Last week, Ahold Delhaize told its own customers to go shop on its competitors’ apps. Stop & Shop’s outage page and other banners’ outage pages pointed people to DoorDash, Instacart, and Uber Eats.1

The apology coupon ran out Thursday. It offered 10% off one in-store trip, capped at $10, and required you to check in at a Savings Station kiosk to get it. I’ve seen bigger make-goods for a late pizza.

Ahold Delhaize USA’s banner sites and apps came back Tuesday morning after at least eight days offline.2 The company says it has no evidence anyone took sensitive customer, associate, or partner data, and the investigation is still open.

I’ve been building grocery and retail apps since 2011, and the first week after an outage tells you a lot. Some customers drift back on their own. The rest have to be fetched.

1. Tell customers everything, on every channel

Ahold has apologized and filed a report with the Dutch police.3 It still hasn’t said what happened. Customers have heard that silence before, because the November 2024 attack on Ahold Delhaize USA exposed personal information for more than 2.2 million people.4

To their credit, Claire Peters and Giant Food’s Ira Kress signed their letters with their own names and made specific promises on refunds and points. I’d keep that voice for the explanation too. A named executive saying what happened carries more weight than a PR post nobody signed.

Shoppers already learned once that “we’re investigating” can end with a breach letter. Ahold doesn’t need to publish forensics, and it shouldn’t. It does need a plain page on every banner site saying what the company shut off and why, what data it checked, and what’s different now, with the same note on receipts, on kiosk screens, and in an email from each banner president.

Ahold reports third-quarter results on November 4. Customers shouldn’t get the real story from an analyst’s question on that call.

2. Make loyalty whole

Two banners have already said something. Giant Food’s Ira Kress told customers they wouldn’t lose value during the disruption, and Stop & Shop said it would extend GO Points that were about to expire.5

That’s two promises in two different places. I’d set one rule for all five banners: anything a customer couldn’t use between October 12 and restoration (points, rewards, fuel discounts, digital coupons) comes back on its own, without a form or a phone call. The Shelby Report noted that shoppers locked out during an outage often discover their expired balances only after the app returns.6

I’d go further and stop expiring points, period. If Ahold can freeze expirations for a week, it can freeze them for good, and a member who never loses points has one less reason to wander down the road.

3. Aim the offer at the channel that broke

The stores never closed, so a discount on the next store trip mostly rewards people who never left. Pickup and delivery broke, and the offer belongs there.

Ahold has been here before. After the 2024 outage, Hannaford sent customers $10 off a $100 purchase.7 The 2026 version is also worth ten bucks if not more.

My version gives every loyalty member who ordered online in the 90 days before October 12 free pickup or delivery for 30 days and a real dollar amount off their first order back. It has to go out wide because Ahold can’t identify shoppers who switched to Instacart. Instacart has that list, and Ahold doesn’t.

That list matters. Ahold’s US digital sales grew more than 14% last quarter while comparable-store sales grew less than 1%.8

4. Make the owned app the cheaper way to order

Here’s the easiest acquisition pitch in grocery: it costs less in our app. Hardly anyone says it, and Ahold can back it up today.

A WGME investigation checked Hannaford items on Instacart and found every one priced above the shelf, with the full order running more than 12% higher before fees.9 Hannaford told the station that Instacart sets the markup within a range the two agreed on. That’s one banner in one market, but it shows the gap is real and that shoppers mostly can’t see it.

So show it. Put a price comparison in the welcome-back email and on the app’s home screen, and hang a sign at the pickup counter.

Then widen it. Ahold should sit down with Instacart, DoorDash, and Uber Eats this quarter and push each markup range up 3 to 5 points. Every marketplace order costs Ahold a commission and the basket data, and the platform gets to pitch competing banners to Ahold’s own customer.

Stop & Shop sent its shoppers to all three platforms last week. Ahold paid for that customer acquisition and the platforms got to keep it, so asking them to carry more of the cost seems fair to me.

Timing is the part I’d sweat. The new ranges should land the same day as the welcome-back offer, so anyone who notices the higher marketplace price finds a cheaper option already waiting with a discount.

5. Use the stores to sign people up

For a week, the Savings Station kiosks did the app’s job. Giant Food customers used them to activate digital coupons and personalized savings, and Food Lion told shoppers they could activate loyalty coupons the same way.[5]

Anyone who walked up to one of those kiosks wanted the digital deal badly enough to go find it. I’d put an associate there through the holidays with a QR code, permission to set the app up on the customer’s phone, and a free pickup order to try it. The line is simple: you clipped coupons here last week because the app was down, and now you can do it from your couch.

Cashiers should ask every loyalty member without an app account. Then score each store weekly on the share of loyalty members who place an online order with Ahold within 60 days.

Store managers already answer for shrink and labor. Omnichannel conversion can go on the same scorecard, with a bonus attached.

6. Write the plan for next time now

Ahold has now been hit twice in two years. I wouldn’t bet against a third.

When the app went dark last week, the banners improvised. Each one wrote its own make-good, and the outage page sent shoppers to the marketplaces because nobody had built another door.

Start with loyalty. A cashier should be able to pull up any member by phone number and apply rewards at the register, and shoppers should hear on day one that their points still count.

Write the outage page and the first customer email now, get legal to sign off now, and keep them ready to send within an hour. If the page still links to Instacart, Ahold should negotiate today to have that order data return when the outage ends.

A customer Ahold sends to a marketplace during an emergency is still Ahold’s customer, and the contract should say so.

Then test it on purpose. Take one banner’s app down on a slow Tuesday night and see who in the stores and the call center knows what to do. Last week’s post made the same case for freezer controllers, and I’d expect the app drill to turn up just as many blank stares.

If you’re not Ahold, run this test anyway

Ahold has plenty of company here. Most regional chains I’ve worked with run loyalty through an app, push some share of digital orders through Instacart or DoorDash, and have never pulled their own site down to see what breaks.

Start at the register and ask whether a cashier can redeem a member’s rewards with the app down. Then pull up your outage page, if you have one, and see where it sends people. Two harder questions belong in the next marketplace contract review: whether you get the order data back from orders placed during an outage, and whether your customers have any idea your app is cheaper.

Ahold spent eight days finding out its answers in public. You can find out yours in a conference room next week.

Cyril Scheubel manages a large Intermarché outside Dijon, and around 4 a.m. Monday, a surveillance company called to tell him his freezers and cold rooms were warming up. He drove straight in. When he opened the freezers, he found hot air coming out where the cold should have been.1

France’s finance minister later told Parliament that roughly 700 of the country’s 10,000 supermarkets got hit the same way. Hackers raised refrigerator temperatures to about 50°C (122°F) for about six hours, and stores dumped the food in dumpster loads.2

That same Sunday, Ahold Delhaize spotted a cybersecurity incident of its own and pulled the Food Lion, Hannaford, Stop & Shop, and Giant Food websites and apps the next morning. Ahold says it sees no reason to connect its problem to the French attack, and I’m taking them at their word.3

The two failures still share something. A freezer controller and a loyalty app both broke, and in each case nobody had decided ahead of time what the store should do next.

Who patches a freezer?

Danfoss supplied the refrigeration software, and it said most affected systems were older products that no longer received security updates.4 ANSSI, France’s cybersecurity agency, reported numerous compromised control modules from Danfoss and from US-based Copeland. Claroty had flagged flaws in the Danfoss AK-SM 800A and Copeland XWeb Pro platforms before the attack, and both vendors had already shipped fixes.5

So the patches existed. I’d love to know whose job it was to install them, because a rack controller sits in an awkward spot on the org chart. The vendor sold it, a refrigeration contractor services it, facilities pays the bill, and IT may not even know it is on the network.

I’ve argued here before that the back room is where grocery tech pays off, with predictive maintenance and shrink prevention beating another shopper-facing chatbot every time. That payoff depends on wiring up equipment that used to sit alone on a concrete floor, and wired equipment needs somebody’s name on it.

The same symptoms showed up on US bases

In late August, at least six US military commissaries confirmed refrigeration outages within days of each other, including Fort Huachuca in Arizona. They threw out thousands of dollars’ worth of meat, dairy, produce, and frozen food. The commissary agency’s refrigeration contract specifically required equipment that could be monitored and controlled remotely.6

Army CID’s Cyber Field Office and the Air Force Office of Special Investigations both confirmed they’re investigating. The Pentagon has acknowledged a “possible refrigeration disruption” and nothing more. Nobody has named a cause or tied it to France.

Ahold’s loyalty program went dark with the app

Ahold kept its stores, pharmacies, and gas stations open and promised refunds for digital orders that never processed. Then the Stop & Shop outage page apologized and pointed shoppers to DoorDash, Instacart, and Uber Eats.7

Ahold’s own website handed its fastest-growing channel to the platforms that keep the customer data when the order is done.

Ahold Delhaize’s US digital sales rose more than 14% last quarter while comparable-store sales grew less than 1%.8 Over in the Netherlands, competitors reported a bump in delivery orders while Ahold’s sites were down.9

Shoppers also can’t access their loyalty accounts on the websites or apps, so they can’t redeem the rewards that justify choosing Giant or Hannaford over the store down the road. Can a Giant shopper give a phone number at the register this week and still redeem points? None of the coverage I’ve read answers that, or whether anyone designed a fallback.

Most households already spread their grocery run across a few stores. A week without points just sends them to another store more often.

This is also Ahold's second major cyberattack in two years. The November 2024 incident affected more than 2 million US customers and disrupted deliveries, payments, pharmacies, and the banners’ online portals.

Somebody has to write the 5 AM plan

Cyril Scheubel learned from a surveillance company’s 4 AM. phone call, and by then the food was already lost. A store needs a plan before that call comes. If I ran a grocery chain, I’d finish this list before the holidays:

1. Count the boxes. List every network-connected device in your stores, from refrigeration controllers and HVAC to electronic shelf labels and scales, and put one person’s name next to each.

2. Get the refrigeration and any other critical controllers off the open internet. Danfoss itself tells customers to run these systems behind a firewall. Ask facilities which controller models you run, check them against the vendor advisories, and budget to replace anything the vendor no longer patches.

3. Put patching in the service contract. Your refrigeration contractor already walks every store, so firmware updates and a quarterly report should be part of the job.

4. Unplug one controller on purpose. Pick a store, pull the network cable, and watch whether the local alarms still fire and whether the night crew knows the manual override.

5. Give loyalty an offline mode. A cashier should be able to find a member by phone number and apply rewards at the register when the app is down, and shoppers should hear about it on day one of an outage.

6. Settle the aggregator question now. If your outage page will send shoppers to Instacart or DoorDash, negotiate today for the order data to come back to you.

7. Run the drill. Put a store manager, your CISO, and your head of facilities in one room and walk through the first two hours of an operations attack. You’ll find out fast which questions nobody can answer.

Most of this costs a few meetings and some contract language. The hard part is getting facilities and IT to agree on who picks up the phone at 4 AM.

The Paris prosecutor’s cybercrime unit is still adding up the spoiled food and the fires. On Friday morning, Ahold’s US banner sites were still down, and they were still giving customers to the marketplaces.

Greg Foran included a number in his memo yesterday, but didn't add a single job title. Kroger will cut about 3,000 corporate jobs over three years, and the company told Grocery Dive it will start identifying affected roles in the coming months. The count is set, and the list of jobs still doesn’t exist.1

Fast Company ran the division Kroger skipped. Corporate headcount is about 8,200, meaning more than a third of the building is going, while stores and distribution centers keep everyone.2

Albertsons reported its second quarter the next day. CEO Susan Morris told analysts the company already cut significant headcount last year, expects to cut more over the next 12 to 18 months, and may exit regions where its stores underperform.3

Nobody is cutting cashiers

Kroger, Walmart, Target, and Albertsons all announced cuts this year, and none touched the front end. Kroger’s cuts are corporate only. In May, Walmart said it would lay off or relocate about 1,000 people in its global technology and product teams.4

Target went after a different part of the org chart. Its February cuts took out roughly 400 supply chain positions and about 100 district roles, closed some regional offices, and left store jobs alone.5 Albertsons hasn’t said where its next round lands.

Foran wrote the line all of these memos were circling. Kroger has “more layers, more handoffs and more overhead than we need,” he said, and he’s right about the handoffs.

The savings go to price and the floor

Foran says the money moves closer to the customer, into lower prices and investment in associates. Target said it would put its savings into store labor hours and new guest experience training. Both companies are spending corporate salaries on the shelf and the sales floor.

They’re doing it because they’re losing share. Numerator data cited by Grocery Dive has Kroger and Albertsons slipping while Walmart, Costco and Aldi gain. About a month before the memo, Kroger also cut its same-store sales outlook to under 1%.6

Albertsons is in a tougher spot. Identical sales fell just under 1% in Q2, with net sales of $18.9 billion, and management declined to update the outlook. Digital grew 11% while total sales still slipped, putting pressure squarely on in-store sales.

