Assortments, POGs, and Pricing, What AI Could Change

Nothing in this post is confirmed Kroger strategy. None of it is a leaked plan, an internal memo, or a roadmap someone showed us. This is a look at where the technology already sitting inside retail category management is heading, and an honest guess about what that could mean for how suppliers work with Kroger over the next several years. Treat it as a heads up, not a headline.

Here is why it is worth thinking about now instead of later. Category management software built specifically to automate assortment, pricing, and planogram decisions is already live at retailers today, not in some distant future. Some of these platforms describe the ability to execute routine category decisions with limited human review. That is not the same as a Category Manager being replaced by a model. But it is a real signal about which parts of the job are becoming automatable, and which parts are not.

So let's walk through the areas where this could show up, one at a time, and think honestly about what it would mean for you if it does.

Assortment, Down to the Store Level

Assortment decisions have historically been made at the division level, sometimes at the enterprise level, occasionally with store clusters layered in for major format differences. That is a reasonable way to manage complexity when a human being is reviewing every SKU decision across dozens of divisions.

It is not the only way to do it if a model is doing the reviewing. AI-driven assortment tools can already cluster stores by actual demand behavior rather than geography or format alone, then generate a tailored SKU list for each cluster instead of a single division-wide plan. If this direction continues, the assortment your item earns in one store could look meaningfully different from the assortment it earns three miles away, based on data rather than a category review conversation.

What that could mean for you: a single "we got the authorizion" moment may matter less than it used to. The real outcome may increasingly be decided store cluster by store cluster, based on how your item actually performs in that specific demand profile, not on how well the initial pitch landed.

Pricing That Moves With Local Demand

Pricing tools already on the market use demand elasticity models to recommend price points by category, and in some cases by local market, updated far faster than a manual pricing review cycle could move. Kroger has talked publicly about price investment as a strategic priority, and 84.51 already sits on the kind of shopper data that makes localized pricing possible in theory.

If this direction plays out further, the list cost and retail conversation you have with your Category Manager could increasingly be a starting point for a model, not the final word. Your item's shelf price could vary more by market than it does today, based on real-time local elasticity data rather than a single national or divisional price point.

What that could mean for you: pricing conversations may shift from "what is the retail" to "what is the retail in this cluster, and why does the model think that is the right number." Coming prepared with your own elasticity story, not just your cost story, could become more valuable over time.

Take this a step further. If pricing models are already blending cost data, sales history, and elasticity, there is no real barrier to layering in operating costs on top of that, labor, shrink, distribution cost to a given store, everything that goes into the actual cost of doing business at that specific location. A model with all of that data could get remarkably close to a true store-level margin picture for your item, not just a category-level or division-level estimate. If that happens, "what is the retail" and "what is this item actually worth to us at this store" could become the same question, answered by the same system, in real time.

Planograms Built by a Model, Not a Planner

Planogram generation is one of the more mature use cases for AI in category management right now. Instead of a planner manually building shelf sets, software can generate a planogram from an approved assortment and space allocation, then adjust it automatically as sales data comes in.

If Kroger leans further into this kind of tooling, planogram resets could happen more frequently and with less manual lead time than the reset cycles suppliers are used to planning around. That cuts both ways. Faster resets could mean a faster path to more space if your item is performing. They could also mean less warning before a reset, and less room to make the case for placement before the model has already decided.

What that could mean for you: the sell-in window before a reset might get shorter. If planograms increasingly update based on rolling performance data rather than a fixed annual or seasonal calendar, showing up with strong, current velocity numbers could matter more than showing up with a well-timed pitch.

Store Mapping and Layout

This one is more speculative than the others, but the pieces are already in motion elsewhere in retail. AI-driven store clustering does not have to stop at product assortment. The same demand-pattern logic that decides which SKUs belong in a store could, in theory, extend to decisions about department adjacencies, aisle layout, and where entire categories sit relative to each other inside a store.

Nothing suggests Kroger is doing this today at scale. But if store-level demand data is already driving assortment and planogram decisions, layout and adjacency are a logical next step, not a stretch.

What that could mean for you: where your category sits in the store, and which categories sit next to it, could become another variable that shifts by store cluster instead of staying consistent across a division. That has real implications for cross-category promotional strategy and secondary placement opportunities.

