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The post Why OpenAI, Amazon and Apple Want to Be the Smartphone in Your Pocket appeared first on PYMNTS.com.
\n", "content_text": "On January 9, 2007, Steve Jobs stood on stage at Macworld and introduced three products. An iPod with a bigger screen and touch controls, a mobile phone, and something he described as a breakthrough internet communications device. He repeated the list three times so that all three product announcements could sink in.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThen he revealed the punchline. The three products were all the same device. Apple, Jobs said that day, was reinventing the phone.\nNearly two decades later nobody uses the iPod, although there are probably still some stuffed in the corner of a bedroom drawer. And that \u201cinternet communicator,\u201d the thing the audience didn\u2019t quite understand when Jobs first described it, was the \u201cone more thing\u201d that made history and built a $4 trillion Apple franchise.\nThe iPhone became the operating system that supports the App Store, the Apple Pay mobile wallet, streaming video and music, and a trillion-dollar services economy that connects people with the internet. The Apple apps tail wagged the iPhone dog so hard that the dog forgot it was ever a dog. Well, except four times a year when earnings day rolls around.\nThat same phenomenon is happening again. Right this very minute. Except this time, the tail is AI and agents. And the dog is still the smartphone. But which one?\nThe smartphone, first the iPhone and later Android handsets, were envisioned as the device that runs AI apps. Instead, AI is becoming the reason for the device. The companies that understand this are building phones, forging $50 billion alliances to explore that, and assembling the commerce ecosystems that will determine who captures the economic value of the most important technology shift since the smartphone itself.\nAI and the agentic economy.\nRead more: Why 30 Million US Consumers No Longer Search\nThat makes the question less about which model is smartest and more about who controls the device and the operating system in the consumer\u2019s hand. Because that\u2019s the device that determines the AI defaults, the distribution, and the revenue.\nIt\u2019s the tail wagging the dog story once again.\nThe Data Story: What People Actually Do\nEvery month, PYMNTS Intelligence publishes our Consumer AI Adoption Benchmark. The latest was fielded in late March 2026 across a national sample of 2,111 U.S. adults and tracks 54 distinct personal AI tasks across nine categories. So far, we\u2019ve captured the trendlines for nearly 20,000 consumers that statistically reflect the composition of the U.S. adult population. It is, as far as I know, the most granular longitudinal dataset on how real people \u2014 not the developers, not the academics, not marketing agencies and tech journalists \u2014 are using AI in their daily lives.\nAnd because we track it monthly, we see trendlines in real time. Not in quarterly snapshots that smooth out the story, or random single data points, but in the month-over-month shifts that reveal what\u2019s actually driving behavior.\nAnd the picture it paints is unambiguous.\nThe top tasks the consumers ask frontier models to perform are not exotic or even one-offs. Editing personal writing. Drafting texts and emails. Finding product links. Looking up symptoms. Planning meals. Learning new skills. These are activities they increasingly do on their mobile phones. They happen on the couch, in line at the grocery store, on the commute home. They happen throughout the day, in between everything else, and almost always on their smartphone.\nThe long-term trendlines are even more fascinating.\nThe fastest-growing AI tasks over the past six months aren\u2019t the flashy research and content creation functions that powered the initial adoption wave. They\u2019re household logistics. Finding discount codes grew three percentage points. So did managing household logistics and meal and grocery planning. Reminders for bills and appointments grew a little more than two percentage points.\nAI is becoming a consumer utility. And consumer utilities live on smartphones.\nRead More: Gen AI: The Technology That Broke the Adoption Curve\nWhat also makes our monthly tracking so valuable is that we can see how the world outside of AI usage shows up inside people\u2019s AI usage.\nIn March, we saw travel planning tasks had pulled back. So did hotel and restaurant searches. And that tracks precisely with what we\u2019re seeing in the broader economy. Rising travel costs, shrinking discretionary budgets and consumers pulling back on the trips they planned six months ago. Resume and cover letter writing is climbing, which maps directly onto a labor market shaped by layoffs and graduation season. Our models are becoming more predictive than simply informative.\nThat means AI usage doesn\u2019t exist in a vacuum. It\u2019s a mirror of the economy. The smartphone is the looking glass through which those insights can be found.\n\n\nThe Distribution Illusion\nHere\u2019s where the story gets a little more interesting. And where the smartphone-as-gateway to AI ecosystem finds a few important competitive implications.\nThe PYMNTS Intelligence data shows that Android users are 24 percentage points more likely to use Google Gemini than iOS users \u2014 73% versus 49%. That\u2019s not a preference signal. It\u2019s an embedded distribution advantage. Gemini ships as the default AI on every Android phone, pre-signed into the same Google account that holds the user\u2019s\u00a0 Gmail, Maps, Calendar, Wallet. Like Apple Pay on iPhones, it doesn\u2019t need to be chosen. It just shows up there.\nChatGPT, meanwhile, is the de facto AI preference on iOS, where it faces no native OS-level competitor. iOS users prefer ChatGPT by 10 percentage points over Android users: 88% versus 78%.\nWhat this suggests is that the phone you carry strongly influences the AI you mostly use.\nRead More: The First Chatbot Consumers Try May Be the One They Stick With\nAnd then there\u2019s Copilot.\nMicrosoft\u2019s workplace AI story is perhaps the most obvious example of what distribution without demand looks like. As of early 2026, Copilot has somewhere between 15 and 20 million paid enterprise seats. But the workplace conversion rate, the share of provisioned users who actively choose to use it, according to our data, is only a little more than a third (36%). ChatGPT\u2019s is nearly three times that at 83%.\n\nAnd according to the Recon Analytics dataset, when employees have access to both Copilot and ChatGPT, only 18% choose Copilot. When all three major platforms are available, Copilot\u2019s share drops to 8%. But when Copilot is the only tool the employer provides? Adoption reaches 68%.\nTranslation: Two-thirds of Copilot\u2019s workplace usage exists because workers have no other option. Is that really adoption? Or lock-in captivity?\nWhat these captive workers do when they want the AI they actually prefer is to pick up their phone and open ChatGPT.\nThis is consistent with what I\u2019ve written about before. Consumers use more than one AI model, but they have a favorite. The average active AI consumer engages with more than two platforms; power users engage with nearly four.\nRead More: The Battle for AI Isn\u2019t About Models. It\u2019s About Habits\nBut engaging with multiple platforms is not the same as dividing time equally among them. The pattern mirrors how most people shop. One go-to that handles the everyday, and a short list of specialists that earn their slots by doing something the go-to doesn\u2019t do as well. ChatGPT is that anchor for most consumers today. The phone they carry determines how easily they reach it. And how hard the alternatives have to work to earn a slot in the rotation.\nThe Engagement Paradox\nThere\u2019s another layer of the PYMNTS Intelligence consumer AI adoption data that makes the smartphone-as-AI platform argument even more complicated. And that\u2019s how AI-fluency varies across operating systems.\niPhone users are 12 percentage points more likely to be active AI users \u2014 to use our vocabulary, power users or mainstream users \u2014 than Android users. Fifty-two percent versus 40%. On the desktop side, 60% of Mac users are active AI users versus 47% of Windows users. Macs score 21% power users versus 12% on Windows.\nWhat separates these segments isn\u2019t just how often consumers open an AI app. It\u2019s the depth and breadth of that usage. How many of the 54 tasks they\u2019ve moved to AI and across how many categories of their daily lives.\nRead More: The Data Behind AI\u2019s Shift to Everyday Consumer Use\nLight users average roughly two activities per month. Mainstream users average eight. Power users perform more than 25 distinct AI-driven tasks a month spanning everything from shopping to health to financial management. That intensity is how we measure how embedded AI has become. And it\u2019s where the smartphone-as-AI-platform thesis gets its economic teeth.\niPhone users are the most AI-engaged consumer base, the users most ready for always-on AI that users can query, or for agents that deliver suggestions and outcomes on their behalf. (We call this anticipatory AI.)\nBut Apple’s own AI (Siri and Apple Intelligence) is the weakest of the bunch. Google has the strongest AI integration, Gemini baked into every layer of Android, but the largest pocket of non-users to convert. Forty-nine percent of Android users are AI non-users, compared to 37% of iOS users.\nGoogle has the integration. Apple has the audience.\n\n\nAnd the smartphone is where those two things intersect.\nWhat the Consumer Actually Wants\nWe asked AI users in March for the first time to tell us whether they want AI that comes to them (embedded and proactive) or AI that responds to their initiated prompts (agents that are reactive at the prompt).\nThe answer is a near-even split.\nForty-one percent of users prefer to activate AI themselves, the \u201cAI they go to\u201d model. A little more than a third, 36%, want AI that anticipates their needs and surfaces information and actions automatically, the \u201cAI that comes to them.\u201d Twenty-three percent are on the fence.