The nationals are paying for price cuts with corporate payroll. None of the memos say it that way, but that’s the trade they describe.

The test is whether a step disappears

Counting heads tells you very little about whether a layoff will work. A cut works when it removes a decision step, and it fails when it removes a person but leaves the step in place. In the second case, somebody else inherits the handoff with less context and the same deadline.

Walmart spent a year moving Walmart U.S., Sam’s Club, and international onto one shared platform. Its memo says teams combined and roles were rewritten after the overlap showed up, so the system removed the duplicate steps first, then the jobs attached to them. On paper, that’s the clean version.

Kroger hasn’t shown its work yet, and it has time to. Foran’s stated goal is broader roles and faster decisions, which, on its face, is a step-removal goal. Kroger published an AI strategy in September and hired a chief data and AI officer earlier this year, so the three-year runway gives it room to match each cut to a process it actually retires.

Morris described Albertsons’ version of the problem on the call. She said some changes added complexity and cost “for good reasons and with the right intentions,” and her answer is a much shorter project list. Fewer projects means fewer steps to staff in the first place.

Most handoffs exist because two systems don’t talk to each other. If you cut the person who bridges them and leave the systems alone, you’ve traded a slow handoff for a dropped ball.

Headlines will call this the AI layoff wave

The memos don’t support that. Fast Company noted that Foran never names AI as the technology he means. Walmart’s memo didn’t mention AI at all, and a spokesperson said the cuts came from restructuring.7

These are org design layoffs that happen to share a news cycle with a lot of AI talk. If a vendor shows you these announcements as proof of AI savings, ask which paragraph of the memo they’re reading.

Down on the floor, the story is different

Store jobs are shrinking for their own reasons. USDA data shows more than 5.3 million fewer people receiving SNAP. In Arizona, grocery and convenience stores lost more than 2% of their jobs in Q1, roughly four times the national rate of about 0.5%.8

Ahold Delhaize put a number on the damage. SNAP cuts took 40 basis points off its Q2 U.S. sales growth, and the company expects 60 to 80 basis points for the year, with SNAP at about 5.3% of U.S. sales.

Corporate headcount is falling because executives decided it should. Store headcount is falling with demand, and Target and Kroger will have to keep their promise of more store hours while a chunk of their SNAP shoppers spend less.

What a regional grocer should do with this

Kroger’s cuts will reach regional grocers whether they cut anyone or not. Prices across the street will drop, résumés from Cincinnati and Bentonville will start showing up, and someone on your board will ask why you aren’t trimming corporate too.

The answer is that you never built most of what Kroger is tearing out, which means your to-do list looks different from theirs:

1. Ignore the headcount number. Kroger’s third reflects years of running many banners under a national corporate structure. A single-banner regional never built most of that layer, and copying the percentage would take out the people already doing three jobs.

2. Count steps before counting people. Write down every decision that needs more than one sign-off and every place two systems keep the same data. Kill the step first, then see whether the role still has work.

3. Plan for sharper Kroger pricing in shared markets. Foran tied these cuts directly to lower prices, so pick the categories you’ll match now and decide where fresh, service, or convenience has to carry you instead.

4. Hire from the wave. Kroger will release corporate people in batches for three years. Walmart is asking affected staff to move to Bentonville or Northern California, and many won’t go. That’s merchandising, pricing, data, and product talent with national-scale experience, arriving on a schedule you can plan around.

5. Put a basis-point number on your SNAP exposure. Ahold did it in public, and you should at least know SNAP’s share of sales by store.

6. Audit your roadmap for well-meant complexity. Albertsons said on its own call that good intentions added process and cost. Look at your project list the same way and cut anything that adds a step without retiring one.

Kroger has three years to make 3,000 role decisions. If shelf prices don’t move by year two, the handoffs just changed desks.

Walmart owns a 2023 patent describing a shelf price for mayonnaise that changes when a shopper already has tuna in her cart.1 On October 5, Dan Bartlett, the company’s head of corporate affairs, published an open letter swearing Walmart doesn’t do that and never will.2 When Retail Dive asked why Walmart would patent a pricing method it promises never to use,3 Walmart didn't respond.

That silence sits at the center of a two-week campaign to defend electronic shelf labels (ESLs). The campaign included a CEO letter,4 a corporate blog post with a Q&A,5 a new in-store navigation feature, and Bartlett’s public rebuke addressed by name to Lindsay Owens, who runs the consumer advocacy group Groundwork Collaborative.6

I read it all, and I came away more convinced that most grocers should leave ESLs on the vendor’s demo cart for now. If the world’s largest retailer fumbles the message, this technology is too hot to adopt.

Two terms get blurred in this fight, so here’s how I use them. Dynamic pricing moves a price for everyone based on demand, like soup costing more when a snowstorm hits. Surveillance pricing moves a price for one person based on what a company knows about them, like that mayonnaise.

What Walmart is saying

Walmart deserves a fair hearing first. John Furner’s late-September letter promised that a shopper’s income, shopping history, urgency, or willingness to pay will never move a price. Bartlett's follow-up went further and described the tags as a paper replacement that displays prices already approved through a central process.

Bartlett’s letter argued that Owens blurred the line between what a technology could do and what Walmart does with it.

The operations case holds up. According to Supermarket News, the LEDs built into Walmart’s tags already power Stock to Light, which shows associates where product belongs, and Pick to Light, which guides online order pickers to the right facing.7 Anyone who has watched an overnight crew chase a few thousand paper tags with a scanner and a stack of printouts knows why a chain would want that job gone.

Furner also pointed out that labels help ensure the shelf price matches what rings up at checkout. Price-accuracy complaints wreck trust faster than most promotions build it, so that benefit counts.

ESLs are promising technology. They cut labor on price changes, keep the shelf in sync with the register, and turn every facing into a small wayfinding beacon. I’d want them in any store I ran if consumer mistrust weren’t part of the price.

The week the story split

The patent is the cleanest contradiction. Retail Dive reported that Walmart received it in 2023 for remotely controlling electronic shelf labels.8 The filing describes adjusting prices based on what’s already in a shopper’s cart and on supply and demand, and its own example says a customer with tuna “may be offered a different price for mayonnaise.”

Bartlett argued that a patent only describes what an invention could do. That argument would land better if Walmart had answered the one reporter who asked why it filed the patent in the first place.

Shop to Light stretches the story a second way. Walmart launched the feature on October 1, and it lets shoppers use the app to make a specific shelf tag blink so they can find an item. Four days later, Bartlett's letter described the same tags as paper replacements that identify no one and decide nothing on their own.

Nothing in the coverage says Shop to Light identifies anyone, and I’m not claiming it does. What worries me is capability creep, since a tag that started as a price display now takes requests from a customer’s phone.

The paper-tag defense works only as long as the tag keeps acting like paper, and every new feature pulls it further from that description.

How short the distance is

I’ve built shopper apps for a living, and this is what bothers me. The space between customer tracking and surveillance pricing is narrow. The display and the pricing engine share one pipe, and the only thing separating them is a rule some developer or bot can rewrite.

Look at what flows through that pipe. Walmart's app and Sparky, its AI shopping assistant, take in what shoppers ask for and, by Walmart’s own account, sometimes how urgently they need it. The shelf tag shows whatever price the central system sends, and Walmart’s Q&A says approved price changes are “typically made outside of shopping hours.”

Typically is a word lawyers love. The reach keeps growing too, because Walmart bought Vizio in 2024, and Walmart U.S. CEO David Guggina told a Goldman Sachs audience he could picture Sparky on the TVs customers keep for about seven years.9

Bartlett’s letter makes one argument over and over: capability doesn’t equal practice. I accept the logic. The letter concedes the key point in making it, though, because nobody spends a page separating capability from practice unless the capability is real.

What separates Walmart’s stack from surveillance pricing today is a policy. Policies live in CEO letters, and CEO letters get rewritten when leadership changes or margins tighten.

Why perception always wins

ESLs carry too much negative perception to be a smart adoption for most grocers right now. Shoppers already distrust them. A GBAO Strategies poll commissioned by UFCW and RWDSU locals found that two-thirds of New York voters support banning the tags in grocery stores, and 66% say they don’t trust grocers to use the technology responsibly.10

Grocery unions have their own stake in labor-saving tech, so weigh the source. The fear still runs far ahead of the evidence, because a study covered by the AP looked at one chain’s prices from 2019 to 2024 and found that temporary price increases barely moved after it installed digital labels.11

That gap between what ESLs do and what shoppers believe they do makes them a bad bet right now.

Lawmakers have already acted on that belief. Maryland’s law barring grocers from using digital labels to change food prices during the day took effect October 1, Seattle banned personal-data grocery pricing in September, and New Jersey and Connecticut passed similar measures. New Jersey also imposed a one-year moratorium on installing digital labels altogether.

A moratorium means a regulator decided the hardware itself became the problem. Walmart can absorb that fight with a corporate affairs chief and an FAQ. Any regional chain in New Jersey with ESLs on its roadmap just lost a year and picked up a public relations problem it didn’t create. Many other states have legislative bills under review.

The exposure spreads past Walmart. Supermarket News lists Hy-Vee, Kroger, Whole Foods, Sprouts, and Save Mart among the grocers rolling out digital labels.12 Each one inherits Owens’s picture of pricier ice cream on hot days.

Shoppers don’t choose grocers by who wrote the better letter.

The fight grocers keep picking

I wrote about this pattern when Maryland lawmakers first went after dynamic grocery pricing. I argued that operators pointed their technology at capturing margin from customers when they could have captured it from reducing operational waste.

ESLs repeat that mistake in public view. A shrink model runs in a back office no shopper ever sees, while a digital price tag hangs at eye level in every aisle. Stock to Light and Pick to Light point the tags at labor and accuracy, and Shop to Light, the one piece aimed at shoppers, is where Walmart’s story started to stretch.

A mid-tier chain can chase part of the same savings this year without hanging anything in front of a shopper. They can implement better handheld workflows for price changes, tighter shelf-to-register audits, and forecasting that cuts markdowns that run out of sight.

Bartlett closed his letter by asking Owens to correct the record. Owens had already asked Walmart to pull the patent filings.

Are ESLs worth the drama attached to them?

Rents in my city rose more than 25% last year, the steepest climb in the country. A one-bedroom in San Francisco now goes for $4,000 a month. The reason is not a mystery: AI hiring pulled high earners back into the city, and the people collecting the rent call it supply and demand.

This month at Groceryshop in Las Vegas, two companies headquartered here told a room of grocery executives that online grocery has become unaffordable and that grocery pricing is the reason.

Same industry, same move, different bill.

The diagnosis was right. The prescription was not.

Instacart CEO Chris Rogers told the Groceryshop audience that affordability is the number one reason shoppers churn off online grocery and the biggest barrier to adoption.1 Every operator in that room already knew it.

Then he named the remedy. Instacart is lobbying the grocers it works with to price online at shelf parity, or only slightly above. Rogers acknowledged those conversations have been difficult, and Instacart says 90% of U.S. shoppers now have access to a parity grocer on its platform.2

DoorDash is pressing the same point. Its VP of grocery and retail partnerships confirmed the company continues to push parity with its grocer partners.3

Look at who is being asked to sacrifice

FMI puts average net profit for grocers at 2.1% and noted that roughly 11% of grocers posted operating losses.4

That is two cents on the dollar, and it is the business is also being asked to solve affordability through price concessions.

Now look at the other side of the table. Both Instacart and DoorDash layer a service fee, a delivery fee, a tip, and often a subscription on top of item prices that grocers mark up above the shelf. That markup mostly covers the commissions Instacart and DoorDash charge grocers, which in my experience run 5 to 10 times a grocer's net margin, and parity asks grocers to absorb them instead.

Last December, a Consumer Reports and Groundwork Collaborative investigation also found identical items priced as much as 23% apart from one Instacart shopper to the next.56

Instacart’s position in Washington makes the point for me. When the FTC opened a rulemaking in April on fee practices in food and grocery delivery, Instacart opposed all-in pricing, arguing it would make individual grocery items look confusingly expensive.7 In Washington, showing shoppers the full cost is confusing, and in Las Vegas, the grocer’s shelf price is the problem.

Neither company proposed changing anything. Cutting that fee stack is the fastest fix for delivery affordability, and it belongs to the two companies that spent Groceryshop pointing at grocers.

The number that ends the argument

Order through a grocer’s own site or app and the cost structure changes completely.

First-party delivery carries real expense in picking labor, packaging, routing, and shrink on substitutions. Grocers typically recover it with a modest item markup or delivery fee. The total still comes to a fraction of what the same basket costs on a marketplace once the item markup, service fees, delivery fees, and tips stack up.