Promotions, Clustered Instead of Broadcast

Today, most promotional planning still works close to a broadcast model. You fund a TPR, it runs across the divisions where you have distribution, and performance varies naturally by market. AI-driven promotion tools point toward something different: promotions targeted and funded by store cluster based on where the model predicts the strongest lift, rather than run uniformly everywhere at once.

If this direction plays out, your promotional dollars could be allocated more efficiently, funding real lift in the clusters where your item responds well to promotion, rather than spreading a flat rate everywhere including markets where it barely moves the needle. That is potentially good news for trade spend efficiency. It could also mean more complex promotional agreements, more cluster-specific terms, and more moving pieces to track and reconcile.

Push this idea a bit further and a broadly cast promotion, the kind that runs everywhere because that is simply how deals have always been built, could start to look inefficient to a model comparing lift by cluster. A promotion that only really performs in a portion of your distribution footprint might only get funded, or only get approved, where the data says it is meaningful to the overall business. The rest of the footprint could be left off the deal entirely, not because Kroger does not want your promotion everywhere, but because the model does not see the return to justify it everywhere.

What that could mean for you: this cuts directly against the KISS principle we have talked about in this publication before, keeping your promotional structure simple and consistent. If promotions become more clustered by default, the suppliers who can still keep their side of the agreement clean and trackable will have a real advantage over suppliers who let the complexity spiral.

The Sales Call Itself

This is the most uncomfortable one to say out loud, so let's say it plainly. If a model can already generate the assortment recommendation, price the item by cluster, and decide where a promotion earns its funding, the traditional sales call, the meeting where a supplier walks a Category Manager through a deck and makes a case, starts to look less essential for the routine parts of the relationship.

It is not hard to picture a version of this where a meaningful share of what used to require a sit-down meeting instead becomes a portal. Suppliers enter their items, their cost, their specs, and their claims directly into a system. This is not entirely new territory, manufacturers and brands are already doing a version of this today through GDSN, the Global Data Synchronization Network. Item attributes, specs, and claims get syndicated through GDSN-certified data pools like 1WorldSync and Syndigo, and that data already flows directly into how Kroger's systems understand a product, no meeting required. The shift this post is describing is less about creating a new behavior and more about that same data doing more of the decision-making. When there is genuine innovation, a new item, a reformulation, a real point of differentiation, the supplier refreshes that entry and the model evaluates it against the same data it already uses for everything else. The meeting only gets scheduled when there is something that actually requires a human judgment call.

Take that a step further and the portal does not have to stop at what the supplier enters. Kroger already sits on its own POS data, and already licenses syndicated market data the way most large retailers do. A portal entry could plausibly get evaluated against that data automatically, not just checked for completeness, but checked against actual sell-through by division, by state, even by store cluster, to see whether the market is really asking for what the supplier is offering. Push it further still, and the same system could pull from other signals entirely, shifting diet patterns, ingredient trends, lifestyle and wellness movements, category innovation happening elsewhere in the market, to flag a gap in the assortment before a Category Manager would ever spot it by hand. In that version, the portal stops being just a faster way to submit your item. It becomes a way for Kroger to tell you what the shelf actually needs, informed by data the supplier never touched.

What that could mean for you: the suppliers who treat the sales call as a formality, a check-in with nothing new to say, are the ones most exposed if this shifts. The suppliers who show up with real innovation, a genuine performance story, or a category insight the model would not surface on its own are the ones who keep earning a seat at the table, because that is exactly the part a portal cannot replace.

What This Could Mean for the Relationship

If even some of this direction plays out over the next several years, the throughline is the same across every area above. Routine, data-driven decisions get faster and more granular. Relationship-driven, judgment-based decisions become more valuable, not less, because they are the part a model cannot do.

That means your Category Manager's time, whoever holds that role and wherever they are sitting, gets more valuable, not less. Fewer manual data pulls could mean more room for a real conversation about your brand, your innovation pipeline, and your category strategy. It could also mean less patience for suppliers who show up unprepared, because the model has already done the baseline homework before the meeting starts.

The suppliers who will be fine in this kind of shift are the ones already showing up with clean data, a clear story, and a simple, executable plan. That is not a new lesson. It is the same lesson this publication keeps coming back to, just with a new reason behind it.

None of this is a prediction with a date on it. It is a direction worth watching, and worth preparing for a little earlier than you think you need to. The suppliers who wait until it is confirmed will be the same suppliers scrambling to catch up once it is.

From Cincinnati CPG Edge, keeping you in the Kroger know.