\nBut peel back the averages and we find a richer story.\nAmong power users, 55% want AI that comes to them. Among light users, just 18% do. Among Gen Z, 48% want AI that comes to them. Among boomers, just 21% do. Among consumers who have already fully replaced their old search and discovery methods with AI, 49% want the proactive model. Among those who only lightly complement their old methods, 25% do.\nHere\u2019s where the plot thickens. iPhone users are 7 percentage points more likely than Android users to want AI that comes to them. That\u2019s despite Apple currently lacking a native proactive AI surface that matches Gemini\u2019s reach.\nThe demand for embedded, always-on AI is sitting inside the Apple ecosystem, waiting for someone to serve it.\nThat someone doesn\u2019t have to be Apple.\nThe Smartphone Wars Are Coming, Part Two\nOn April 27, analyst Ming-Chi Kuo reported that OpenAI is developing a smartphone in collaboration with Qualcomm, MediaTek, and Luxshare. Mass production is targeted for 2028. The device is said to reportedly replace apps on the home screen with an AI-agent interface. The kind where ChatGPT agents handle tasks directly rather than routing users through individual apps.\nThe same day, Sam Altman posted on X that it \u201cfeels like a good time to seriously rethink how operating systems and user interfaces are designed.\u201d\nNone of this should come as a surprise to regular readers of my columns.\nTwo years ago, I wrote that a new dream team was reimagining the future of smartphones with GenAI, and it wasn\u2019t at Apple. Sam Altman and Jony Ive, Apple\u2019s original design visionary, were raising a billion dollars to create an AI-powered device. I asked then whether this new device could become as transformative as the iPhone was in 2007. I also noted, with some irony, that one of the rumored investors was Laurene Powell Jobs, Steve Jobs\u2019 wife. The dream team has since become a dream company. OpenAI acquired Ive\u2019s firm for $6.5 billion. Since then, the ambition has shifted from a device to a phone.\nA month before the Kuo report, Reuters reported that Amazon is developing a smartphone within its Devices and Services division. That effort is led by a group called ZeroOne under the leadership of a former Xbox executive. It\u2019s said that the device would integrate with Amazon\u2019s shopping, Prime Video, Prime Music, and Alexa+ ecosystems.\nThe conventional analysis of these moves focuses on whether OpenAI or Amazon can compete with the iPhone. History would say, whoa, not so fast. The Fire Phone. Windows Phone. Facebook phone. The graveyard of failed smartphone challengers is deep and well-populated with some of the biggest names in tech.\nThen again, maybe that was then and this could be what\u2019s next.\nThe question isn\u2019t whether OpenAI can outsell Apple. It\u2019s what happens if the company with 900 million weekly active users and 50 million paying subscribers decides that it no longer wants to access those users through someone else\u2019s hardware.\nChatGPT was the most-downloaded app of 2025, with 770 million installs. Its mobile app alone generated $1.35 billion in revenue for Apple that year. That would make OpenAI the largest tenant in Apple\u2019s App Store by any AI measure. And Apple takes a cut of every subscription sold through it. Every ChatGPT query flowing through an iPhone is an interaction that OpenAI controls and Apple monetizes.\nThat\u2019s a business relationship that works until it doesn\u2019t. And OpenAI is clearly signaling that it wants to own the full stack.\nThe Google Problem\nIf OpenAI builds a phone, it almost certainly runs Android. Building a proprietary OS from scratch is the path that killed every previous challenger. You need the app ecosystem, the carrier relationships, the developer tools. Android provides all of that.\nAmazon learned this lesson the hard way with the Fire Phone. It ran Fire OS, a fork of Android that lacked access to the Google Play Store and popular apps. It was a technical achievement and a commercial disaster.\nThe OpenAI phone would need to somehow maintain Android app compatibility while replacing the AI layer with ChatGPT. That\u2019s possible in theory but challenging in practice. Google controls the default search engine, the default assistant, the default maps, and the default mail client on every Android phone. Displacing all of that requires a value proposition that goes far beyond \u201cour chatbot is smarter and our phone is cooler looking.\u201d\nAmazon\u2019s Different Bet. And the Agentic Commerce Land Grab\nAmazon\u2019s phone thesis is different from OpenAI\u2019s and grounded in the behaviors that now drive nearly 60% of all online sales in the U.S.\nAmazon is betting that the commerce ecosystem is the product and the phone is the lock-in mechanism to access it. And deliver the personalized and proactive AI experiences that consumers say they want more of.\nAmazon doesn\u2019t need consumers to think its AI is the smartest. It just needs consumers to buy more things from Amazon, watch more Prime Video, use Alexa+ to manage their households and Rufus to chase down what they want to buy. Their phone becomes a dedicated remote control for the Amazon economy. An economy that has expanded beyond retail and groceries to healthcare, cars, food delivery, pharmacy and insurance, both in and outside of its own ecosystem.\nRead More: Amazon Says Rufus Gives It an Edge in Agentic Commerce Race\nThat\u2019s a narrower pitch, and maybe even a more practical one. It\u2019s also the one that faces the highest bar to clear. People don\u2019t need a dedicated phone to be part of the Amazon economy today. For Amazon\u2019s phone to succeed, Amazon has to do in 2026 what Steve Jobs did with the iPhone in 2007.\nConvince consumers to try something new that they never asked for and didn\u2019t think they needed. And to be blown away when they try it.\nBut there is one thing that makes Amazon\u2019s position a little stronger than \u201cthere they go again.\u201d Amazon wouldn\u2019t just be building a phone. It would be building the connective tissue to the agentic commerce ecosystem that everyone else is trying to replicate, the ecosystem Amazon already has.\nIn March, Amazon expanded its Shop Direct program to 100 million products from more than 400,000 merchants. Most of those merchants don\u2019t sell on Amazon\u2019s traditional marketplace. Using AI agents, Amazon can now complete purchases on third-party websites on behalf of consumers through its Buy for Me feature, using the shopper\u2019s saved Amazon address and payment credentials. Amazon is going outside its own walls, reaching into the open web, and pulling commerce back through its ecosystem. It isn\u2019t waiting for merchants to come to the marketplace. It\u2019s sending agents to the merchants and delivering sales. And the logistics that close the loop from sale to delivered product.\nAnd then, in February, Amazon announced a $50 billion investment in OpenAI. The company with the most popular AI answer engine, ChatGPT and its 900 million weekly users, is now joined at the hip with the company that owns the most popular product search engine and the most sophisticated fulfillment infrastructure on the planet.\nOpenAI\u2019s models will power Amazon\u2019s customer-facing applications. Amazon\u2019s commerce fundamentals, which include real-time inventory, delivery logistics, payment credentials, and purchase history, will power OpenAI\u2019s agents.\nThis is the dimension of the AI competition that most of the \u201cwho has the best model\u201d coverage whiffs. The real land grab isn\u2019t the AI model. It\u2019s the ecosystem. It\u2019s who builds the platform where AI agents don\u2019t just answer questions but actually do things. That shop, book healthcare appointments, manage groceries, schedule entertainment, handle insurance claims, and reorder prescriptions. The consumer\u2019s entire agentic experience, mediated by AI and agents, flowing through a single set of commerce rails.\nRead More: Why AI Shopping Is Still Just a Smarter Search Bar\nGoogle has search and ads but not fulfillment or payments at scale. Apple has the device but not the commerce infrastructure. OpenAI has the users and the model but not the transaction layer. Amazon has the whole stack. And now it has OpenAI too.\nApple\u2019s Accidental Genius\nAnd then there\u2019s Apple.\nApple\u2019s fiscal Q2 2026 earnings, reported on April 30, tell an AI story that is hiding in plain sight. Services revenue reached a record $31 billion in the quarter, up 16% year over year. iPhone revenue hit $57 billion, a March quarter record. Total revenue was $111.2 billion. Gross margins were 49.3%.\nServices revenue now exceeds Mac, iPad, and Wearables revenue combined. It\u2019s the monetization layer that sits on top of the hardware installed base. And AI is becoming a meaningful, and growing, component of that layer.\nBy being smart in a very clever-like-a-fox kind of way.\nApple doesn\u2019t have to be the smartest AI to capture the economics of AI. By plan or pure luck, you decide, they just need to be the device through which someone else\u2019s smart AI is accessed.\nEvery ChatGPT subscription purchased through the App Store. Every Gemini query routed through iOS. Every AI-powered app that lives on the iPhone. Apple takes a percentage of each transaction without having to build a model, train a model, host a model, or explain why a model hallucinated.\nApple\u2019s the AI toll bridge, not the agentic highway.\nThe PYMNTS Intelligence data shows why this position is sticky.\nAt least right now, iOS users are the most AI-engaged demographic and the most likely to want anticipatory AI. That’s the kind of experience where an agent knows the consumer well enough to surface three dresses and a matching hat for the wedding in London in July, consistent with their preferences, favorite color, budget and the event, before they\u2019ve started looking.\nBut the agentic trust picture is more complicated than Apple might like to admit. AI-native brands like OpenAI and Anthropic are the most trusted providers of AI assistance over handset manufacturers. The phone manufacturer comes in slightly behind that for both iOS and Android.