It is the same store with the same shelves, and frequently the same pickers. The only difference is how many parties take a cut.8

Stage 4 announced itself

I created The Marketplace Dependence Curve months ago to warn grocers about this risk of overreliance. Like an addiction, this curve runs through four stages: incremental demand, shared discovery, platform influence, and margin extraction. My argument was that most mid-tier operators believe they sit in Stage 1 while already in Stage 3, and that Stage 4 arrives quietly.

Grocers stay because a growing share of their online shoppers already order there, and walking away hands those shoppers to a competitor in the same app.

This time Stage 4 showed up on a main stage.

Margin extraction is what happens once a platform controls enough demand to dictate the grocer’s economics. A public campaign to set your online shelf price, already reaching 90% of U.S. shoppers, is not a negotiation between partners.

Instacart’s defense is that parity grocers have grown more than 10 percentage points faster on its platform and retain customers better.9 Read through the curve: faster growth inside someone else’s interface, with discovery, data, and the customer relationship on their side, is the exact mechanism that carries an operator from Stage 2 to Stage 4.

They described the dependency and sold it as a feature.

I made the case for why that growth belongs to Instacart, not the grocer, in The Instacart Search Question.

An analyst asked DoorDash’s CEO Tony Xu on their Q2 2026 earnings call whether the company could reprice grocer relationships and shift some of the affordability burden back onto grocers.10 Xu declined to discuss specific economics, but he said that DoorDash might represent 100% of the growth those partners see.

That is not how a partner talks. That is how a landlord talks.

Same playbook, different asset

I call this marketplace misdirection. Build a position between customers and something they need, then price against that position. When the cost turns politically untenable, announce the crisis, express concern, and recommend that somebody else absorb it.

In housing, the somebody else is the tenant. In grocery, it is a business earning two cents on the dollar.

What operators should do with it

Reject the framing first. Grocery pricing didn't break delivery affordability, and a 2.1% margin business isn't where the slack lives.

Then run your first-party model again with today’s parity requirement in it, not the model you built before parity. Ask what it costs to serve that same customer through your own app, at a modest markup, with the relationship on your side of the ledger.

Now run the comparison your shoppers can already see. Put your delivered basket against the same basket on the marketplace, including item markup and fees. If your own channel wins on price, and it usually does, that is a marketing campaign you have not run yet.

The two companies built the most expensive layer in online grocery, then took the stage to explain that the problem is your shelf price. You are being asked to fund a discount on a basket you no longer own, by the same companies whose commissions and fees created the crisis.

Decline.

Every empty slot an Instacart shopper sees in your aisle now becomes a data point. Instacart just announced a portal that shows CPGs, store by store and day by day, when shoppers switch to a competitor. What Instacart hasn't said is whether you'll see what they see.

Here’s what Instacart shipped

On Sept. 21, Instacart announced in-store software that maps a store down to individual products on the shelf. Instacart says its network of about 600,000 shoppers visits each store more than 15 times a day on average, and that those visits produce over 10 million unique shelf-scan data points daily.1

The company blends those scans with more than 1.6 billion lifetime orders plus shelf imagery from shoppers and Caper Carts. The computer vision comes from Arpalus, a startup Instacart acquired in July. Instacart’s chief commercial officer said traditional inventory systems “weren’t built for today’s reality.”

Two products come out the other end. Grocers get Inventory Intelligence, a virtual store view with shelf visibility, merchandising suggestions, and out-of-stock forecasting.

CPGs get the Store Excellence Portal. It gives them a daily store-level shelf view, estimated lost sales by store and UPC, and a record of whether shoppers switched to a competitor when an item was missing. Both products are rolling out with select grocers and brands now, with more coming over the next few months.

An empty shelf costs more than one sale

Out-of-stocks rarely get their own line on the P&L. FMI put missed sales of consumer packaged goods from out-of-stocks at 5.8% of total U.S. grocery sales2, or roughly $58 billion.

The damage keeps going after the missed sale. Your customers already split their grocery spend across several stores, and an empty slot hands them one more reason to fill the basket somewhere else.

So a live view of every gap is worth real money to whoever holds it. Instacart says it’s in each store more than 15 times a day.

Three questions Instacart left open

Who controls the shelf data Instacart collects in your stores?

Take a single scan. A shopper stands in your aisle 7, phone out, scanning the tag under the gap where the granola should be. You bought that granola, and your category manager slotted it right where the planogram binder says it goes.

Any customer can see that empty slot, so a legal fight over who owns the view won’t get you far. Your power sits in the contract. Instacart needs your front door and your inventory feed, and it gets neither without a fulfillment agreement you signed.

Instacart drew this map in your stores, on orders your fees help fund, so you should set the terms.

Stitching those scans into a store map took real engineering, and Instacart deserves credit. Read what your delivery contract says about data captured on your floor, because the announcement says nothing about it.

Will Instacart share the full picture with you?

The announcement doesn’t say what Inventory Intelligence costs, or whether it shows you every data point Instacart collects in your stores.

Who else gets to look?

The Store Excellence Portal gives CPGs a daily read on your shelves, store by store, with a dollar figure on every hole. Advantage Solutions, a services firm that works for CPGs, says the portal helps it find missing products and send people to the stores with the biggest sales opportunity.

While brokers filling holes on your shelves help everybody, the category review is where it gets uncomfortable.

The CPG’s sales team opens its deck with your stores ranked by estimated lost sales on its items, refreshed that morning. The next slide shows how often your shoppers walked to the competing brand when its product was gone. Your category manager brings last month’s scan-out report and a planogram binder with a coffee ring on the cover.

Every number in that deck argues for something. They might ask for more facings, or question why it keeps funding promotions your stores didn’t execute.

When CPGs show up with better data about your shelves than you have, you lose control of the negotiation before anyone sits down.

And don’t forget that your store brand sits on that same shelf. If the portal counts a switch to your private label as a competitor’s win, a national brand gets a daily read on how your own product performs in your own stores.

The release might not say whether private label shows up in brand reports, whether you can see what CPGs see about your stores, or whether you can block their access. Get those answers before your next category review.

You’re renting eyes

I call this rented eyes: shelf visibility a third party provides back to you, built from data captured in your own stores, which you can lose when the contract ends. Rented eyes can be excellent. The catch is that the landlord rents the same eyes to the brand across the table.

I made this argument about losing substitution data in The Marketplace Dependence Curve. Instacart’s launch carries that loss from their order screens into your physical aisles.

Your department managers might walk the aisles with a scan gun at opening, and again after the truck if the schedule allows. Few regional grocers can staff 15 floor walks per store per day. Instacart gets those walks as a byproduct of orders from your customers, and the fees you’ve paid.

That 15 is a network average, and visits follow order volume. The more a store leans on Instacart, the richer the data it loses.

The Grocery AI Pyramid put shelf accuracy at the foundation. Forecasting, replenishment, markdowns, and personalization all depend on knowing what’s on the shelf right now.

If your shelf truth lives at Instacart, every system you build on top of it runs on rented ground.

What to do this quarter

This is a store operations and data decision, separate from whatever your digital team is scoping for a shopping assistant. Before you sign up or renew, get written answers to these six questions, and sign only if they come back in your favor:

1. Who owns the shelf scans and images captured inside my stores, and can I export the raw data on a set schedule?

2. What happens to my stores’ shelf history if I leave the platform?

3. Does Inventory Intelligence show me every data point Instacart collects in my stores?

4. What does Inventory Intelligence cost, and is it priced per store, per banner, or bundled into existing fees?

5. Which CPGs can view my stores in the Store Excellence Portal, and can I approve or block that access store by store?

6. Do I receive any share of what CPGs pay for portal data about my stores?

Start building your own view before Instacart answers anything. Ask for every not-found and substitution record from your delivery orders, down to the item and the store. How fast that file arrives tells you how Instacart thinks about your stores' data.

Then price a shelf-scanning vendor such as Simbe Robotics for your three busiest stores. Inventory Intelligence should have to beat that number.

Last, call your top broker. Ask what the portal shows them about your stores, then ask for a copy. If they hesitate, you have your answer to question five.

This is the fifth in a 5-part series, You Can’t Prompt Produce, on the AI grocers keep shipping, and most shoppers keep skipping. We started here.

Picture forty units of ground beef getting rerouted from a store with three days of shelf life left to one that’ll sell out by Thursday. This is a composite scenario, not one company's dashboard. It's already happening across grocers running real-time inventory routing.

No customer asked for it. No customer will ever know it happened. That’s AI actually earning its keep in grocery right now. It’s running quietly behind a much louder conversation about customer-facing AI tools that a fraction of shoppers ever bother opening.

Call the first category Ambient Utilities. They run in the background, constantly, improving margin and moving product without ever asking the customer to type a sentence:

• Predictive sizing quietly cuts returns before shoppers notice the friction.

• Dynamic markdown pricing clears aging produce before it turns into shrink.

• Reconciliation between the warehouse system and the supply chain platform used to take two analysts a week. Now it runs itself.

Call the second category Performance Theater. Customer-facing AI tools work fine for the shoppers who use them, and some genuinely do. The problem is most retail AI budgets get sized around that small, vocal slice, while the systems running every single transaction, whether or not a shopper ever opens an app, get treated like plumbing.

None of the ambient stuff shows up in a keynote. All of it shows up in the P&L.

Grocery margins are thin enough that this math actually matters. A customer-facing feature has to clear a brutal bar: get used, and get used often enough to justify what it cost to build.

A backend feature only has to work correctly once. It keeps paying out every day after with zero adoption risk, because there’s often no human standing between the system and the outcome.

The five-step framework

Every CTO staring down a budget cycle needs a way to sort the pile of AI vendor pitches sitting in their inbox. Here’s the process:

1. Inventory: List every AI tool in production or in the pipeline, including the one a VP championed personally.

2. Score Customer Friction: Rate how much it requires from the customer: a click, a prompt, a decision, attention. Low friction means it works whether or not the customer notices it exists.

3. Score Operational Yield: What's the measurable business impact? Is it reduced shrink, saved labor hours, better forecast accuracy, or lower fulfillment cost per order?

4. Plot the Quadrant: Plot two axes: friction on one, yield on the other. Every tool gets a dot.

5. Allocate by Quadrant: Set budget bands per quadrant, not per project. Let the quadrant decide the funding tier, not whoever pitched hardest.

Step four is where the real argument happens

:

Notice what the framework doesn't say. It doesn't say abandon customer-facing AI to fund the routing engine. Those tools usually draw from different budgets run by different teams answering to different people anyway. This is about weighing new capital against both criteria at once, visibility and yield, so spend lands where it's actually earned instead of wherever the demo looked best.

Running this at budget time

Boards love a demo. They fund what they can see. That's exactly the bias this framework has to fight, which means you walk in with the plot already drawn, not a list of asks.

Bring the quadrant chart, not a feature roadmap. Show which tools already sit in “eliminate” and name the dollar figure that frees up, separate from any new ask. A CTO who kills their own pet project before someone else asks why it’s still funded earns more trust in that room than any pitch deck ever will.

Frame every request in yield language before friction language. “This cuts a manual reconciliation step” beats “this gives customers a smarter experience” every time, even when it’s the exact same tool wearing a different shirt. Yield is a number a board can defend to its own shareholders. Experience is a feeling, and feelings get eaten alive in the second round of questions.

Run this as a standing quarterly review, not a one-time exercise. Tools drift between quadrants the way milk drifts toward the back of the fridge, quietly, until someone finally checks the date. What scored high yield eighteen months ago might be curdled by now.

The real test isn't whether the customer notices the AI. It's whether the AI is earning its keep either way.

Where does your stack fail that test?

You Can’t Prompt Produce is a five-part series on how the AI grocers keep shipping, and most shoppers keep skipping.

1. The AI Mass Adoption Mismatch

2. The 95% Graveyard

3. Your Chatbot Sucks

4. Ten Million Against Thirty-Six Thousand

5. Five Steps to Fix How Grocers Greenlight AI

This is the fourth in a 5-part series, You Can’t Prompt Produce, on the AI grocers keep shipping, and most shoppers keep skipping.

Why are grocers pouring budget into customer-facing AI when shrink alone eats close to their entire profit margin?

That should stop every CFO in the room. It doesn’t, because a personalization demo looks better in a slide deck than a shrink report ever will. I’ve sat through both pitches. Only one gets applause.

The front office mirage

Grocery tech conferences all look the same now. Presentations promote smart carts, AR aisle navigation, and even some concept pitched as “the metaverse of grocery” that nobody in the audience asked for.

Please note: this is NOT an argument against customer-facing AI. It’s definitely needed to secure the next generation of grocery shoppers. A personalization tool that lifts basket size a couple of percent is a real win. I won’t pretend otherwise.

