The Future of Retail Isn’t Omni-Channel. It’s Agentic.
For nearly two decades, retailers have been perfecting omni-channel. The strategy was clear: integrate physical stores, e-commerce, mobile apps, loyalty programs, and supply chain visibility into a seamless experience. The customer would move fluidly between touchpoints, and the retailer’s job was to reduce friction at every step.
That model assumed something fundamental. It assumed the customer would do the navigating.
Recently, Loblaw signalled something more consequential than another digital enhancement. The company expanded AI-enabled shopping experiences that allow customers to plan meals, generate grocery lists, and complete purchases directly inside conversational systems such as ChatGPT and Google’s Gemini. In parallel, it has accelerated internal AI deployment across merchandising, forecasting, and supply chain operations.
On the surface, this looks like another chapter in retail digitization. In reality, it may mark the beginning of a structural shift in how commerce is orchestrated.
In omni-channel retail, the consumer searches, compares, evaluates, and selects. The retailer competes for attention, optimizes conversion funnels, and invests in interface design. In agent-mediated commerce, the AI system performs much of that navigation. It interprets intent, narrows options, ranks alternatives, and increasingly recommends or even executes the transaction.
The control point moves.
Retail has long competed at the storefront. In an agentic future, the storefront may no longer be the front door. Discovery begins inside algorithmic systems that sit upstream of brand websites and mobile apps. The retailer’s digital properties become fulfillment layers rather than primary arenas of persuasion.
This changes the nature of competition. The question is no longer simply how compelling your brand appears to a human shopper. The question becomes whether your products, pricing, inventory, and data structures are intelligible and attractive to the algorithms mediating choice.
The AI does not respond to mood boards or advertising slogans. It responds to signal clarity. Clean product taxonomies. Structured data. Transparent pricing logic. Reliable availability. Predictable fulfillment. In a world where agents compare options in milliseconds, operational precision becomes visible in a new way.
This is where the story becomes strategically interesting.
If agents increasingly filter and rank options, retailers are no longer competing solely for human preference. They are competing for algorithmic preference. That demands a different architecture. API-first systems. Real-time inventory intelligence. Data models designed not just for internal reporting but for external machine consumption. Loyalty mechanisms that are legible to recommendation systems, not only to marketing teams.
For some retailers, this will compress margins. If AI agents optimize aggressively for price and convenience, commoditization accelerates. For others, it opens a path to differentiation through orchestration. Private label ecosystems, predictive replenishment models, subscription logic, and tightly integrated fulfillment networks can become signals that agents learn to trust and prioritize.
The physical store does not disappear in this future. It evolves. Stores become fulfillment nodes, experiential anchors, and logistical advantages in a network optimized upstream by AI. The discovery moment shifts earlier in the journey, but the execution still depends on operational excellence on the ground.
What Loblaw has done is not simply experiment with a new channel. It has stepped into the reality that commerce is moving from interface-centric to agent-centric. That distinction matters. Omni-channel was about multiplying touchpoints. Agentic commerce is about orchestrating intelligence.
The deeper implication for all retailers is this: strategy can no longer be framed as a marketing upgrade layered on top of existing systems. It is an architectural decision. If your organization is not designed to be consumed by machines, it will be filtered by machines.
For years, executives have debated whether AI will transform retail. That question is now obsolete. The more relevant question is whether retailers will shape how AI systems represent them, or whether they will passively accept the logic imposed by external platforms.
This is not a marginal shift. It is a redefinition of where value is created and where power resides. The next decade of retail will not be won by the company with the most elegant app. It will be won by the company that understands that algorithms are becoming storefronts, and that intelligence, not interface, is the new terrain of competition.
Michael,
I really love your morning musings!
Your line, “the control point moves,” landed for me instantly, because education is quietly going through the same structural shift you describe in retail. For years, many schools have been chasing an omni channel version of schooling. A portal here, a learning management system there, a wellbeing check in another platform, a dozen dashboards, a handful of PDFs, and a heroic amount of human glue holding it all together. The promise is seamlessness. The reality is that the human does the navigating.
In your retail framing, omni channel assumes the customer will search, compare, evaluate, and decide across touchpoints. In schools, the equivalent customer is everyone: students trying to understand expectations, parents trying to interpret progress, teachers trying to see the whole child, leaders trying to make sense of patterns. We have multiplied touchpoints and called it modern. But the deeper problem is not the number of channels, it is that the cognitive load of stitching them together is still sitting on humans who already have full plates.
Agentic thinking flips that. Instead of asking teachers, students, and families to become expert navigators of systems, the system becomes an expert navigator of intent.
That is where your “compete for algorithmic preference” idea gets especially interesting in an education context. Most schools are not yet machine legible. We have lots of data, but it is often messy, duplicated, trapped in PDFs, inconsistently labeled, or divorced from context. In retail, the AI does not respond to mood boards, it responds to clean taxonomies and signal clarity. In schools, the agent will not respond to visionary slogans about whole child learning if the underlying student record is fragmented and contradictory.
So here is my education analogy to your retail storefront moving upstream. The new “front door” to learning is not the LMS. It is not the report card. It is not even the classroom website. The front door is the interpretation layer, the intelligence that can translate a student’s history, current performance, learning preferences, wellbeing indicators, and next best supports into something a teacher can act on in seconds.
In an agentic education future, the differentiator is not who has the slickest parent portal. It is who has built the cleanest, most coherent student data ecosystem, with nuance and ethics baked in.
That includes at least three things.
First, access to a fuller picture of the student. Not surveillance, not creepiness, not replacing teacher judgment. More like finally giving educators a well organized cockpit view instead of a glove compartment full of crumpled maps. When data is cleaned, contextualized, and connected, teachers can see patterns over time, across courses, across learning skill development, and across supports, without having to hunt.
Second, reporting evolves. Today’s reporting is often periodic, lagging, and constrained by formats that were built for paper workflows. An agentic layer could generate clearer narratives about learning growth, not just achievement snapshots, and could surface strengths, emerging risks, and next steps in language that families actually understand. The report becomes less of a document and more of an ongoing interpretive service.
Third, optimization and prediction become possible, but only if we stay humble. The point is not to predict futures like a fortune teller. The point is to catch preventable problems earlier, and to personalize supports and extensions with better timing. The agent might flag that a student’s writing fluency dipped after a schedule change, or that a particular feedback cycle reliably produces growth for that learner, or that a workload cluster across courses is likely to create stress in the next two weeks. That is not replacing the human in the loop. It is giving the human better headlights.
And just like retail, this is not a marketing upgrade. It is an architectural decision. Schools that treat AI as a bolt on tool will get bolt on results. Schools that treat AI as an operating layer, grounded in clean data structures, clear ontologies, and trustworthy governance, will be able to orchestrate support and learning in ways that feel almost unfair compared to the current state of play.
Your post also reminded me why I enjoy your writing. You keep your examples rooted in sectors outside of education, which is ironically the most useful way to help educators think. When you talk about commerce, supply chains, and consumer platforms, you force me to translate the pattern, not copy the tactic. That translation step is where the best ideas appear.
Thanks for the provocation, again. You are not just predicting a future, you are pointing at where power relocates. In schools, I think the same thing is happening. Algorithms become the new interpretation layer, and the institutions that can make themselves legible, ethical, and coherent to that layer will be the ones that truly evolve. - JM