\nThat suggests that Apple’s hardware trust gives it permission to be the intermediary, but consumers trust the AI companies themselves more than they trust the device maker. At least, that’s what consumers say \u00a0now.\nIt\u2019s true that Apple’s installed base is more active with AI and more open to the idea of a trusted device intermediary than Android’s. And that’s a real advantage. But it’s a tenuous one. One built on the absence of a better alternative, not on deep conviction that Apple is the right company to deliver an intuitive, smart AI experience itself.\nTenuous positions have a way of unraveling from multiple directions at once. Apple’s is unraveling from three: the courts, the regulators, and the AI companies whose subscription revenue it clips.\n\nThe Tollbooth Problem\nApple’s App Store commission on AI subscriptions is, functionally, interchange. A percentage of every transaction that flows through the apps ecosystem Apple controls, charged to merchants who have no alternative if they want to reach the consumers on the other side.\nThirty percent the first year, 15% thereafter. For a company that didn’t build the AI, didn’t train the AI, and struggles to ship a version of Siri that can hold a conversation, it’s a pretty good deal.\nThe numbers are getting large enough to matter. Apple’s cut of just ChatGPT’s subscription revenue represents a revenue stream that most AI startups would envy. And Apple earns it by doing nothing more than owning the handset and operating the App Store.\nI flagged this dynamic back in October 2024, when Apple Pay turned ten. Then I wrote about the fault lines forming beneath Apple’s iPhone-centric business model. The combination of regulatory pressure and GenAI, I argued, had the potential to become the biggest disruption to the smartphone ecosystem since Apple launched the iPhone in 2007. Those fault lines appear wider now.\nRead More: Apple Pay\u2019s 10-Year Journey and Its Next Decade of Decisions\nThe problem is that interchange has a history. And that history is one of regulatory pushback.\nAnyone who has watched the payments industry over the past fifteen years knows the gory details. Interchange starts as a fair fee for services rendered. Network access, fraud protection, transaction processing. It becomes a significant revenue engine for the network operator. Merchants complain. Regulators investigate. Caps get imposed. The Durbin Amendment. The EU interchange regulation. Australia. India. The pattern repeats across geographies and decades. And it always ends the same way. The party taking the clip gets clipped.\nThat pattern is now playing out with Apple.\nThe Epic Games case has been working through the courts for years, and the trajectory is clear. In April 2025, the District Court found that Apple had willfully violated an earlier injunction by systematically erecting barriers to prevent developers and consumers from actually using external payment options. The Ninth Circuit backed that finding. Apple’s attempt to charge a 27% commission on purchases made outside the App Store, a hardly magnanimous three-point discount for the privilege of not using Apple’s payment system, was rejected. The court ruled that any commission must be limited to costs that are genuinely and reasonably necessary, not a de-facto tax on all commerce that touches the iPhone.\nJust days ago, Apple lost its bid to pause these changes. Developers can now link to external payment options without Apple charging a commission on those purchases.\nAnd Epic is not alone. In the U.K., a Competition Appeal Tribunal ruled against Apple in a \u00a31.5 billion class-action representing 36 million consumers, finding Apple abused its dominant position. In the U.S., the Department of Justice and fifteen state attorneys general have filed suit. The legal pressure is global, it\u2019s accelerating, and it\u2019s aimed squarely at the revenue model that makes Apple’s \u201ctoll bridge\u201d strategy a cash register.\nIf you’re OpenAI, this is the reason the AI phone project might be a hedge rather than a gamble.\nIf the courts force open payments, and the trend line strongly suggests they will, OpenAI can route subscribers directly and keep more of its revenue without building a single handset. The phone becomes less urgent. But if Apple finds ways to maintain its commission, or if system-level AI integration becomes the competitive moat, or, or, or, then owning the hardware is the only way out.\nJust Don’t Ask Siri\nWhich brings us to the question that\u2019s becoming harder for Apple to answer. What happens when the toll bridge operator is also supposed to be the AI provider?\nLast July, I wrote that Apple Intelligence didn’t appear in any serious public GenAI ranking. That it lagged behind even Grok. I called it a strategic misstep. Maybe even a crater-sized hole that could prove Apple’s fatal flaw. I asked whether Apple was at risk of repeating IBM’s trajectory. That of an early innovator that squandered its AI lead and became irrelevant in the revolution it helped start. Siri was chat before there were chatbots. And then Apple watched while everyone else \u2014 Google, Amazon, OpenAI \u2014 built the future around conversational AI.\nRead More: Apple\u2019s $10B AI Crisis. 3 Bold Moves To Reinvent Its Future\nTen months later, not much has changed. Apple Intelligence remains a branding exercise more than a product breakthrough.\nThat strategy works when the phone is the universal remote control for AI. When consumers are happy to use the iPhone as the device that launches ChatGPT, summons Gemini, opens Claude. Apple doesn’t need its own AI to be good. It just needs to be the best hardware through which other people’s AI gets accessed.\nRead More: Apple Says It Will Let Siri Access Multiple AI Assistants\nBut the shift toward agentic AI could change that thesis. Agents don’t just answer questions. They book flights, manage calendars, execute transactions, coordinate across apps. They need system-level permissions that Apple has historically guarded with religious zeal. An AI agent that can’t access contacts, or read notifications, can’t initiate a payment without three permission dialogs isn’t an agent. It’s a friendly chatbot with agentic ambition.\nThe agentic future isn’t theoretical anymore. Sure, we’ve seen our share of initial agentic commerce missteps. Does anyone remember how miserable the first iPhone experience was? But technology improves and the flywheel starts to rev and use and adoption take off.\nRead More: Consumers Stop Sampling AI and Start Relying on It\nThe companies building these agents \u2014 OpenAI, Google, Amazon, Anthropic \u2014 all understand that the agentic future requires either deep OS integration or ownership of the OS itself. And Apple is the company least likely to grant that integration to a third party.\nSo, the question becomes something like this: Is the iPhone the universal remote control for the AI era? Or is it the cable box, the device that once controlled the living room but became irrelevant when the apps it was supposed to deliver started going directly to the TV?\nCable boxes didn’t die because they stopped working. They died because they stopped being necessary. The content providers, first Netflix and then everyone else, realized they could reach consumers without the intermediary, and the intermediary’s toll became unjustifiable. They had a financial incentive to explore those alternatives. The cable company’s response was to make the box “smarter,” to add apps, to integrate streaming services into the guide.\nApple’s bet is that the iPhone is stickier than the cable box ever was. That’s probably right today. People don’t switch phones the way they swapped out set-top boxes. The switching costs are higher, the ecosystem lock-in is deeper, the muscle memory is more entrenched.\nBut the history of platforms teaches us that the intermediary’s power is a function of how much value it adds versus how much value it extracts.\nRight now, Apple adds a lot. The hardware, the security, the ecosystem, the trust. The courts are asking whether the toll, the 30% of every AI subscription, every in-app purchase, every transaction, is proportionate to that value.\nAnd the AI companies building the next generation of agents are asking whether the intermediary is necessary at all.\nThe Real Question\nThe AI industry is obsessed with the model race. Who’s smarter? Who’s faster? Who’s cheaper per token? Those questions matter, but they’re not the ones that will determine who captures the economic value of AI over the next decade.\nRead More: The Battle for AI Isn\u2019t About Models. It\u2019s About Habits\nThere are two questions that might.\nThe first: Who controls the device in the consumer’s hand? Because the device determines the defaults, the defaults determine the distribution, the distribution determines the usage, and the usage determines the revenue.\nThe second: Who builds the ecosystem where AI agents actually transact? It’s not enough to have the best model or even the best phone. You need the commerce ecosystem. The merchant relationships, the payment credentials, the fulfillment infrastructure. You need the whole agentic economy. Not just the chatbot concierge at the front door.\nThe phone, in this context, becomes something more than a screen, a camera and a music player. It becomes the command center for the agentic economy. The device where the consumer\u2019s AI agent lives, where it has their permissions, their payment credentials, their preferences, their context.\nWhoever controls that device controls the default agent. And whoever controls the default agent controls the commerce that flows through it.\nGoogle understands the device question. That’s why Gemini is embedded at the OS level on Android. Microsoft understood a version of it. That’s why Copilot is bundled into every Microsoft 365 license, although the data shows that bundling without quality produces adoption without loyalty. OpenAI is starting to understand both questions. That’s why it’s building a phone and partnering with Amazon and others building AI-native marketplaces like Marc Lore’s Wizard.\nAmazon understands the ecosystem question better than anyone. That’s why it’s investing $50 billion in OpenAI while sending AI agents to buy from merchants outside its own marketplace.