But that lift, for a business running a 1.6% to 2.1%1 net margin, is still small next to what's sitting untouched in operations. The gap comes down to ROI. It's real, just not as dramatic as people make it when they're chasing a headline instead of running the math.

Some of this is pure ego. A CEO announcing an AI shopping concierge gets a stage and a press hit. Nobody claps for “we cut spoilage in the dairy case by 8%,” even though that number shows up in EBITDA every quarter, on time, without a demo.

The grocers who thrive will balance investments in AI that customers can see with the fixes they’ll never notice.

Where the real money sits

The real money sits in parts of the store nobody photographs. The five AI projects below carry more weight than their price tag suggests, and none of them needs a customer to open an app.

Dynamic Markdown Pricing: Pricing algorithms are tied to SKU-level expiration data to catch a product hours before it becomes a total write-off. Forty cents recovered on a dollar you were about to lose outright is margin with no acquisition cost attached.

Automated Shrink Detection: Shrink prevention runs bigger than most operators say out loud. Computer vision at checkout catches sweethearting and scan avoidance in the moment. Point the same tech at the loading dock, and it flags inventory gaps before finance has to ask what happened. Point it at the produce aisle and less of it ends up in the dumpster.

Demand Forecasting: Predict SKU-level demand down to the store and the day, including weather and local events, then trigger reorders automatically. No marketing campaign will ever do that. Fewer stockouts walk straight to Walmart. Less overordering spoilage rots on the shelf.

Predictive Maintenance: Refrigeration and HVAC get ignored because they're the least glamorous line item in operations, and that gets expensive fast. A compressor failure in a frozen foods case doesn't just cost the repair; it costs everything in that case at once, and the sensor data that would've flagged it three weeks earlier usually just sits there unwatched.

Micro-Fulfillment Routing: Batch picking at the store level cuts labor cost per online order. Digital fulfillment bleeds margin badly right now. This is the one lever actually pulling it toward breakeven.

Running the numbers

Take a $1 billion chain running with a 1.8% net margin. That’s $18 million in profit. Shrink runs 2.7% of revenue on average2, so $27 million is already gone before anyone opens a strategy deck.

Cut shrink 100 basis points, from 2.7% to 1.7%, and you recover $10 million. That was money already spent on product that left the building and earned nothing. Getting it back drops almost straight to net income. Profit goes from $18 million to $28 million, a 56% jump from one fix.

Give the front office side its best case. A well-run personalization push drives a 2% conversion lift on $100 million in digital sales, a result most retailers would happily take. That’s $2 million in extra revenue. At a 1.8% margin, that’s about $36,000 in real profit, before licensing fees and the months it took to build.

Ten million against thirty-six thousand. I gave the front office example every benefit of the doubt, and the gap still isn’t close.

These usually come from different budgets and different teams, so it’s rarely a true either-or on the org chart. But someone still decides where the next dollar goes, and where the next round of exec attention goes, and that person is picking the wrong side of this math.

Where this leaves us

Grocery runs on thin margins and high volume. The operators still standing in five years won’t be the ones with the slickest app. They’ll be the ones who also fixed shrink, forecasting, and fulfillment while everyone else just demoed a chatbot on stage.

AI isn't a strategy here. It's a capability, and it only pays off when it's aimed at what's actually bleeding your margin.

Ten million dollars doesn't need a demo. It just needs someone to stop chasing the shiny things long enough to fix the boring ones.

Tell me where I'm wrong.

You Can’t Prompt Produce is a five-part series on how the AI grocers keep shipping, and most shoppers keep skipping.

1. The AI Mass Adoption Mismatch

2. The 95% Graveyard

3. Your Chatbot Sucks

4. Ten Million Against Thirty-Six Thousand

5. Five Steps to Fix How Grocers Greenlight AI

This is the third in a 5-part series, You Can’t Prompt Produce, on the AI grocers keep shipping, and most shoppers keep skipping.

Ask any grocery chatbot to rebuild last week’s order for your family, but now has a peanut allergy. Watch it recommend a snack bar that’s out of stock. Watch it swap in a “nut-free” substitute that isn’t, because nothing in that decision chain might have checked the label against the allergen flag.

That’s a real task. Complex tasks like meal planning, dietary restrictions, and rebuilding a cart from purchase history are exactly what a chatbot is supposed to do well. And it still gets it wrong.

Please note: this is NOT another rant against chatbots. They have an important role in securing future generations of loyal customers. It’s not “chat versus search for a gallon of milk.” Nobody’s trying to replace the produce grid with a text box, and no grocer is dumb enough to force that fight.

The focus is on ensuring your chatbot delivers the hard stuff it was built for.

Half the room hasn’t shown up

Even Walmart’s own research supports this. They found 47% of consumers would trust a digital agent to buy household essentials within a set budget. That number drops to 38% once you get specific about food and groceries, the lowest category they tested1. The company running the most visible AI shopping assistant in the country is telling you, in its own data, that groceries are the category people trust it with least.

And that’s despite real usage. Roughly half of Walmart’s app users have tried Sparky, and those users carry a noticeably higher average order value than the ones who don’t. So it’s not that nobody’s touching these tools. It’s that even heavy usage hasn’t closed the trust gap for the one category that matters most here.

Somebody else’s chatbot isn’t your chatbot

Here’s the part that gets skipped in the boardroom deck. National chains can build in-house, fund the engineering, and iterate on hallucinations weekly. Most regional and independent grocers can’t, and they know it.

That’s exactly why Associated Wholesale Grocers, a distributor, built a shared AI shopping tool, SmartMeals, in partnership with Breez AI, and handed it down to its independent grocery members2. Not because those grocers wanted a wholesaler’s chatbot. Because the alternative was falling further behind better-funded national chains with no realistic path to catch up on their own.

That’s the two-tier gap in its purest form. A middleman had to step in because small operators structurally can’t compete on this front alone, no matter how much they want to.

Where it actually breaks

Three specific failure modes keep showing up, and none of them are about typing being annoying.

Substitution logic is a known pain point across the industry. RetailWire’s coverage of online grocery substitutions found real, sustained shopper frustration, with cases where the replacement item sent wasn’t even a plausible stand-in for what was ordered3. That’s not a rare glitch. It’s a structural problem with how these systems guess at alternatives.

Inventory disconnection is the mechanical reason substitution keeps failing. Grocery inventory turns over multiple times a day, and pricing can shift within hours, but many retailers still only sync their product feeds overnight. A bot working off a stale feed will confidently recommend something that sold out that morning, and there’s no amount of clever prompting that fixes a data pipeline problem.

Hallucination is the third leg, and it’s the hardest to catch because it looks confident. The bot invents a product, a price, or a promotion that doesn’t exist, and the shopper only finds out at checkout.

The wrong answer costs more than a bad search result

There’s a reason getting this wrong stings more than a clunky menu. A shopper who hands a chatbot something complicated, such as a dietary restriction, a budget, or a week of dinners, is trusting it to think, not just to click. They’re not skimming a shelf on autopilot. They’re trusting the system to reason through something that actually matters.

When that trust gets a confident but wrong answer, the damage isn’t friction. It’s a shopper who bought the wrong bread for a kid with celiac disease because the tool she was told to rely on didn’t actually check.

The gap between the demo and the deployment

Vendors aren't lying when they show off a slick model in a sales meeting. They’re showing you the model’s ceiling, not the floor it hits once it’s stitched into your actual inventory system.

Baymard Institute ran a version of this test on the auditing side of the house. When they had GPT-4 evaluate live ecommerce pages against the same usability issues human experts had already found, the model caught only 14% of the real issues, and 80% of what it flagged was a false positive4. That’s the easier job: spotting a problem. The harder job, the one your chatbot is being asked to do, is not spotting friction but removing it in real time against a live catalog.

Retailers are moving faster than shoppers are willing to follow them. FMI’s early 2026 survey found more than two-thirds of food retailers now say they’re using AI, up from less than half the year before5. Meanwhile, groceries sit at the bottom of consumer trust in the very same Walmart data above. That gap will matter more than any individual product launch.

Build the chatbot worth trusting

Chat is a reasonable tool for genuinely ambiguous asks like meal planning around a budget, substitutions when shelves are empty, or shoppers working through dietary restrictions. These are genuinely hard problems, and language is a fine interface for solving them.

The mistake isn’t building a chatbot. It’s benchmarking yours against a company with ten times your data and a hundred times your engineering budget, then acting surprised when your version hallucinates a product or misses an allergy. Here’s what actually separates the two.

1. Sync your feed in real time. An overnight sync means the bot is recommending things that have already sold out. If yours updates in hours instead of minutes, find out why.

2. Write the substitution rules yourself. Leave allergen and diet swaps to a model’s guesswork and eventually something dangerous gets through, not just something annoying. Write the rules and own them.

3. Get a real number on how often it’s wrong. No vendor hallucination rate means no one’s actually measured it, and you’ll be the one who finds out in production. Ask what the bot does when it isn’t sure, too. A tool that always has an answer is worse than one that occasionally says it doesn’t know.

4. Figure out if it’s actually built for grocery. Plenty of what’s on the market is a general model wearing a grocery skin. This is usually where a national chain’s tool and everyone else’s start to look nothing alike.

5. Leave the door open, and ask if anyone wants in. Give shoppers a real way back to search and grids. And find out if they even want this before you bet the launch on it, because high usage with low satisfaction just means people feel stuck.

6. Decide what success looks like before you scale. Pick your baseline metric first: cart completion, time to checkout, number of complaints. Then put someone in charge of the failure log every week, with a number that actually triggers pulling the plug.

Get those six right, in-house or with a vendor, and you’ve got something people can rely on.

A downloadable version of these six rules, with supporting questions for each rule, is available on the Resources page.

Your turn, shoppers

What’s the dumbest thing a grocery AI has ever recommended, hallucinated, or gotten flat wrong for you? Maybe it swore an out-of-stock item was sitting in your cart, swapped in a substitute that ignored an allergy, or invented a price that never existed.

Drop it below.

You Can’t Prompt Produce is a five-part series on how the AI grocers keep shipping, and most shoppers keep skipping.

1. The AI Mass Adoption Mismatch

2. The 95% Graveyard

3. Your Chatbot Sucks

4. Ten Million Against Thirty-Six Thousand

5. Five Steps to Fix How Grocers Greenlight AI

This is the second in a 5-part series, You Can’t Prompt Produce, on the AI grocers keep shipping, and most shoppers keep skipping.

A grocery chain’s board approved a seven-figure budget for a GenAI pilot last year. Nobody on the exec team can tell you exactly which use case it was supposed to solve anymore. The tool is still live, but nobody’s opened it in months.

That’s not a hypothetical. It’s the default outcome across grocery right now, and the industry keeps issuing press releases like it isn’t happening.

Most of that capex never got signed off by “the board” in the vague way people like to blame. A CTO or CIO built the business case, a CFO signed the number, and the board wanted the comfort of knowing its company wasn’t the one that sat out.

Grocers still run on razor-thin net margins. That’s the environment these AI pilots got funded in, which makes the whole thing worse, not better.

Somewhere in a testing environment right now, a chatbot pilot from eighteen months ago is quietly dying. Nobody killed it. Nobody’s writing the postmortem. It just stopped coming up in the QBR.

That’s the graveyard. Almost nobody wants to admit how full it already is.

The Data Nobody Wants On The Board Slide

Here’s the number that should open every grocery AI strategy meeting instead of getting buried three slides deep in an appendix.

MIT’s NANDA initiative concluded that despite $30–$40 billion in enterprise AI investments, 95% of generative AI pilots produce zero measurable impact on the P&L.1 The researchers looked at the results of 300 publicly-disclosed AI deployments, surveyed 350 employees, and conducted 150 executive interviews. and the failure wasn’t about model quality. These failures largely came down to integration and priorities that never connected to an actual business problem.2

(Please note: this stat has taken its own share of hits since it went viral, with critics saying the methodology gets oversimplified. Fair warning before you drop it into a board deck cold. That said, it doesn’t undercut the pattern grocery operators are living through though, and the research below backs that up from a different angle.)

Gartner isn’t offering much cover either. Last year, the firm expected over 40% of agentic AI projects to be canceled by the end of 2027, blamed on ballooning costs, murky business value, and risk controls nobody bothered building before launch.3

Meanwhile, the money keeps moving. FMI reported that US grocery capex-to-revenue ratios have ballooned to 1.9%4 (with Walmart hitting 3.5% to 4%). Over 80% of food retailers are actively expanding technology spend to drive operational efficiency.

Capex is up, cancellation risk is also up, and most of what’s funded never touches the bottom line. This is not a strategy. It’s a lottery ticket with a seven-figure buy-in, and grocers keep buying more tickets.

