoint as part of a unified system.

• The customer journey is continuous and customized
• Context follows the customer everywhere
• The brand behaves like a single, coordinated organism, not a set of channels (your call is never transferred to another representative)
• Data, identity, inventory, and personalization function across devices, whether online, in-store, or both simultaneously
• A website can be a living store with the thrill of discovery, and a store can be a living website that directs you to what you are searching for
• The experience adapts to the customer, not the other way around

Metachannel retail (no connection to the Big 7 tech company) is an apt reengineering of omnichannel. Metachannel reflects a quasi-scientific interpretation of systemic metapsychology, which applies concepts of self-examination (of a company, not a personality), interdependence (within the organization and between trusted partners), systems-level behavior, and self-repair or mitigation. It is a retail channel that learns from itself, tests and evaluates alternatives, executes decisions, and automatically improves.

Operations and Aspirations

While the pace and intensity of adoption may vary, an AI transformation is underway across the retail industry. Granted, Agentic AI is the latest hot topic, and there is evidence of early stages of early adoption. Plans and aspirations outweigh practical applications in retail. However, there are a few pioneers.

• LVMH executives Gonzague de Pirey and Soumia Hadjali have announced an initiative called AI for All, which spans the company’s portfolio of Maisons. The aim of the program is to elevate the user experience across all touchpoints in the system, associates, management, clients, artisans, and operations, through AI. De Pirey emphasizes the importance of an AI that is aligned with the brand’s culture as the company implements what it calls “quiet AI.” He stresses, “The technology should be everywhere, and invisible.” Each Maison at LVMH develops unique AI activations, but the company shares best practices and technology to scale the implementation across the organization. An AI activation at Dior will look very different than one at Sephora, but many of the systems interconnect.
• At Louis Vuitton, Soumia Hadjali, Global Vice President, Client Development & Digital, applies AI to leverage LV’s many cultural initiatives. “Our digital concierge is both deeply personal and context aware. It will know how our clients shop; not just what they buy, but who they are, and where they live.” She continues, “Louis Vuitton has cafés, restaurants, and exhibitions. We can organize a private table at our restaurant when it is hard to get a table. We can book tickets for an exhibition. We can become part of our client’s life.”
• Target is on an accelerated trajectory. CIO Prat Vemana describes the retailer’s partnership with OpenAI. He is clear about the retailer’s AI ambitions: “Target has moved from using AI to running on AI.” The new OpenAI integration includes many Agentic AI functions aimed at efficiency both online and in-store. Customer service centers and associates have gained two Agentic AI superpowers via the Agent Assist and Store Companion devices developed with OpenAI. The devices allow price matching and can start returns on the spot in-store. Vemana adds, “Target is not a marketplace; it is a curated retailer. The process of vetting vendors used to take thousands of hours; now we get it done in minutes.”
• Albertsons’ Agentic AI shopping assistant (which doesn’t seem to have a very catchy name) is jockeying for relevance as grocery options multiply. In a field test AI was challenged with a complicated prompt that required analysis from diverse streams of data and information. It delivered a menu designed to satisfy a group comprising a vegan, lactose-intolerant, peanut-sensitive, and gluten-free group of guests. The bot produced a list of recipes from which to choose and eventually filled the virtual shopping cart with the necessary ingredients, including a few private-label items.
• Ralph Lauren’s Ask Ralph chatbot is a more primitive version of Agentic AI that shares sophisticated fashion advice from a virtual facsimile of the fashion icon himself. Chief Branding and Innovation Officer, David Lauren, was an early adopter of OpenAI’s ChatGPT through the brand’s tech integration with Microsoft Azure. Understandably, Lauren is an advocate of the shoppable, conversational version of his Dad steering potential clients toward a sale.

Eyes Wide Shut

AI’s stealth infusion into everything has been stunning. User adoption, exploration, and exploitation are fully turbocharged. Both business and society (including consumers) have had little time to consider the implications of these emerging technological shifts as the world races to retool and keep up with the pace of change. The shiny object that is AI, and the even shinier possibilities promised by Agentic, are alluring, but there are vexing implications that can’t be ignored.

Agentic AI reflects engineering advancements that continue to encroach on unique human capabilities. In a capitalist economy, public corporations beholden to shareholders will choose efficiency over the workforce. These will not be layoffs; It is unlikely that jobs lost to AI will ever return. The economic impact of job losses writ large will impact society, culture, governance, and naturally slam into the retail industry.

The computing power needed to support the massive data centers that power AI currently, compounded by the exponentially increasing energy demands of the more complex Agentic AI, will strain aging energy grids in the U.S. and elsewhere. High power demands drive up electricity costs, particularly in the U.S., as renewables stall. Obviously, higher electricity costs reduce discretionary spending for consumers across many income segments.

Agentic AI requires deep, personal data sharing with service providers. For a consumer-facing Agentic agent to be effective, trust is critical. Consumers will be required to share personal and financial information, including personal characteristics (age, weight and size), location data, biometric information for identity verification, routines, habits, pricing sensitivity and logic, and more. In other words, Agentic AI needs to understand who you are, what you want, how you behave, your constraints, and which actions you are comfortable authorizing the agent to perform. It goes without saying that undisclosed data sharing, selling, leaks, and security breaches will shatter the trust between the customer and the brand.

AI Axiom

As retail’s AI transformation marches forward, it should be approached thoughtfully.  Brands and retailers need to anchor every AI decision in their culture, identity, and strategic intent. Ask Ralph, LVMH’s AI for All, and other luxury AI applications should deepen the bespoke, high-touch relationship between client and brand; anything that feels like a generic chatbot breaks the spell. For Target, where speed, scale, and efficiency fulfill the brand’s AI intent, intelligent automation that delivers faster, more personalized service is exactly on‑brand. And when operational AI unlocks cost savings, passing those savings on to shoppers reinforces Target’s value-driven identity. The rule is simple: Innovation only works when it authentically amplifies the brand and delivers meaningful benefit to the one stakeholder who matters most, the customer.