\nApple understood the device question first. It called it the iPhone. But toll collectors don’t build economies. They collect rents until someone builds a better highway that avoids them. And the companies racing to build an agentic economy aren’t asking Apple’s permission.\nAt the same time the courts, the competitors, and the consumers are all starting to ask the same question Steve Jobs answered on that stage in 2007 with his \u201cinternet communicator.\u201d\nJust from the other direction.\nWhat if the AI tail wags the smartphone dog so hard that the whole thing has to be reinvented?\nIncluding by Apple itself.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\n \n\r\n\r\nThe post Why OpenAI, Amazon and Apple Want to Be the Smartphone in Your Pocket appeared first on PYMNTS.com.", "date_published": "2026-05-06T04:00:23-04:00", "date_modified": "2026-05-06T11:23:19-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/05/2026-Web-Hero-Image-KLW-NEXT-1200x780-05052.jpg", "tags": [ "agentic commerce", "AI", "Amazon", "Apple", "artificial intelligence", "Karen Webster", "KLW Commentary", "Main Feature", "News", "next newsletter", "OpenAI", "PYMNTS News", "Smartphones", "artificial intelligence" ] }, { "id": "https://www.pymnts.com/?p=3668422", "url": "https://www.pymnts.com/news/2026/65-call-insurance-essential-why-most-spending-isnt-so-clear-cut/", "title": "65% Call Insurance Essential. Why Most Spending Isn\u2019t So Clear-Cut\u00a0", "content_html": "Picture two families living four miles apart in the same mid-sized American city. Both earn about $85,000 a year. Both have two kids in elementary school. On paper, they are demographic twins.
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The post 65% Call Insurance Essential. Why Most Spending Isn\u2019t So Clear-Cut\u00a0 appeared first on PYMNTS.com.
\n", "content_text": "Picture two families living four miles apart in the same mid-sized American city. Both earn about $85,000 a year. Both have two kids in elementary school. On paper, they are demographic twins.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThe first family lives in a neighborhood where the zoned public school has mediocre ratings and a reputation that keeps most of their neighbors up at night. They send their two kids to a private school at roughly $7,800 per child. The mother works a second job on weekends to cover it. Ask her what she would cut if money got tight and she doesn\u2019t even blink. Tuition is the last line she would touch. Private school, for her, is absolutely essential.\nThe second family earns the same income, lives in the same metro area, and has the same number of kids. Three years ago, they moved specifically because the suburb they targeted has one of the top-rated public-school districts in the state. Their kids walk to a public school that sends graduates to top universities every year. Ask this mother whether private school is essential and the question doesn\u2019t even register. Private school, for her, is irrelevant.\nSame line item. Same income. Same city. One household treats it as non-negotiable. The other doesn\u2019t even think about it.\nThe Tomato-Tomahto of the Household Budget\nThis is the heart of what new PYMNTS Intelligence data shows.\nWhat makes something feel non-negotiable is almost never about what it costs in absolute dollars. Essential isn\u2019t a characteristic of the expense. It\u2019s the characteristic of the person spending the money on it.\nA PYMNTS Intelligence survey of more than 3,400 consumers put the same 22 line items in front of everyone and asked them to classify each one as absolutely essential, necessary but a choice or purely discretionary. The results don\u2019t line up by income. They line up by life stage, family structure and the commitments each household has locked in over the years.\nTake private school. It\u2019s rated absolutely essential by 17%, necessary but a choice by 23%, and purely discretionary by 60%. Family financial support splits 26/34/40.\nThen there\u2019s grocery delivery. Half of consumers earning less than $50,000 say it\u2019s essential or necessary, versus 42% of those earning $150,000 or more. For a household earning $45,000, getting groceries delivered might be a logistics requirement rather than a premium convenience if the consumer doesn\u2019t own a car or works irregular hours or multiple jobs and can\u2019t easily get to a store.\nCompare all of that to insurance, rated essential by 65% of consumers with only 11% calling it discretionary.\nNobody really disagrees about insurance. The items where consumers are closer together than apart are universal, structural necessities. The items where consumers split are the ones where essential means whatever the specific household at the specific life stage decides it means.\nRead More: The Three Blind Spots in How Consumer Sentiment Is Measured\n\u00a0A Better Way to Think About Essential\n\nSource: PYMNTS Intelligence framework, New Reality Check: The Paycheck-to-Paycheck Report, January 2026.\nThe traditional way finance professionals sort spending is into fixed, variable and discretionary buckets. The flaw with that classification is that it isn\u2019t always correlated with how consumers manage their money.\nA better frame has two dimensions:\n\nWhy the expense feels essential\nHow locked in it is\n\nStructurally-Locked expenses are forced by circumstances that feel immovable. Childcare for a working parent. A mortgage. A car in a sprawling metro area. Student debt from a decision made a decade ago. PYMNTS Intelligence data finds that more than half (54%) of the full sample rates mortgage/housing absolutely essential, half rate car ownership essential, and 37% of married parents rate childcare essential.\n\nValues-Locked expenses are conscious priorities that have hardened into a financial commitment. Private school tuition. A gym for pain management. Monthly support to an aging parent. The households rating these as essential aren\u2019t wealthier than the ones that don\u2019t. They\u2019re different because of what they\u2019ve decided matters, and is therefore essential.\nCircumstantial Recurring expenses are related to needs shaped by a personal situation rather than universal necessity. They can be changed or eliminated, but not easily and not without other potential financial consequences. Accordingly, 65% of consumers rate insurance as essential and 58% of consumers rate healthcare as essential; these expenses dominate this quadrant and the overall rankings.\nLifestyle Choices are the only expenses that map cleanly to the conventional discretionary bucket. Subscriptions (14% of consumers say they\u2019re essential), travel (13%), entertainment (10%) and expedited shipping (10%) are nice-to-haves but could be dialed back or eliminated, depending.\nWhen expenses are put into this framework, the same items land in different buckets. A gym membership lives in Lifestyle Choices for one household and in Values-Locked for another. Childcare is Structurally-Locked for a 34-year-old working parent and irrelevant to a 68-year-old retiree. Essential becomes a subjective call, not an objective one.\nHow Life Stage Rewrites the Priority List\nThe clearest way to see the many sides of \u201cessential\u201d at work is to walk through the U.S. consumer base segment by segment. Each one tells a different story about what gets pulled into the non-negotiable tier and why.\nLet’s start with millennials, the generation living through the years when earlier commitments show up as non-negotiable monthly line items. Nearly seven in ten, 69%, say they live paycheck to paycheck. Half point to long-term life decisions as the reason, a rate 19 points higher than boomers and the steepest of any generation.\nRead More: 38% of Millennials Pay Out of Pocket for Healthcare\nTheir non-negotiable tier reads like a structural inventory. Insurance, healthcare, the car, the mortgage, outside family support. What sets millennials apart is the tier just below.\nMore than a third (37%) cite childcare as essential. \u00a0Grocery delivery at 31%, because when both parents work and a toddler is in the car seat in the back, the friction associated with a trip to the grocery store gets real. Student loans at 27%. Where they live at 23%. None of those come across as lifestyle preferences. They\u2019re commitments made years ago that can\u2019t easily be unwound. That explains why only 54% of millennials feel in control of their financial situation even as they stay stuck inside it.\nMarried parents are the most obvious case of how Structural Lock-in operates. Sixty-seven percent live paycheck to paycheck. Sixty percent say they could change their situation with effort. Their budget says otherwise.\nMarried parents cite long-term life decisions as the reason for their financial situation at 59%, the highest rate of any household segment in the survey. Their top essentials look like everyone else\u2019s: insurance, healthcare, mortgage. The difference shows up in the next tier.\nChildcare and clothing at 37%. Grocery delivery at 30%. Private school at 29%, nearly double the 17% full-sample rate. Where they live at 25%. Every one of those categories runs at least eight points above the full sample, and none of them are stated preferences. They\u2019re what it takes to run a household with kids. A second car to cover two drop-off routes. A neighborhood chosen for its middle school. Groceries delivered because nobody has time to shop on the way home. That\u2019s why the married-parent budget is the most locked-in profile in this study.\nSingle parents show what happens when the same financial scaffolding has to stand on one income. More than eight in ten (82%) live paycheck to paycheck, the highest rate of any segment profiled. Only three in ten report any flexibility to cut. Clothing jumps to 40% essential, eleven points above the full sample, because a single income is feeding and clothing kids who outgrow their shoes every four months. Grocery delivery hits 34%, twelve points above the full sample. Family financial support, 39%. Private school, 28%. Childcare, 30%.