Grocery also carries three handicaps most industries never have to deal with. Their ERPs are older than cloud computing itself, the supply chains have dozens of handoff points where data quality rots, and the industry margins are thin enough that a six-month ROI delay kills a pilot before it gets a fair shot.

None of that will shows up in the pitch deck.

The Boardroom Fight Nobody Talks About Publicly

Picture the room. A grocery CTO is staring down a renewal contract for a “predictive AI platform” that hasn’t moved a single operational metric yet.

Across the table, the vendor’s slide deck still says “transformational.” Their renewal ask is up 22% year over year. And nobody on that side has set foot in a distribution center floor since the deal closed.

The CTO asks the only question that matters: what did this move on shrink, forecast accuracy, labor hours saved? The answer pivots into softer language: foundational infrastructure, the next phase, features slated for “Q3” that were supposed to ship eighteen months ago.

The vendor can't answer because the pilot was never built to. It was built to answer a question asked a year earlier, several floors up: would this company show up in a trade article as the retailer that did nothing while a competitor announced an AI push?

The board bought insurance against that headline. That's reputational risk management, and it's why metric-free pilots are still getting funded a year after the failure data went public.

The CTO isn’t the villain. Neither is the vendor rep. Both are negotiating over a metric that was never the actual goal of the money behind it.

A pilot funded to protect a headline was never going to survive a real P&L review.

Three Rules For Killing Vanity AI Before It Kills Your Budget

Grocery leaders don’t need a new framework. They need permission to say no more often, and something better than a headline to hand the board.

1. No pilot gets funded without a named operational metric before day one. Shrink rate works. Forecast accuracy works. Labor hours per case picked works. If nobody can name the specific P&L line it’s supposed to move, it doesn’t get capex, it gets a whiteboard.

2. Every pilot gets a 90-day kill clause, no exceptions. That’s a hard date on the calendar. By day 90 the project shows a measurable delta against its named metric or it dies. Vendors will fight this one hard, and that fight is exactly how you know you’re negotiating seriously.

3. Back-office AI gets funded before anything customer-facing. Demand forecasting, inventory accuracy, shrink reduction, these have an actual ROI track record in grocery ops. Chatbots and generative shopping assistants photograph better in a board deck. They rarely pay for themselves.

None of this is complicated. It just gives a board something better than a headline to point to the next time a competitor announces its AI initiative. A capex rule nobody can second-guess in an earnings call beats a chatbot demo every time.

A one-page version of these three rules, with text to paste straight into a vendor contract review, is available on the Resources page.

Has your organization seen actual, measurable P&L yield from GenAI pilots, or are you just burning capex to satisfy the board?

You Can’t Prompt Produce is a five-part series on how the AI grocers keep shipping, and most shoppers keep skipping.

1. The AI Mass Adoption Mismatch

2. The 95% Graveyard

3. Your Chatbot Sucks

4. Ten Million Against Thirty-Six Thousand

5. Five Steps to Fix How Grocers Greenlight AI

This is the first in a 5-part series, You Can’t Prompt Produce, on the AI grocers keep shipping, and most shoppers keep skipping.

Scroll through any conference keynote or ‘thought leader’s’ post this year, and you’ll hear the same assumption stated as settled fact: everyone uses AI now, daily, instinctively. Many point to Pew’s latest number: 49% of US adults now use AI chatbots.1

That number is not nothing. It’s just smaller and stranger than the hype implies. Grocers have the most at stake here, because their actual customer base looks like the excluded half of the country, not those on the tech conference stage.

What “using AI” actually means

Let’s get back to that number. Pew found 49% of US adults now use AI chatbots, up from 33% in 2024 and 23% in 2023. ChatGPT alone reached 44 percent of adults, up from 18 percent in 2023 and 34 percent in 2025.

But what do they mean by “use”?

Dig deeper, and you’ll find that only 24% use chatbots daily. Half the country has never opened one. Another chunk opened one once, maybe out of curiosity, and never went back. Trying something and living inside it are different behaviors, and the topline adoption number conflates them into one figure.

Layer on age and that gap stops being a footnote and starts being the whole story. 66% of those 18 to 29 ‘use’ it, and that number drops to 42% for 50- to 64-year-olds, and 23% for those 65 and older. Reverse those numbers, and you get the plain sentence nobody puts in a keynote: most Americans over 50 have never used a chatbot.

Education plays a role. A Brookings analysis released alongside Pew’s numbers found a steep education gradient, with generative AI use hitting 67% among adults with a bachelor’s degree or higher vs. 24% for those without.2

And geography adds a third axis. Microsoft’s county-level diffusion data for early 2026 found metropolitan counties carry an average AI user share of 32.9%, more than twice the 16.2% average in rural counties.3 Rural America is using AI at roughly half the metro rate.

(None of that is the same conversation as passive AI, such as the recommendation feeds and spam filters running quietly in the background. That layer is functionally universal. This isn't the same as opening a chat window and typing a prompt.)

Why the industry can’t see it

Full disclosure: I live in San Francisco, a city that tech journalist Kara Swisher once called “assisted living for millennials.” It’s hard to escape AI in these parts, and that can easily warp perceptions of what happens outside of the Bay Area bubble.

The people making the loudest claims about universal adoption are the same demographic where it's already near-universal: under 40, college-educated, urban, knowledge workers. A chatbot slots into their job at a keyboard; it has no obvious slot in stocking shelves or driving a route. Ask SF Bay Area techies if "everyone" uses AI, and they’ll answer based on everyone they know.

That's not bias. It's sampling error. Silicon Valley is measuring a room and reporting it as the country.

The wrong playbook

Most grocers are trying to mimic the wrong retailers: Walmart, Amazon, and Instacart.

Walmart’s own online numbers make the point more precisely. Lower-income households are still Walmart’s largest online grocery segment by volume. But its fastest-growing slice, the one driving recent gains, is households earning $200,000 or more, growing almost five times faster than the overall base.

That’s exactly where Walmart pointed its AI efforts. In March, it embedded Sparky, its shopping assistant, directly into ChatGPT and Gemini, chasing what CEO Doug McMillon called a “native AI experience.” Amazon and Instacart built the same pattern into their own tools.

That’s three layers of mismatch, not one. Online grocery is still only about 19% of total US spend.4 Inside that minority channel, the affluent shoppers Amazon, Instacart, and Walmart are chasing are a minority too.

All three retailers have the talent, scale, and capital to make those bets. Most grocers don't, but they keep copying that playbook anyway. They build in-house chatbots that go unused, aimed at a shopper base that doesn't look like the one Walmart, Amazon, and Instacart are chasing, in age, income, education, or geography.

Copying Walmart’s answer to a different question doesn’t fix it.

The mismatch

The AI mass adoption story is real. It’s just landing almost entirely on one narrow slice of the population, the slice that also happens to write the roadmaps and fund the pilots. Call it a mismatch.

And Silicon Valley is still clueless about who’s actually in the grocery aisle.

Is your non-tech inner circle, or your actual store’s shopper base, prompting AI tools on a weekly basis? Or are we all just describing the same twelve people in San Francisco to each other?

You Can’t Prompt Produce is a five-part series on how the AI grocers keep shipping, and most shoppers keep skipping.

1. The AI Mass Adoption Mismatch

2. The 95% Graveyard

3. Your Chatbot Sucks

4. Ten Million Against Thirty-Six Thousand

5. Five Steps to Fix How Grocers Greenlight AI

Here’s the problem nobody puts in the board deck: every order routed through a marketplace app, white-labeled or not, is an order where the grocer never saw the search, never touched the loyalty data, never got a cut of the ad revenue. As digital penetration climbs, the ownership of the customer quietly disappears underneath it.

Grocers can still turn this around, but not by trying to out-market Instacart. The move is to make their own tools the default habit, so a shopper opens them before they even think about a competitor’s. Whoever gets that first click keeps the margin and the customer.

Everyone else is just fulfilling someone else’s sale.

This video is a summary of The Marketplace Trap, a 10-part series on why grocers are handing customer ownership and profit margins to platforms, and what they can do to take it back.

The full series:

1. Ownership > Growth

2. The $1,000 Problem

3. Digital Penetration Is a Vanity Metric

4. Customer Ownership Is the Real Moat

5. Mid-Tier Grocers Are Stronger Than They Think

6. The Instacart Search Question

7. The Retail Media Margin Shift

8. The Loyalty Illusion

9. Defensive Distribution Strategy

10. If I Were a Grocery CEO

    Bonus: The Marketplace Dependence Curve

I laughed hard when I saw this 80 yo brass mechanical grocery list on Reddit. It’s pre-loaded with 40 categories, and you slide the little tabs left to flag what you need. There’s no battery, no sync issues, and no sponsored results pushing a Mongolian butter brand you’ve never heard of before. You walk the store, you slide the tabs back as you go, and you’re done.

Then I stopped laughing, and PTSD set in.

I spent months working on a few grocery list pilots for a national grocer. We had beautiful designs, detailed stakeholder decks, and a product manager (me) who genuinely believed we were going to change how households shop. Every project cratered. The plan was solid. The technology wasn't there. Other teams weren't either.

That brass tab-slider from the 1940s still works better than anything I’ve seen demoed since. It had two states per item, no account to create, and no location permissions to dismiss.

The biggest competitor in grocery list tech isn't Instacart. It isn’t an 80-year-old piece of brass. It's a pen and the back of an envelope.

The Kroger app has a 4.8-star rating. So does Walmart Grocery. Instacart sits at 4.9. These apps do a lot of things: weekly ads, coupons, pharmacy, fuel points, order tracking. And yet the grocery list, the feature every household actually uses every week, is the worst part of all of them. A 4.8 with a broken core feature means the bar for grocery apps is pen and paper.

We are using 2010-era digital checklists in an era of predictive AI, and giving them five stars because they didn’t make us cry in the cereal aisle.

The Friction Is the Feature (And That’s the Problem)

Picture Tuesday evening. You’re standing in your kitchen, halfway through prepping chicken piccata. You’ve got your phone out because you remembered you need capers, and now you’re trying to add them to your grocery app while one hand is covered in lemon juice.

You open the app. It wants your location. You dismiss the prompt. You tap the search bar. The keyboard slides up and covers the “Add Item” field. You tap again. You type “capers.” The app serves you four sponsored results: a brand you’ve never heard of, a bulk 5-pound jar from a restaurant supplier, some kind of artichoke antipasto, and buried fourth, the Goya 4-ounce jar you actually buy.

By the time you’ve added the item, you’ve overcooked the chicken.

That’s not an edge case. That’s grocery list apps performing exactly as designed, which is to say, poorly. It compounds: laggy household sync, dead wifi mid-aisle, item entries that default to wrong sizes, and zero awareness that you just cooked a meal and already know what you need.

Grocery apps were never built around the actual moment of use.

The Ad-Tech Hijack

Here’s what happened. Grocers looked at their apps, saw a captive audience with purchase intent, and turned them into billboards.

Retail Media Networks now generate high-margin billions. Instacart’s retail media arm pulled in $1 billion in 2023. Every major grocer is right behind them. CPG brands pay serious money to sit at the top of your search results.

The operator math is easy. The shopper math is what you feel every time you open the app.

When you type “butter” into your grocery app and get three sponsored results for brands you don’t use, in sizes you don’t buy, sorted by who paid the most, the app has stopped working for you. It’s working against you.

This is where utility breaks down. The grocery list is the highest-intent moment in consumer retail. You’re not browsing. You’re not impulse-buying. You know what you want. And the app’s job, at that exact moment, has been flipped from “find what you want” to “show you what someone else paid to show you.”

Every sponsored result injected into a list search is a direct tax on the person who opened the app. It adds time, creates confusion, and often forces a second search. The grocer has monetized user frustration.

Brain-Dead “Household Intelligence”

There’s a concept in software called “cold start.” No data on the user, start from scratch. It’s a well-known problem in recommendation systems. Netflix solved it over a decade ago.

Every time you open a grocery app, it cold-starts.

It doesn’t know your household goes through a gallon of 2% milk every five days. It doesn’t know you buy coffee filters in 100-count packages and you’re probably running low. It doesn’t know you’ve ordered chicken thighs every third week for eight months.

It has your order history, timestamps, and frequency data. And it does nothing with any of it.

The average American household shops from a stable repertoire of roughly 150 to 300 SKUs, with modest seasonal variation.1 Grocery shopping is almost entirely cyclical, and yet every session starts the same way: blank list, search bar, waiting for you to remember everything yourself.

Recipe integration makes this worse. Most apps that offer recipe-to-list functionality scale the ingredients and then dump them into your list without checking what you already have. There’s no pantry state, and no “skip if in stock” toggle. You get told to buy paprika when you have three jars at home.

The Walled Garden Fallacy

Grocers build their apps on an assumption that is empirically false: that you shop with them exclusively.

Nobody does this. The actual pattern for most American households looks more like Costco for paper goods, Trader Joe’s for snacks and seasonal items, the local grocer for produce and fresh meat, and maybe an ethnic grocery or farmers market on top of that. Four or five stores for a single household’s weekly food supply.2 None of those stores share data. None of their apps talk to each other.

The grocer’s app assumes 100% wallet share. Industry estimates put the typical primary grocer’s actual share of household food spend closer to 16% to 25%,3 directionally, though that number varies significantly by market. A rural household with one dominant grocer nearby looks nothing like an urban household splitting trips between Whole Foods, Trader Joe’s, and a neighborhood butcher.

Third-party apps have tried to solve this. AnyList, OurGroceries, and others position themselves as store-agnostic. The problem is that without deep API integration, they’re just digital paper. They can’t access past orders, real-time inventory, or fulfillment routing. AnyList didn’t fail because the interface was bad. It stalled because a great list app without grocers’ data connections is a notepad with pretty UX.

Grocer APIs, where they exist, are proprietary, poorly documented, and locked behind partnerships that protect the grocer’s own app. It's not negligence. Locked APIs are a margin protection strategy.

An open standard connecting a neutral list app to Kroger, Publix, and H-E-B would make price comparison trivially easy, and that destroys margin. The wall stays up because the wall protects the business model.

Which also means no grocer fixes this voluntarily. Any grocer that opens its API to a neutral list platform is handing a price comparison tool to every household it serves. That’s not a product decision. It’s a business model decision, and the economics run the wrong way.

The Holy Grail

Here’s why any of this matters beyond UX pedantry.

The grocery list is the single most valuable piece of commercial real estate in retail e-commerce, and nobody has properly built on it.

Think about what the list actually represents. You built it at home, before you opened any app, and before any brand got near you. The person who puts capers on a list is going to buy capers. That’s not true of someone who sees a banner ad for capers and maybe clicks through. List conversion isn’t browse conversion. It’s not even close.

That said, this moment is already being contested. Grocer apps push weekly circular notifications. Recipe features inside store apps seed the list with sponsored ingredients. Alexa adds items to an Amazon list by default.

The window of opportunity is real, but it's closing. Whoever captures the list-building session early enough, before the shopper opens the grocer’s app, owns the uncontaminated version of that intent. That window is the prize.

Marketers have a term for the moment a consumer first becomes aware of a need: Zero Moment of Truth. Google coined it in 2011 and built much of its search advertising model around it.

The grocery list is earlier than that. It’s pre-ZMOT: the moment a household commits to a purchase based entirely on internal knowledge of their own consumption, before any brand or algorithm weighs in. The app that lives at that moment owns the most intimate commercial relationship in consumer retail.

Whoever controls the grocery list controls the basket. Whoever controls the basket controls grocery e-commerce.

The obvious question is: why hasn't anybody won this yet?

Amazon is the most credible incumbent answer. Subscribe and Save is a crude but functional version of replenishment cycle intelligence. Alexa captures voice-driven intent at the moment it forms. Whole Foods gives Amazon purchase history across a major grocery format. If anyone had the data and distribution to build what I’m describing, it’s Amazon.

But Amazon can’t be the neutral layer, because Amazon is a grocer competing on its own platform. Kroger won’t open their APIs to a company that sells groceries in direct competition with them. A neutral third party without that conflict is the only entity grocers have any rational incentive to cooperate with, which is exactly why the winner here doesn’t look like a retailer at all.

It looks like a company that already owns the upstream moment. Think of a meal planning platform with tens of millions of active households, or a recipe app that lives on your phone before you ever think about where to shop. Any service that captures what your household intends to eat this week, before any grocer gets near the session, is sitting on the most valuable real estate in grocery commerce.

That company, with the right API partnerships, becomes the demand layer that sits above the entire fragmented grocery ecosystem.

The platform that wins will be store-agnostic, routing list items to the lowest-cost or fastest fulfillment option across your actual shopping ecosystem.

It will have genuine household intelligence that learns consumption cycles, suggests replenishment before you run out, and connects meal plans to list generation without making you own three jars of paprika. It makes money at the list-building stage, through placement that fits the context instead of hijacking it. Suggest the off-brand olive oil when I’m browsing, not when I’ve already typed in the one I want.

These platforms exist. The deep grocer partnerships don't.

The person standing in aisle nine, consulting their phone at 6:47 PM on a Wednesday, is not browsing. They’re executing. Execution requires frictionless retrieval, not an ad for a product they didn’t ask for.

The prediction models exist. The API infrastructure can be built. The household data is sitting in purchase histories that grocers are actively failing to use.

The obstacle was never engineering. It’s that grocers spent a decade monetizing friction instead of eliminating it, and the ad revenue was good enough that nobody felt the urgency to build something better.

And the one company with the scale to force the issue is structurally prevented from being the neutral party grocers would actually work with.

That's the opening, and it's not a feature gap. It's a structural one. And it’s sitting there unclaimed, worth more than anyone inside the grocery industry has admitted out loud.

An 80-year-old piece of brass already figured out the interface. The rest is just the will to build it.

PS: Want to embarrass your kids at the store? Amazon carries a version of this thing: https://amzn.to/4fDPD28 (yes, Bezos cuts me a check). More on the tool itself here.

PPS: If you’d prefer a digital version, a reader vibe-coded just one for you (it’s wonderfully ridiculous): https://mysimplelist.base44.app.

PPPS: On the same day as the post, a Walmart EVP boasted that they launched an AI-powered shopping list. Can you see where they failed?

This is the final post in a 5-part series, The Unit Problem, on how grocers can reverse unit volume contraction. Here’s where we started.

Most grocery apps greet you with a coupon library. Browse hundreds of offers, find the relevant ones, clip each manually, and remember them at checkout. Shoppers doing that in 2026 are already doing more work than they should.

One banner figured out a different greeting. She opens the app and sees: “We’ve saved you $4.75 this week. Already applied.” No clipping. No browsing. The grocer did the work before she even wrote her list.

44% of consumers are leaning harder on coupons and promotions right now.1 That number will not surprise anyone reading this. What should surprise you is how few grocers have figured out what that shopper actually wants from the experience.

Every extra click you make her take is unit volume walking out the door to someone who did the work for her.

What AI Is Actually Good For Here

68% of food retailers surveyed by FMI in early 2026 said they use AI in their business, up from 47% a year ago. Technology spending averaged nearly 2% of total sales in 2025, double what it was in 2024.2

Most of that investment is going into consumer-facing AI: chatbots, search improvements, and product recommendation carousels. These are real applications. None of them are where the unit-volume leverage lives. (Review the Grocery AI Pyramid.)

The AI that actually moves units works at the household prediction level. It is not visible to the shopper as “AI.” It shows up as an offer that feels like the grocer actually knows her.

McKinsey’s data shows grocers currently report 35% of promotions are fully personalized, and expect that share to reach 55% in two to three years.3 The grocer that reaches 55% by mid-2027 instead of late-2028 has a loyalty and unit advantage during the exact window when shoppers are most open to switching banners.

How the Engine Actually Works

The architecture is not complicated. It starts with purchase velocity: AI identifies which households are running low on which categories based on purchase cadence, pack size, and days since last purchase. A household that buys a 32-ounce Greek yogurt every 10 days is probably out by day 8. That is a known fact sitting in POS data that most grocers are not using.

From there, the offer writes itself. Not “buy 2 get 1 free” on a shelf tag, but a push notification: “You’re probably out of Greek yogurt. Buy 3 and save $2.50, good until Saturday.” That unit pull is real because the household was going to buy one anyway and is now buying three.

The last piece is basket completion. When a digital cart reflects a recognizable mission but is missing something the household always buys on that mission, surface one targeted offer to close the gap. A household that always pairs chicken thighs with salad dressing but only has chicken in this basket gets one prompt: “Complete the meal, 20% off your usual dressing today.”

These offers are not generic. The unit volume goes up because the prompt answered a question the shopper was going to answer somewhere else.

The 44% Coupon Cohort

The shopper clipping coupons in 2026 is not who you think she is. She is not stretching a tight budget and hoping for the best. She is a high-frequency, high-intent shopper telling you exactly what she values. The problem is the coupon experience most grocers hand her.

A paper coupon in a Sunday insert requires her to find it, clip it, remember it, and present it at checkout. A digital coupon library requires her to browse hundreds of offers, identify the relevant ones, and manually clip each before she shops. Both formats add friction at exactly the moment you want it eliminated.

The move is automatic application. Before each shop, the system identifies every offer in the weekly set that is relevant to the household’s purchase history and applies it to the loyalty account without any action required. She opens the app and sees: “We’ve saved you $4.75 this week. Already applied.” She did not have to do anything to earn that. The grocer did.

The behavioral consequence is straightforward. A shopper who gets that feels recognized. She connects that feeling to the banner. She is less likely to do her coupon sweep at a competitor because the competitor requires her to do the work herself.

This is not a complex feature to build. It is a policy decision about how coupons work, combined with a loyalty data integration that already exists in most modern grocery tech stacks. The tech is easy. The willingness to change is not.

Connecting the Series

The promotional engine is the piece that connects everything. It is the connective tissue that makes everything in this series work.

The mission-based app architecture from the second post generates the basket start. The friction fixes from the fourth post keep shoppers in the app long enough to build a full list. The AI engine fills it with personalized offers and bundle recommendations. The GLP-1 telehealth partnership from the third post feeds new household acquisition into the loyalty database, where the engine immediately begins learning the new shopper's behavior.

These are not four separate strategies. They are one strategy with four execution layers. The grocer running four disconnected initiatives loses to the one that built a system.

The 90-Day Starting Point

Pull 12 months of POS and loyalty transaction data for the top 20 SKUs by household penetration. Run a repurchase interval analysis: average days between purchases by SKU and household segment. Identify the five SKUs with the most consistent repurchase cadence. Those are the first five items for the bundle offer test.

Build a control group of 500 loyalty households and a test group of 500. Push a replenishment offer to the test group for those five SKUs in week 3 of the next promotional cycle. Measure unit pull at the SKU level and total basket impact at the household level over four weeks.

That test costs almost nothing. It runs on existing loyalty infrastructure. It produces a result the CFO can read. And it is the data that justifies everything else in this series.

Grocery unit volume will not recover because the macro improves. It will recover because individual grocers build systems that earn back the trust and trip frequency of shoppers who are currently hedging across four banners and pruning every cart.

The grocers that pull ahead will be the ones that figured out how to win during the contraction. The ones that waited for it to end will find the winners already dug in.

The Unit Problem is a five-part series on why US grocery unit volume is falling and what traditional grocers can actually do about it.

1. The Unit Problem

2. Your Tools Are Teaching Shoppers to Spend Less

3. The Partnerships You Haven’t Made Yet

4. You Fixed the Wrong Friction

5. $4.75, Already Applied

This is the fouth post in a 5-part series, The Unit Problem, on how grocers can reverse unit volume contraction. We began here.

The grocery industry spent five years making the physical checkout faster. Amazon walked into grocery, scared everyone with what frictionless looked like, and the industry responded by throwing billions in capital at the last thirty feet of the shopping journey. With exceptions, most of it failed12.

The physical checkout is not where unit volume is leaking.

The Three Friction Points Nobody Fixed

The shoppers who abandon their carts at digital checkout are visible. The transaction fails, and the data captures it. The shoppers who get three substitutions they did not want on their pickup orders, accept them, and never place another order are harder to see. And the shoppers who built a mental list on Sunday night, opened the app Monday morning, could not find two items, and quietly decided Instacart was easier never show up in the data at all.

There were no failed transactions, no abandonment events tracked. The orders never happened, baskets never got built, and unit volume crashes.

Grocery has not solved that problem. It has been optimizing around it.

There are three points in the digital grocery journey where unit volume actually disappears. Checkout is not one of them.

The first is list-building. The shoppers who plan their trip in the app are the highest-value shoppers in the building. They arrive with intent, buy closer to their full list, and are far more likely to be loyalty members you can actually learn from.

Most grocery apps treat list-building as a notepad bolted onto a storefront. Search returns poor results for anything but an exact match, and there is almost no personalization pulling shoppers toward items they have bought before. They build half a list, get frustrated, and finish it in their heads on the drive over. Half those mental items never make it into the basket.

The second is substitution. Shoppers who place a pickup or delivery order and receive three substitutions they did not want do not complain to customer service. They place their next order somewhere else.

Substitution is where the grocer’s inventory problem becomes the shopper’s problem, handed off with a notification and a hope. The industry has put real money into substitution logic on the fulfillment side, better picker tooling, smarter item matching, and almost nothing on the shopper side.