\nThe most telling insight isn\u2019t any single line item. It\u2019s the pattern across the three reasons people give for their financial situation. Day-to-day spending, long-term decisions and unexpected events all rank within five points of each other. For most households, one of those three clearly dominates. For single parents, all three hit at once. There is no single lever to pull when things get tight because the budget is pressured from every direction.\nThe paycheck-to-paycheck struggling household closes the segment story in a way that looks paradoxical at first. Nearly half (48%) report little or no perceived control over their finances. Forty-three percent say spending changes alone can\u2019t fix it. What drives their budget isn\u2019t a pattern of everyday choices but shocks they didn\u2019t plan for. Sixty-one percent point to unexpected events as the reason for their financial circumstances, compared with 31% of non-paycheck-to-paycheck consumers.\nBut here\u2019s the counterintuitive part. Their essential ratings are lower across the board than other segments. Insurance at 55%. Healthcare at 50%. Mortgage at 46%. Car at 44%. This isn\u2019t because they care less. It\u2019s because they\u2019ve already cut everything that could be cut. What remains is non-negotiable, and there\u2019s nothing more left to trim.\nRead More: How 30 Million Workers Borrow from Tomorrow to Pay for Today\nGeneration layers on top of all this. The sharpest divide between age groups isn\u2019t on insurance or housing, where consensus about essentials is broad. It\u2019s on the small daily line items that older generations treat as obviously discretionary.\nGen Z and millennials are up to ten times more likely than boomers to classify coffee, lunch out, food delivery, subscriptions and gym memberships as essential. For a 24-year-old with a two-hour commute, a food delivery subscription is about logistics and convenience. For a 72-year-old retiree with a fully stocked pantry, it\u2019s an obvious waste. Neither is wrong. They\u2019re describing different lives with different priorities.\nRead More: Healthcare on Hold: Why 1 in 4 Gen Z Consumers Skip the Doctor\nParenthood is the single variable that reshapes the priority list more than any other. Married and single parents rate childcare, private school, family support, grocery delivery, food delivery, clothing and meal kits dramatically higher than adults without children. Childcare is obvious. The less obvious ones tell the real story. Meal kits, food delivery, grocery delivery. For parents, these aren\u2019t indulgences. They\u2019re how the household runs.\nRead More: How Time Became the Next Great Asset Class\nGrocery delivery sits at 22% essential in the full sample, 30% for married parents, 34% for single parents. Food delivery shows the same logic from a different angle. Thirty-four percent of households earning under $50,000 rate it essential or necessary, compared with 31% of households earning over $150,000. A worker juggling two hourly jobs treats the $12 delivery fee as the cost of eating the meal that has to happen between shifts. For a higher earner with a predictable schedule, the same line item is convenience.\nThe clearest signal in the entire dataset is clothing for single parents. Forty percent call it essential and another 46% call it necessary, putting 86% at or above necessary. Only 14% call clothing purely discretionary. For most consumers, clothing is lifestyle. For a single parent with growing children, it is regarded more like the utility bill.\nFinancial lifestyle adds a final twist to the picture. Nearly a quarter of paycheck-to-paycheck consumers (23%) earn $100,000 or more. Not because they spend carelessly, but because of commitments already locked in. The mortgage on a house in a good school district. Childcare for two kids. Student loans still running a decade after graduation. A six-figure salary doesn\u2019t unwind any of that.\nRead More: Who Is the Paycheck-to-Paycheck Consumer in America?\nConsumers who aren\u2019t paycheck to paycheck actually rate structural items like insurance, healthcare and mortgage as more essential than consumers who are struggling. You would expect the opposite. The reason shows up in what each group blames. Two-thirds (66%) of non paycheck-to-paycheck consumers point to day-to-day spending as the main driver of their financial situation. They frame it that way when the structural bills feel handled. For these households, their control levers live in the daily choices, not the locked-in commitments.\nStruggling consumers tell a different story. They\u2019re twice as likely as non-paycheck to paycheck consumers to cite unexpected events as the cause of their financial situation (61% versus 31%). Forty percent report low or no flexibility to cut expenses, compared with 18% of non-paycheck-to-paycheck\u00a0 consumers. That 22-point flexibility gap is the single widest in the dataset, and it is structural that becomes behavioral.\nRead More: Meet the 27 Million Americans Who Drive 8% of Consumer Spend but Struggle to Pay Their Bills\nPulling It All Together\nThe picture that emerges across these segments is that essential is a characteristic of the person spending, not of the expense itself.\nWhat actually predicts how a household will behave under financial pressure is the combination of life stage, family structure and financial history. Millennials cite long-term decisions at 50%. Married parents at 59%. Boomers at 31%. That 28-point spread between younger or parenting households and boomers is about which commitments are actively running through the budget. And how willing those households are to protect the priorities behind them.\nSegmenting customers by income decile or FICO band captures none of that. Segmenting by priority profile captures all of it.\nThe Priority Behind the Payment\nEvery payments company, credit issuer and bank has built its data stack around two questions. What did the consumer buy, and how much did they spend? The PYMNTS Intelligence data in this report says those questions answer less than half of what matters.\nWhat a consumer buys is visible in the transaction record. Why they bought it, whether it is a conscious priority or a forced one, and whether they would fight to protect it under financial pressure, isn\u2019t.\nThe next competitive edge in payments and financial services isn\u2019t more behavioral data. It\u2019s priority data.\nConsider two $7,800 annual tuition payments sitting in two different customer profiles. Same category, same frequency, same payment amount. In the transaction record, they\u2019re indistinguishable. In reality, they\u2019re three different customers. For the 17% of households that rate private school absolutely essential, that $7,800 is sacred. Those consumers will go into debt to protect it. For the 23% who call it necessary but a choice, it\u2019s up for review the minute cash flow tightens. For the 60% who call it discretionary, and who happen to be paying the tuition because a grandmom is paying, it\u2019s the first thing to go. Transaction data alone can\u2019t tell them apart.\nRead More: Why the Offers Economy Is Broken\nPriority data gives a view of who the customer is, what they have committed to and what they will trade off to protect those commitments. It predicts the next move in a way the transaction record can\u2019t. The implications play out differently for different parts of the ecosystem.\nFor credit issuers, priority data answers the single most valuable question in the business. If this consumer\u2019s cash flow tightens, which bills get paid and which may not? Forty-three percent of paycheck-to-paycheck consumers who are struggling say spending changes alone cannot fix their situation. They\u2019ll miss a payment on something.\nPriority data tells the issuer which something.\nRead More: Your Business Has Its Payments Data. Now What?\nFor merchants, the lesson is that product category isn\u2019t destiny. The same subscription service splits 14% essential, 32% necessary, and 54% discretionary across the full sample, but 17% essential and 43% necessary among single parents. That\u2019s one product and two completely different retention fights.\nThe Values-Locked customer will swallow a price increase. The Lifestyle Choice customer will cancel the moment a competitor runs a promotion. The same treatment for both leaks revenue at both ends. Dynamic pricing, loyalty programs and churn prevention all need to follow where the category sits in the customer\u2019s priority stack, not the category code.\nFor Buy, Now Pay Later and installment lenders, priority data points to an opportunity much larger than discretionary retail. Thirty-seven percent of married parents call childcare absolutely essential. Twenty-nine percent call private school essential. Forty-one percent call family financial support essential. These are the recurring, high-ticket, Values-Locked line items that households currently put on credit cards, take from savings, or borrow from family to cover. Pay later products built for those priorities capture a segment the retail-focused BNPL players aren\u2019t actively addressing. The underwriting case is stronger, too, because households don\u2019t default on what they\u2019ve decided matters most.\nFor banks and personal financial management tools, the implication is that sorting spending by category is a map drawn against how consumers actually think. A single parent\u2019s clothing spend isn\u2019t lifestyle. A millennial\u2019s food delivery isn\u2019t dining out when it functions as a logistics and convenience tool for a working household. A gym membership isn\u2019t fitness when the user joined for chronic pain.\nMoney management tools that let consumers tag their own priorities, or that infer priorities from which categories survive the next income shock, stop being transactional logs and start being the household’s priority dashboard. That\u2019s a stickier relationship, and one of the few defensible positions left as transaction-data parity among competitors continues to erode.\nRead More: The Next Battle in Credit Won\u2019t Be for Top of Wallet\nAcross all of those use cases, the strategic insight is the same. The successful payments, credit and banking players over the next decade won\u2019t always be the ones with more transaction data. They\u2019ll be the ones who know who their customer is underneath the transaction.\nTwo consumers with identical demographics and identical purchase histories can have radically different priorities, and those priorities decide how they behave under pressure.\nThe question everyone should be asking is whether their data strategy reflects that. Or whether it is still bucketing customers simply by what they bought last month.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\n \n\r\n\r\nThe post 65% Call Insurance Essential. Why Most Spending Isn\u2019t So Clear-Cut\u00a0 appeared first on PYMNTS.com.", "date_published": "2026-04-22T04:00:23-04:00", "date_modified": "2026-04-21T22:56:10-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/2026-Web-Hero-Image-KLW-NEXT.jpg", "tags": [ "Consumer Insights", "Consumer Spending", "economy", "Karen Webster", "KLW Commentary", "Main Feature", "News", "next newsletter", "PYMNTS Intelligence", "PYMNTS News" ] }, { "id": "https://www.pymnts.com/?p=3649918", "url": "https://www.pymnts.com/news/retail/2026/why-the-offers-economy-is-broken/", "title": "Why the Offers Economy Is Broken", "content_html": "Picture a woman standing in the checkout line at her local grocery store, frantically thumbing through her phone looking for a promo code she was sure she had saved somewhere. Maybe it was one of the times you were standing behind her.\u00a0 The cashier waited. The line waited, though not very patiently. She eventually gave up, paid full price and walked away annoyed. But not at herself. At the store.