Shoppers should be able to pre-authorize preferences by category, rank what they will and will not accept, and trust that the system knows they will take Fage but not store brand on Greek yogurt. Most apps give them a binary: accept or reject after the fact. One of those outcomes is a satisfied customer. The other is a churned one.

Grocery has the lowest digital cart abandonment rate of any retail category, around 57%, a figure that has held across multiple industry analyses. Most grocers read that as a win. The likelier explanation is that volume loss is happening before the cart, in the list-building and discovery phase, where nothing gets tracked.

The third is pickup and delivery window selection. This one sounds operational, but it is a conversion problem. Shoppers who cannot find a window within two hours of when they need it either abandon the order or move to a competitor with more availability.

Kroger, Walmart, and Instacart have all invested in dynamic slot management. Most other grocers are still running static windows on a 24-hour refresh cycle. The shopper who wants a 5-6 PM delivery on Thursday and sees nothing until Saturday morning is a shopper Instacart just acquired.

What Checkout Optimization Actually Solved

Digital penetration varies widely by banner and market, but the direction of travel is the same everywhere: the share of orders touching a digital surface before checkout is growing, and the friction points upstream are growing with it.

Faster checkout reduced one real cost: associate labor at the front end. On margins that leave almost no room for error, that matters, and nobody should pretend otherwise. Scan-and-go also improved the experience for a specific type of shopper, the one buying eight items who does not want to stand in any line at all.

Those shoppers were already loyal. They knew what they wanted. They were not the conversion problem.

The conversion problem is the shopper still deciding whether any of this is worth the effort. They tried delivery twice and got bad substitutions. Their app keeps surfacing items that are out of stock. They cannot find a window that fits. They are one friction event away from deciding Instacart is just easier, margin be damned.

No amount of physical checkout optimization reaches that shopper.

Where the Next Infrastructure Dollar Goes

List-building, substitution preference, and slot availability are not glamorous investments. They do not photograph well in a press release. Nobody is cutting a ribbon on a new substitution preference UI.

But these are the three places where the digital grocery shopper decides whether to stay or go. Get list-building right, and unit volume grows among the shoppers worth keeping. Get substitution right, and you stop losing pickup and delivery customers after their second bad experience. Get slot availability right, and you stop sending Instacart new users every time your Thursday evening windows fill by noon.

The physical checkout investments made the stores faster for shoppers who were already there.

Grocers that do not fix these three things are not losing a price war. They are losing to a slow app, a bad substitution, and a full pickup window. That never shows up in the quarterly ops review. It shows up six months later, in a cohort chart nobody wanted to explain.

The Unit Problem is a five-part series on why US grocery unit volume is falling and what traditional grocers can actually do about it. Next, we'll explore the promotional engines and close with planning for the next 18 months.

1. The Unit Problem

2. Your Tools Are Teaching Shoppers to Spend Less

3. The Partnerships You Haven’t Made Yet

4. You Fixed the Wrong Friction

5. $4.75, Already Applied

This is the third post in a 5-part series, The Unit Problem, on how grocers can reverse unit volume contraction. Here’s the first.

Three forces are pulling grocery unit volume out of traditional grocers right now, and none of them are competing retailers.

The first is GLP-1 adoption. The second is gasoline prices. The third is meal planning. Each one is forming purchase intent before shoppers walk into any store or launch an app. Almost no grocer has built the partnerships to intercept all three.

The GLP-1 Basket Is Already Here

16% of consumers report current household GLP-1 use, with another 7% reporting past use, according to McKinsey’s State of Grocery North America 20261. That is not a niche population. That is a meaningful share of grocery-buying households that have reorganized what goes in their carts.

The category shift is specific. After GLP-1 adoption, spending falls in chips, sweet bakery, soft drinks, cookies, candy, and desserts. It rises in nutrition bars, meat snacks, fresh produce, and yogurt. A grocer’s center store is built for the basket these shoppers are actively dismantling. The perimeter is where their spend is going.

The question is not whether to respond. It is whether you own the moment when new GLP-1 users reorganize their grocery identity, or watch it happen from a distance.

That moment happens exactly once per adopter. The first four to six weeks after starting the medication, eating patterns are disrupted, and old purchasing habits break down. Whoever reaches them in that window with a relevant, easy-to-execute basket owns their grocery behavior going forward.

The telehealth platforms that prescribe and support GLP-1 users (Noom, Hims & Hers, Ro, and Weight Watchers) have direct relationships with these shoppers at exactly that moment. A co-branded nutrition program, where new subscribers get recipe ideas and curated digital baskets of high-protein, low-sugar, nutrient-dense items available at your stores, puts you in that window.

The telehealth partner promotes heavily during onboarding and ongoing care. The grocer handles the assortment, the loyalty integration, and weekly basket nudges calibrated to what a GLP-1 household actually buys.

ShopRite has already moved here, launching wellness kits for GLP-1 patients to bridge the gap between pharmacy and nutrition. A wellness kit is a start.

The co-branded basket model with a telehealth platform is the more ambitious version, and it is still emerging. No operator has built it at scale. But the conversation with a platform like Noom or Ro is worth starting now, before a competitor does.

Nearly 90% of grocers expect to increase shelf space for high-protein products by more than 10% over the next two to three years, according to McKinsey. Shelf space without a demand generation strategy is just inventory cost. The telehealth partnership is the acquisition channel that fills the expanded shelf with actual shoppers.

The Gas Price Problem Is Solvable

Kroger and other national grocers have run a fuel rewards program for twenty years. Most operators at every other tier have not. When gas prices climbed over 20% across the US in March 2026, that gap is no longer just a missed loyalty feature. It is a weekly pain point shoppers feel every time they fill up, and grocers that address it directly earn goodwill a competitor sitting on the sidelines cannot match.

It’s not a complicated idea. Every dollar spent in the store earns fuel cents per gallon that’s redeemable at their stations or within a partner fuel network. A loyalty card or app QR code handles redemption at the pump. Shoppers connect the grocery trip directly to relief at the gas station. That connection changes the calculus on trip frequency, driving distance, and banner loyalty.

Gas at 20% above last year makes fuel rewards less of a perk and more of a response. A grocer without a fuel reward is invisible on the issue shoppers care about most right now.

"We know gas is expensive. Shop here and we'll help." Simple, direct, and emotionally resonant for a household running the numbers on where to spend this week.

The Meal Planning Play

The technology that turns recipe content into grocery baskets exists. Many grocers have not plugged into it.

Chicory runs a shoppable recipe network across more than 5,200 food blogs and recipe sites. Its “Get Ingredients” button sits on content ranging from Martha Stewart to thousands of independent food bloggers. Shoppers find a recipe, click the button, select a preferred retailer, and the full ingredient list populates in that grocer’s digital cart. Chicory is already integrated with more than 70 retailers, including Albertsons, Giant Eagle, and Wakefern.

The integration requires no proprietary engineering and no national platform deal. There is no good excuse for not having it. The playbook already exists. The content network already exists. And the shopper behavior is already there.

Shoppers planning a meal add the complete ingredient list. Shoppers browsing without a meal anchor add items they remember they need. When the basket is built around a meal, every item belongs. There is nothing to prune.

This is not an emerging opportunity. It is an available one. The question is why more grocers have not taken it.

The shopper's next basket is being built right now, on a telehealth platform, at a gas pump, on a recipe site. Most won't be. Will you?

The Unit Problem is a five-part series on why US grocery unit volume is falling and what traditional grocers can actually do about it. Next we'll look at the bottom of the purchase funnel.

1. The Unit Problem

2. Your Tools Are Teaching Shoppers to Spend Less

3. The Partnerships You Haven’t Made Yet

4. You Fixed the Wrong Friction

5. $4.75, Already Applied

This is the second post in a 5-part series, The Unit Problem, on how grocers can reverse unit volume contraction. We started here.

The grocery apps and sites were supposed to drive basket size. They’re doing the exact opposite.

A shopper building a cart online has something no in-store shopper has ever had: a running total, visible at all times, and updated with every item added. The behavioral consequence is predictable. When the number gets uncomfortable, items come out. The planned items remain, but the high-margin items, the impulse additions and premium versions, would have gone in the cart without a second thought in the store.

This is basket pruning. It is a structural feature of the digital grocery channel. The shoppers who removed three items before checkout are not behaving irrationally. They’re doing exactly what the interface trained them to do.

Showing the cart total is not the problem. What the shoppers think about before they get there is.

McKinsey’s State of Grocery North America 2026 documents the channel shift driving this pattern. Purchase frequency increased 5% year over year from August 2024 to August 2025, while units per trip fell across every major grocery channel1. Shoppers are coming more often and buying less each time, splitting missions across banners, channels, and delivery platforms based on where the value lands this week.

I saw this same pattern eight years ago: omnichannel shoppers allocate 31% of their grocery spend to a given retailer, compared with 26% who only shop in-store. Cross-channel shoppers spend more. The digital channels, as most grocers have built them, are quietly eating that advantage.

The fix is not incremental.

From Convenience Tool to Mission Builder

Current grocery apps and sites are built around a search bar and a cart. Shoppers arrive with vague lists, search for items, and add them as they watch the total grow. Then they remove anything that pushes past a mental threshold. The channels are neutral containers, and neutral containers get pruned.

Mission builders change the entry point. Instead of a blank search experience, shoppers arrive at intent-driven entry points: “Tonight’s Dinner,” “Stock the Pantry,” “Reorder Essentials,” and “Weekly Value Grab.” Each path feeds a curated or predicted cart of products built for that specific trip type, with items pre-selected from past purchases and the current promotions.

Shoppers who enter through “Tonight’s Dinner” will not prune a basket. They’re choosing between two meal configurations that are already complete. The usual behavior has nowhere to operate because the carts were assembled before they touched them.

Kroger and some grocers are already seeing more than 20% of online orders come from recommended or preassembled carts, according to McKinsey. The behavior exists. Most grocers are not building toward it deliberately.

The Smart Basket Push

The highest-leverage version of this is a pre-built weekly basket delivered to the shopper before the shop begins.

The mechanic: every Thursday, the app pushes a household-specific replenishment basket built from purchase cadence, average pack size, time since last purchase, recommended recipes, and the week’s personalized offers. The shopper opens it, reviews, adds or removes items, and confirms. The cart starts full, not empty.

The behavioral difference matters. A shopper editing a complete cart removes less than one building from zero. Defaults are sticky. Items that go in before the shopper touches the experience tend to stay in.

Reframing the Checkout Moment

Before the cart total appears, reinforce the savings summary first: loyalty credits applied, bundle discounts captured, and private label savings versus national brand equivalents.

The sequence matters. Shoppers who see “You saved $8.40 this week” before they see the cart total are in a different mindset than those who see $94 and start pruning. Right now, most digital tools present the number that triggers anxiety before the number that provides relief.

Pair this with a basket completion prompt. When a digital cart reflects a recognizable build but is missing something, surface one targeted offer to fill the gap. Not three. One, specific to what is actually missing.

The grocer that knows a household always buys chicken thighs and salad dressing together. When there's only chicken in the cart, surface the salad dressing.

The Physical Store as Digital Activation Engine

The same shoppers who pruned their baskets online are standing in your store with full purchase intent and no running total in their heads. Digital is invisible at this moment when shoppers are most ready to buy.

End caps, fresh perimeter displays, and seasonal fixtures carry enormous foot traffic but don’t deliver digital conversions. This is free media inside a physical space the grocer already owns, and it is almost entirely unused. Adding a QR code to a display that links to a curated basket costs almost nothing to produce. It converts a physical browse into a digital cart start.

Shoppers who scan tend to complete the cart. The ones who do not scan buy what they remember and leave.

Consumers who are buying fewer items, trimming non-essentials, and cross-shopping three banners will not respond to a better search bar. They will respond to an experience that makes value visible before their anxiety sets in. Give them a complete cart to edit, not a blank slate to fill. And make sure it shows up in their Thursday planning window.

That is the difference between digital tools that prune and those that deliver.

The Unit Problem is a five-part series on why US grocery unit volume is falling and what traditional grocers can actually do about it. Next, we’ll cover how partnerships can move unit volume externally.

1. The Unit Problem

2. Your Tools Are Teaching Shoppers to Spend Less

3. The Partnerships You Haven’t Made Yet

4. You Fixed the Wrong Friction

5. $4.75, Already Applied

This is the first post in a 5-part series, The Unit Problem, on how grocers can reverse unit volume contraction.

There is a metric that will never appear on a grocer’s financial report: unit sales. That absence is not an oversight.

When unit volume contracts, grocers’ thin 1% to 3% profit margins get squeezed because fixed costs are spread across fewer items moving through checkout. So the reporting machinery moves to protect the narrative. Identical sales growth, propped up by inflation, leads the earnings call. Basket depth gets bundled into "average ticket size." Raw unit losses get buried in qualitative management discussion. The financials look stable while the volume story quietly deteriorates.