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\r\n\r\n\r\nThe post Why the Offers Economy Is Broken appeared first on PYMNTS.com.
\n", "content_text": "Picture a woman standing in the checkout line at her local grocery store, frantically thumbing through her phone looking for a promo code she was sure she had saved somewhere. Maybe it was one of the times you were standing behind her.\u00a0 The cashier waited. The line waited, though not very patiently. She eventually gave up, paid full price and walked away annoyed. But not at herself. At the store.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nThat scene plays out millions of times a day across grocery stores, retail shops, restaurant apps and eCommerce checkout flows. A deal is out there. A consumer wants it. But the engine to connect them sputters and fails. The store collects the data point on an unredeemed offer and moves on. The brand absorbs the wasted promotional spend and chalks it up to the cost of doing business. The consumer, tired by all the jitter-jive associated with the three or four steps required to find and then redeem a promo code or an offer, eventually stops trying.\n\u201cSo what,\u201d some merchants and brands might say. The customer still shopped at the store, still bought the product and, bonus, paid full price. What\u2019s so wrong with that picture?\nHere\u2019s what\u2019s wrong.\u00a0 Every one of those unredeemed offers isn\u2019t just a missed connection. It\u2019s a missed connection with a customer who may shop somewhere else next time.\nThe consumer who never saw the offer and paid full price probably wondered whether they missed out on a deal somewhere else for the same thing. The brand spent the promotional dollar and got nothing back. The merchant lost the chance to win a basket that might have looked different, even bigger, with a customer who might be more loyal had the right incentive arrived at the right moment.\nNew data from PYMNTS Intelligence finds that 50% of restaurant diners and nearly half of retail shoppers noticed no offer during their most recent visit. The offer was there but they couldn\u2019t find it, didn\u2019t see it. That\u2019s not a rounding error in the redemption data. These are the customers brands most want to reach and are most consistently failing to convert.\n\nA new PYMNTS Intelligence report produced in collaboration with FIS surveyed 2,754 consumers across grocery, restaurant and retail, putting hard numbers to a problem the industry has been tolerating for years. The findings aren\u2019t a gentle critique. They describe an offers economy that\u2019s out of step with the consumers it was built to serve, extracting data and attention at the top of the funnel while failing to deliver value where it matters most. At the bottom of the funnel, when it\u2019s time to close the deal.\nWhat the data reveals is that the breakdown in the offers economy isn\u2019t about coupon mechanics or loyalty app design. It\u2019s about whether the promotional dollars that brands, merchants and issuers collectively spend are achieving the one thing offers are meant to do. Change behavior.\nPYMNTS Intelligence data shows they can, and sometimes they do. But the $42 billion gap in the offers economy points to a structural deficiency that has grown too large to ignore.\nThe right framework for understanding this structural breakdown is FIT: Friction, Inertia and Time. I\u2019ve been writing about these forces for years, drawing on data from dozens of platform inefficiencies to develop it. What I\u2019ve found is that each force operates independently. Together, they\u2019ve created an offers economy that consumes enormous promotional investment while delivering outcomes that fall far short of what new technology, new models and new ways of thinking about offers now make possible.\nRead More: Using the FIT Framework in a\u00a0Digital 3.0 World\nThe Wanamaker Problem, Still Unsolved\nThe early department store pioneer John Wanamaker famously said that half of his advertising money was wasted, but he didn\u2019t know which half. That was in the early part of the 1900s, when paper and pen were the most innovative tools available to track those outcomes.\nOne hundred and twenty-six years later, that dilemma defines the failure of the 2026 offers economy, even as offers have gotten richer and more plentiful and the technology available to serve them is now extraordinarily sophisticated.\nAmong the consumers who did find an offer, only 13% online and 10% in-store experienced the automatic application of the discount at checkout. The other 87% to 90% had to put a lot of elbow grease into redeeming them, clearing an average of more than two active hurdles just to use a deal they were already aware of, the PYMNTS Intelligence data found.\nAnd when consumers who saw an offer but said \u201cno thanks\u201d were asked why, 40% said the offer wasn\u2019t relevant. Not the obstacle course between discovery and checkout. Irrelevance. The offer had already captured their attention and in many cases their personal data. But returned a deal they didn\u2019t want.\nRead More: Personalized Offers Are Powerful \u2014 But Too Often Off-Base\nThis disconnect is commercially more serious than a leaky funnel.\nThe $42 billion sitting uncaptured in the gap between promotional dollars spent and consumer value delivered is the economic impact of a system that isn\u2019t living up to its potential.\nSeven in ten consumers who noticed an offer changed what they bought or how much they bought when the offer actually reached them.\nSo, every dollar disappearing into the gap between an offer that existed and a consumer who never found it is more than wasted spend. It\u2019s a lost opportunity to change behavior, win a brand switch, build a basket or earn a payment method preference.\nThe outcomes the offers economy is supposed to produce.\nMaking the Offers Economy More FIT\nMy two cents is that the friction in the current offers system isn\u2019t accidental. It\u2019s a deliberate design choice.\nThink about your own experience in finding and redeeming offers. The offers ecosystem was built to use the lure of a discount as a mechanism for data capture at the top of the funnel. Sign up for the email. Create the account. Opt into the text notifications. The consumer is asked to \u201cpay\u201d in personal information and future attention before they have any basis for trusting that what comes back will be relevant or worth the exchange.\nWhen 40% of non-redemptions happen because consumers find them irrelevant, it means the data capture is often happening without the personalization payoff that was supposed to justify it. The brand got the email address. The consumer got nothing they wanted.\nThe \u201cI love it/I hate it\u201d ubiquitous promo code is the most visible symptom of this dysfunction.\nA brand distributes codes across email campaigns, coupon aggregator sites and promotional partnerships, loses control of who redeems them and under what conditions, captures no meaningful attribution data from the transaction and builds no relationship with the consumer who found the deal on a third-party site and who will return to that site next time rather than to the brand or merchant. The code is not a marketing tool. It is a markdown without meaningful attribution.\nThat dysfunction persists not because merchants and brands can\u2019t see it, but because inertia has made it too comfortable to ignore. And, ironically, so durable.\nConsumers have adapted to the broken system rather than rejecting it. They use secondary email addresses for promotional signups. They abandon carts and wait for the recovery email that almost always arrives with a better offer. They have learned the game and play it, which creates a false signal for merchants and brands who look at redemption rates and email list growth and conclude that the system is working.\nWhat the metrics don\u2019t capture is the 27% of shoppers who pay no attention to offers at all because the current offers ecosystem hasn\u2019t given them a reason to engage. Those consumers aren\u2019t lost. They\u2019re waiting for an offers experience worth their attention and their loyalty. The longer inertia holds the current system in place, the more the third force, time, works against everyone.\n\nRead More: Embedded Offers: The Billion-Dollar Opportunity Inside Recent Consumer Spending\nThe dominant offer discovery channels \u2014 the merchant apps, checkout screens and in-store signage \u2014 all require the consumer to already be inside the purchase flow. The offer arrives after they\u2019ve already finished their shopping. It creates no opportunity to influence what goes in the basket, consider an alternative product or increase spend.