The Numbers Grocers Aren't Showing

Bain & Company went looking for that volume story in their analysis of NielsenIQ’s data1. Unit growth went negative in mid-2025. Then in February 2026 it stepped down sharply enough to pull actual dollar sales lower across the US. By June 2026, units were down 1.8% year over year. Twelve months earlier, they were up 0.1%. That is a nearly two-point deterioration in a single year, and it happened while prices were still climbing 2% to 3%.

Inflation did not cause the unit decline. Inflation was supposed to mask it. It stopped working.

How Shoppers Are Responding

Bain’s analysis points to overlapping pressures rather than a single shock. SNAP benefits were scaled back in late 2025, tighter eligibility rules followed in early 2026, and gas prices climbed over 20% in March. Consumers were already absorbing a 33% cumulative rise in grocery prices since 2019.

Among those trimming their bills, 56% are trading down to lower-priced brands, 49% are buying fewer items, and 44% are leaning harder on coupons. NielsenIQ panel data shows 22% of shoppers are now actively cross-shopping multiple banners to chase the best price on whatever matters most this week.

Value players, from discounters to mass and club, are taking trips from traditional grocers. Gaining shoppers does not resolve the unit problem. People are still buying less overall. The consumer who walks into your store instead of a competitor’s store is still buying fewer items than she was a year ago.

This is a contraction. Gaining share during a contraction is still winning, but it requires a completely different set of moves than the ones that worked during inflation.

The Trip Has Changed

McKinsey’s State of Grocery North America 2026 report surfaces the structural shift underneath the numbers2. Growth is now being driven more by purchase frequency, which increased 5% year over year from August 2024 to August 2025, than by basket expansion. Shoppers are not planning one large weekly mission and executing it fully. They are splitting trips across value stock-ups, fresh and prepared food occasions, convenience-led delivery runs, and targeted coupon sweeps.

The traditional large-format grocer was built for the opposite behavior: big baskets, infrequent trips, and planned purchases. That model is under direct pressure from club stores on staple volume, c-stores and prepared food operators on immediate consumption, discount grocers on price perception, and delivery platforms on convenience.

What this means operationally is that grocers who are optimizing for basket size are optimizing for a shrinking shopping behavior. The grocer that learns to win on trip frequency, capturing smaller baskets more often across more missions, is playing the game consumers are actually playing right now.

What Comes Next

The edge goes to grocers who price sharply on the products shoppers notice, and use promotions, loyalty, and private brands with enough precision to build a value story shoppers can actually trust.

Trust is the operative word. The 33% cumulative price increase since 2019 did not just stretch budgets. It eroded the default assumption that a familiar banner is a safe choice.

Shoppers who used to drop $180 in a cart without much thought are running the numbers now. They know what eggs cost. They know what ground beef costs. They are watching those items as a proxy for whether the store is being straight with them.

That is the real problem underneath the unit problem. Consumers are not just buying less. The implicit contract between grocer and shopper has been tested, and a significant portion of the customer base found it wanting.

Rebuilding that contract is the work.

The Unit Problem is a five-part series on why US grocery unit volume is falling and what traditional grocers can actually do about it. Next, we’ll cover why digital channels became pruning tools, and what to build instead.

1. The Unit Problem

2. Your Tools Are Teaching Shoppers to Spend Less

3. The Partnerships You Haven’t Made Yet

4. You Fixed the Wrong Friction

5. $4.75, Already Applied

A picker walks into a Kroger, pulls up an order, heads to the dairy cooler, and the shelf holding the chocolate milk is empty. The app reroutes. The substitution goes out. The customer gets frustrated. That friction, repeated across millions of orders each week, is where grocery economics quietly bleeds out by the billions each year.

Instacart just bought a fix for that. And it’s a much bigger deal than their press release made it out to be.

Why POS data doesn’t solve this

Here’s a question that sounds like it should have an obvious answer: don’t grocery stores already know what’s on their shelves? They scan everything at checkout. Sell three cans of soup, and the system knows three fewer cans exist.

The problem is that sell-through data tells you what left the store, not what’s on the shelf right now. Inventory counts constantly drift from reality due to shrink, misplaced products, vendor mispacks, employee errors, and theft. Columbus Consulting estimates that 56% of grocery inventory records are actively inaccurate at any given moment.1 The system says 12. The shelf says zero.

“System real time” and “shelf real time” are not the same thing for most grocers. The only way to know what’s actually there is to look, and that meant manual audits on a fixed schedule.

Instacart’s acquisition of Arpalus gives it a computer vision system that turns standard smartphones into shelf-scanning hardware with better-than-95% accuracy. Point a phone at a shelf, and Arpalus maps what’s there, item by item, in real time.

Instacart’s plan is to pipe that into its 600,000-plus shopper network, turning every order fulfillment into a passive shelf audit. A continuous, physical feedback loop on what’s actually in stock.

No grocer has built this at scale. Instacart is about to.

The found rate is the number that matters

There’s one operational metric at the center of all of this, and most grocery tech investments ignore it: the found rate.

The found rate is the percentage of ordered items a shopper actually locates and delivers. Every missed item costs a substitution, a support ticket, or a lost customer. A grocer can build a beautiful personalized recommendation engine and still bleed money on every order if the found rate is soft.

Arpalus directly protects that number by giving pickers real-time intelligence on what’s available, so they don't waste time searching for product that isn’t there.

Consumer-facing AI doesn’t touch this metric. That’s the whole problem.

The number grocers don’t like to say out loud

Out-of-stocks are costing North American food retailers 5.9% of total retail sales, according to IHL Group.2 On margins that already sit between 1% and 3%, that’s not a rounding error. That’s the difference between a good quarter and a bad year. And a loyalty program with points and tiers doesn't survive one too many substitutions.

The customer damage compounds fast in digital channels. A shopper who places a delivery order expects the items they ordered. When substitutions stack up, they don’t complain; they switch. According to FMI, the average American household shops at 5.4 different grocery banners per month.3 Switching costs nothing.

The pyramid problem

If you’ve read The Grocery AI Pyramid, you know the argument. Most grocery AI budgets flow toward the top of the stack: personalized search, generative shopping assistants, chatbots, and other consumer-facing features look good in demos. But the bottom of the stack gets treated like infrastructure nobody wants to put on a roadmap: demand forecasting, inventory accuracy, shelf availability.

Consumer-facing AI is visible. You can screenshot it, show it to a board, and put it in a press release. Shelf data infrastructure is invisible until it fails, and when it fails, it gets filed under ops, not technology. So the budgets keep going to the top.

Instacart buying Arpalus is a direct shot at the bottom. A personalized recommendation engine is worth nothing if the item isn’t on the shelf when the picker goes to grab it.

The part grocers should be worried about

Instacart is building the most accurate, continuously updated map of physical shelf inventory across thousands of stores. They’ll use their own shoppers, their own technology, and even their own carts. The data belongs to Instacart.

CPG brands already pay serious premiums for shelf positioning and promotion compliance data. The grocer who controls that data has pricing power. The grocer who outsources fulfillment to a platform running its own shelf-intelligence layer has handed over that leverage, along with the customer relationship and the delivery margin. It’s a slow transfer, and most operators don’t notice until the renewal conversation.

Caper Carts extend the reach. They’re now deployed in 100 cities across 15 states, with Instacart tripling their store count in 2025. In stores where the carts are running, every pass through an aisle silently updates the shelf map. The more stores adopt, the harder it is to unwind the dependency.

What the acquisition actually signals

With the Arpalus acquisition, Instacart is building at the bottom of the pyramid, while grocers keep spending at the top. The gap doesn't close while you're busy perfecting your chatbot.

Jennifer Shawgo published a post worth reading on the FMI blog.1 It’s measured, as trade association writing tends to be. Read past the careful framing, though, and AI’s impact on grocers’ loyalty programs is rough.

Her post opens with a simple observation most operators already know: shoppers tell them when they want an exact item, when a substitute is fine, and when they’d rather skip the purchase. Her argument is that as grocers’ AI agents take over list building and purchasing, those one-time choices can become standing instructions. Always buy this coffee. Do not replace this yogurt.

The loyalty programs grocers spent years building are their agents’ starting point. The question is whether that’s enough to keep the basket from moving elsewhere.

Please note: This isn’t an argument for building a better app or racing to deploy an AI agent. It’s an argument for understanding what these agents will reveal about existing loyalty investment before they act on it.

The Habit You’ve Been Calling Loyalty

I’ve made this argument before2, but Shawgo gives it new teeth. Only about 1% of grocery shoppers are genuinely loyal to a single banner.3 The rest are loyal to convenience. The rest are loyal to convenience. The store on the way home. The app already on the phone.

Grocers built loyalty programs on that friction and called it retention. It wasn’t. Shawgo is polite about it: “A familiar app or convenient location may drive repeat trips today.” What she doesn’t say is that the grocer’s own agent will reveal how much of that repeat behavior was friction, not preference.

Frequency tells you what happened. It doesn’t tell you what the shopper will protect once decisions get delegated to AI.

The Rise of the Protected Preference

Shawgo introduces a concept she calls “protected preferences.” When a shopper hands their basket to an AI agent, some items are excluded from the optimization. The shopper names them explicitly, removing them entirely from the agent's discretion.

Protected preference isn’t just a product decision. It’s a routing decision.

Shawgo lays out the structural logic. A national brand can anchor a product choice within the grocer’s system. Without a protected preference, the grocer’s agent may substitute or optimize around it. A private label product does the opposite. Protecting the item also protects the retailer, because you can only get it in one place. A small share of the basket becomes the reason the entire basket starts at a specific store.

When nothing is protected, the agent optimizes for price, delivery speed, nutrition, or whatever parameter the shopper sets. Grocers aren’t a destination in that world. They’re a default, one algorithm update away from being optimized out.

The Tension Between Suppliers and Retailers

Suppliers and retailers want different instructions embedded in the shopper's agent, and Shawgo is the first trade voice I've seen say it plainly.

A supplier wants the shopper to say: buy this exact product wherever you can fulfill it best. That protects the brand while making the retailer interchangeable.

A retailer wants the shopper to say: start with this store and choose the best products available there. That protects the banner while leaving product selection open.

The strongest outcome protects both. When neither is protected, the grocer's agent fills the gap with its own logic: substitution rules, private label defaults, and algorithmic ranking within the assortment. That's not a loyalty relationship. It's a transaction, and one algorithm update away from losing the trip.

What Your Promotions Are Actually Doing

Shawgo raises a question that should make every trade marketing team uncomfortable: is a promotion building a protected preference, or is it just setting the conditions for replacement?

Did the shopper buy again at full price? Did the product become a named preference? Did the promotion strengthen the retailer relationship, or did the purchase move the moment another deal appeared?

Immediate return on ad spend doesn’t answer any of that. A shopper can instruct the grocer’s agent to substitute if a preferred product exceeds a certain price. The promotion didn’t build preference. It built a condition for replacement.

Promotional mechanics can get encoded directly into the agent’s standing rules. Run enough promotions, and you haven’t built loyalty. You’ve trained the agent to wait for you.

The Product Metric No One Is Tracking

The KPI that actually matters in an agent-mediated market isn’t DAU. It isn’t session length, coupon redemption rate, or monthly active users. Those metrics describe a human interacting with a UI that the agent is increasingly bypassing.

The metric is: what share of your customer’s basket would they protect from agent optimization?

Call it Protected Basket Rate. It belongs on the product roadmap, not in a campaign brief.

Start with a proxy. Pull the top 10% of customers by spend and identify items that have had no substitution history over the past 18 months. No swaps accepted, no alternatives taken, no refund requested. Those are the protected anchors. That’s the baseline Protected Basket Rate. Everything else is at risk.

It’s an approximation. Zero substitution history reflects revealed behavior, not stated preference. But until grocers have agent-level instruction data, it’s the closest signal available.

From there, ask which items those customers would explicitly name in a standing instruction to an AI. Which store visits are habitual, and which are chosen? The gap between those two numbers is the actual vulnerability.

What to Do About It

Shawgo's call to action: understand which products, attributes, and retail experiences shoppers are most likely to protect, then strengthen the value behind those choices.

Here’s what that means in practice.

Stop optimizing the app for engagement. The grocer’s own agent is now making many of those decisions before the shopper ever opens it.

Find the fifteen things a customer would fight their agent to protect. Start with the prepared food section that makes Thursday night work. Look at the private label line where the product and the banner are inseparable. Find the produce department nobody in the market matches.

Those are the protected anchors. Everything else is a commodity. Commodity gets optimized.

Build the thing people hardcode. That’s the job now.