\nThe generational data makes the time pressure across merchants and brands more real. Gen Z is the most likely to shut the door entirely on offers, and therefore brands and stores, when presented with manual steps and promo code hunts.\nRead More: The Five Rules of Engagement for Gen Z Spending and Payments\nNearly one in five Gen Z (19%) shoppers who saw an offer and didn\u2019t use it cited too many steps as the reason, compared to one percent of boomers. For this generation, friction is not an inconvenience. It\u2019s a reason to shop with another merchant or stick with the brand they already know rather than try something new.\n\nWhat a Different System Changes\nThe\u00a0 case for a better offers architecture starts with understanding, and then embedding offers, into the customer journey at the start, and not at the end.\nNearly nine in ten consumers say they want to see every relevant discount before they decide what goes in their cart. Before they decide they\u2019re finished shopping and want to call it a day.\n\nAn embedded smart offer delivers it at the moment of intent. That shift does more than improve the user experience. It changes the purchase decision itself.\nAn offer that finds the consumer rather than waiting to be found doesn\u2019t require effort, trust in a coupon aggregator or memory of a promo code under pressure at checkout. It arrives attached to the product in their consideration set, delivered through the card they already use, built on their actual purchase history rather than a demographic bucket.\nThe data finds that when offers do reach consumers, seven in ten change what they buy. Seven in ten change how much they buy. The goal is to make finding offers the default rather than the exception.\n\nRead More: The $42 Billion Checkout Opportunity Hiding in Plain Sight\nOne-to-one personalization is what separates this model from every prior iteration of card-linked offers. The card credential carries transaction history across merchants and categories at a level of granularity that no email list or loyalty program can approach.\nIt knows not just that a consumer shops at a particular grocery chain but which brands they buy consistently, which they have tried once and abandoned, which categories they trade up in and which they treat as commodities.\nA dynamic offer built on that data is much more than a discount. It is a precisely timed intervention in a known purchase pattern, served to defend a brand relationship showing signs of fatigue, to introduce a product at the moment the consumer is most likely to try something new, or to reward the specific behavior the brand wants to reinforce.\nRead More: AI Pushes Personalization From Guesswork to Growth\nAccording to PYMNTS Intelligence data, four in ten consumers say a form of smart, embedded real-time savings would be highly influential in making a payment method that supports it their default. Seventy-seven percent say it would be at least somewhat influential. The card that delivers embedded smart offers seems influential in winning in the wallet.\n\nNew Economics for Merchants and Brands\nThe attribution crisis at the center of the offers economy is as consequential as the consumer experience problem but gets far less attention.\nBrands spread promotional budgets across email, coupons and loyalty programs and get back data that is too weak and too delayed to guide the next decision. Money goes out and redemption rates come back, but whether an offer drove new behavior or simply discounted a purchase that would have happened anyway is largely unknowable.\nIt is the Wanamaker problem, 126 years later, still unsolved.\nUsing the card credential as the delivery layer could address this at its root. The card is present at every transaction, which means a brand funding an offer at this layer knows exactly what was purchased, where, by whom and whether the behavior was incremental, such as a trial, a basket expansion or a brand switch.\nThe promotional dollar no longer buys impressions. It buys outcomes. Return on investment is not inferred. It is measured, attributed and immediately actionable.\nFor merchants, this changes the economics in a meaningful way.\nBrand-funded offers become a source of incremental revenue rather than a cost center. For merchants, this reframes offers from an expense to manage into an asset that drives acquisition and retention.\nThe result is a reallocation of promotional spend away from impression-based marketing with uncertain outcomes and toward incentives tied to specific products, merchants and behaviors. A fundamental shift in how the offers economy works.\nThe Card\u2019s Untapped Commercial Position\nFor issuers, this creates something new. It is a commercially meaningful position at the center of the offers economy that no ad network, email platform or coupon aggregator can replicate.\nIt is grounded in trust, verified transaction data and full visibility across the purchase journey. The question is whether issuers recognize the opportunity and move with the urgency it demands.\nPYMNTS Intelligence data suggests the consumer is already there. Among those most willing to engage with embedded and personalized offers, 75% are willing to share data with banks and 78% with retailers.\nThese consumers are high frequency and high value. They skew toward millennials and bridge millennials and have established financial relationships.\nThis is the audience brands want to reach, and they\u2019re ready to let the card be the platform that connects them.\nWhy Agentic Commerce Shortens the Window\nThe case for a better offers economy stands on its own in the current environment. But agentic commerce makes the window for building it shorter than most issuers and merchants currently appreciate.\nAI-powered agents already assist consumers with purchase decisions. The next generation won\u2019t assist. They\u2019ll execute. A consumer sets preferences and constraints and the agent handles the research, the selection and the transaction. In that environment, the offers ecosystem as currently constructed isn\u2019t just inefficient. It\u2019s irrelevant.\nRead More: What Happens to Stores When AI Agents Do the Shopping?\nAn AI agent doesn\u2019t hunt for promo codes. It doesn\u2019t sign up for email lists. It doesn\u2019t activate loyalty IDs. If an offer can\u2019t be accessed programmatically through a credentialed interface the agent can query and apply without intervention, the offer doesn\u2019t exist for that transaction. The promotional dollars behind it simply don\u2019t connect.\nA tokenized smart credential is best positioned to survive this transition. An offer embedded at the card credential layer doesn\u2019t need the consumer or the agent to do anything. It\u2019s queried, surfaced, applied and attributed automatically. The agent selects the payment credential that delivers the most value at the point of purchase. The card with the richest offer layer wins the transaction by default, without ever competing for attention at a checkout screen.\nRead More: Demystifying AI\u2019s Capabilities for Use in Payments\nThe card that hasn\u2019t built this capability will not lose that competition gradually. It will lose it at scale, as agent-mediated commerce moves from a niche behavior to a default one over time.\nThe Offers Economy Bet Worth Making\nThe argument for a better offers economy ultimately rests on a behavioral claim the PYMNTS Intelligence report makes with data-driven clarity.\nOffers change what people buy, how much they buy and which payment method they use to buy it.\nAccording to the data, these aren\u2019t incremental improvements. They\u2019re substantial shifts in commercial outcomes driven entirely by the presence of a relevant, timely, frictionless offer.\nThe current offers economy captures only a fraction of this behavioral potential because it was designed around data extraction rather than value delivery, and because the friction it introduces at every stage causes the majority of consumers to never encounter the offer at all or to abandon it before redemption.\nAn embedded smart offers model inverts this logic entirely. The offer finds the consumer. It\u2019s relevant because it\u2019s built on verified transaction data rather than demographic stereotypes.\nIt\u2019s applied automatically, so the behavioral effect does not depend on the consumer remembering to act at the right moment or clearing two, three or more hurdles to redeem it, if they see it in the first place.\nAnd it creates a data loop that allows brands to observe outcomes directly and improve targeting with each iteration.\nThe consumers most ready to engage with this model represent more than half the U.S. adult population.\nThe brands most likely to fund it are the ones watching their promotional budgets produce diminishing returns in channels that can\u2019t demonstrate attribution.\nThe merchants most likely to benefit are the ones that need new sources of promotional revenue that don\u2019t require them to sacrifice already thinning margins.\nThe issuers most likely to build it are the ones that understand the card credential is no longer just a payment instrument. It\u2019s foundational for building a relationship with their customer.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\n \n\r\n\r\nThe post Why the Offers Economy Is Broken appeared first on PYMNTS.com.", "date_published": "2026-04-15T06:53:51-04:00", "date_modified": "2026-04-15T06:53:14-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/2026-Web-Hero-Image-KLW-NEXT-1200x780-04142.jpg", "tags": [ "agentic commerce", "AI", "artificial intelligence", "Card Linked Offers", "Karen Webster", "KLW Commentary", "Main Feature", "News", "next newsletter", "offers economy", "promotions", "PYMNTS News", "Retail" ] }, { "id": "https://www.pymnts.com/?p=3633975", "url": "https://www.pymnts.com/artificial-intelligence-2/2026/the-battle-for-ai-isnt-about-models-its-about-habits/", "title": "The Battle for AI Isn\u2019t About Models. It\u2019s About Habits", "content_html": "Think about how you shop. Not how you used to shop, but how you actually shop now. You probably do most of it on Amazon. Not all of it. You still go to a specialty running store for shoes, to a wine shop for a bottle for that special occasion dinner, maybe to a boutique for a dress or fancy shoes that need to be exactly right.
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\r\n\r\n\r\nThe post The Battle for AI Isn\u2019t About Models. It\u2019s About Habits appeared first on PYMNTS.com.
\n", "content_text": "Think about how you shop. Not how you used to shop, but how you actually shop now. You probably do most of it on Amazon. Not all of it. You still go to a specialty running store for shoes, to a wine shop for a bottle for that special occasion dinner, maybe to a boutique for a dress or fancy shoes that need to be exactly right.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nBut for everyday things, all of the things you need reliably, relatively quickly and without a lot of rigamarole, Amazon is probably your go-to.\nIt didn\u2019t take long. By the beginning of the millennium, four to five years after it launched, Amazon had secured its spot as the world\u2019s largest online retailer. And it deepened its moat over time as Amazon added more and more to its inventory and more and more sellers with more products to its digital shelves. Books first, then music, then electronics, then apparel, then grocery, then healthcare, then used cars, then prom gowns, bridal dresses and patio furniture.\nEventually the question of where to look first was replaced by a reflex to pop open the app with the lowercase \u201ca\u201d on the home screen.\nThat reflex is now forming in GenAI. And the story of how consumers are choosing AI models is tracking the same arc.\nUnderstanding one is the fastest way to understand the other. And to predict what the competitive landscape in AI is likely to look like not next quarter, but over the next three to five years. The data is already telling the story. The question is whether the platforms competing in this space are reading it right.\nThe Habit Is Already Here\nA decade ago, conversations at my hair salon over the holidays were about Alexa. Not the countertop Echo device, but the personification of an assistant whose wish would become their command.\nIn 2026, conversations at those same salons are remarkably similar. But the main character is not Alexa anymore. It is \u201cChat\u201d or \u201cGPT.\u201d People are asking about the best mattress to buy, whether it is faster to fly or drive from Boston to New York, how to plan the most perfect 13th birthday party and how to assemble a capsule wardrobe for spring. GenAI, and how consumers use it, has gone mainstream.\nPYMNTS Intelligence surveyed roughly 15,000 U.S. consumers over the last five months about how they complete 54 personal tasks across nine categories of daily life. The goal was to document which tasks are now AI-first and where AI models have replaced traditional methods of primary consumer interface.\nToday, 146 million people, 56% of U.S. consumers, use an AI model to complete at least one personal task. The share of non-users fell from 48% to 44% between October 2025 and February 2026. Mainstream users rose from 30% to 34%. There is a steady, directional move toward more AI use by more people doing more things.\nNot a hockey stick. Not a hype cycle. A habit forming at scale, with no signs of slowing down.\n\nThe tasks driving this adoption are mundane, even a little tedious. Writing and communication dominate, with editing and drafting emails representing the most common activity. Product discovery follows closely. In fact, finding product links is the single most-performed task across the entire survey sample, the top-ranked activity, and the most stable trend line across the five-month dataset. The traditional method users are leaving behind is Google.\nRead More: Why 30 Million US Consumers No Longer Search\nMore than a quarter of consumers consult AI models for health-related activities, specifically looking up symptoms and researching medications. Daily task lists, meal planning and grocery lists all show gradual upward momentum.\nThese are not one-off use cases but the daily slog of modern life. AI models are becoming a convenient one-stop shop to address them all.\nAI habits are formed at the low-stakes, high-frequency end of the day-in and day-routine, the activities that everyone does more or less daily or multiple times a week. Exactly where Amazon built its lead in online retail.\nAmazon won by being the most reliable first stop for the broadest range of everyday needs, and by making the experience of starting there so easy and reliable that choosing something else required a reason not to. It\u2019s the same position ChatGPT is building right now, even as other models emerge to capture more specialized activities.\nThe More Than One Model Reality\nThe average active AI consumer now engages with more than two platforms; power users, the 10% of consumers who represent roughly 30 million people, engage with nearly four. On the surface that looks like a fragmented market. It really isn’t.\nThink about how most Americans shop. When looking at consumers\u2019 latest retail purchase, 55% shopped at a total of 13 stores. Amazon captures more than 56% of their online spend. The rest goes to a handful of specialists for those one-off, less routine purchases. The boutique with the better shoe selection, the wine shop where someone knows their palate, the specialty fish store worth driving to. It looks like variety. It’s actually much like the shopping hierarchy: one go-to that handles the everyday, and a short list of niche players \u00a0that earn their slots by doing something their go-to doesn’t do as well.\nThe multi-model AI story is the same story. Engaging with multiple platforms is not the same as dividing time equally among them. ChatGPT anchors the stack for every user segment. Other models earn specific slots based on use cases. The consumer using more than one platform is real. Parity of use among those platforms is not.\n\nAI Task Map: Complexity vs. Frequency\nWhat determines where a model earns its slot, and where ChatGPT holds its ground, becomes clear when you map the 54 daily tasks PYMNTS Intelligence tracks against two dimensions: how complex and high-stakes the activity is, and how frequently the consumer performs it.\n\nThe Commodity quadrant, high frequency and low stakes, is where habits form and where ChatGPT wins by the simple fact of having been there first and improving over time. Rewording a sentence, finding a product link, building a grocery list. The consumer knows in three seconds whether the answer is right. The cost of the wrong one is a paragraph with too many em dashes or radishes on the grocery list instead of radicchio. It\u2019s where most consumers started their AI-first journeys, and where the critical mass of everyday use still lives.\nRead More: How Time Became the Next Great Asset Class\nThe Trust Gap quadrant, high stakes and lower frequency, is where the market is still being decided. Medication interactions. Loan comparisons. Whether that non-compete clause is enforceable.\nUnlike the commodity tasks, the consumer has almost no way to verify the accuracy of the output in real time. Adoption lags here not because consumers don’t value it, but because trust is harder to build when the tasks happen less often and the stakes of getting it wrong can be consequential.\nThis is where platform differentiation does matter, and where the AI fluency of the user becomes more apparent and adoption-defining.\nChatGPT leads all nine task categories, from 42% in shopping to 60% in writing. it looks different among power users depending on the task. Gemini gains ground in financial and health-related tasks. Claude does in complex document and contract review. Copilot holds where Microsoft Office integration makes specific outcomes more personalized.\n\nWe find that power users have already built what the rest of the market hasn’t needed to yet: an AI portfolio with a primary model for everyday tasks and others where they\u2019ve discovered that different models deliver a better outcome.\nThe more subtle insight is that this behavioral pattern did not emerge from how any of these models set out to build a user base. It emerged from trial, error and engagement over time. Trust grew incrementally, activity by activity, until the habit of starting somewhere became the habit of staying there.\nThe Invisible Influencer: Consumer vs. Enterprise\nThe most important force shaping how consumers discover new AI models isn\u2019t visible to them. It isn\u2019t marketing. It isn\u2019t social media. It\u2019s the workplace.\nFor most consumers, the journey starts the same way Google once did. Personally. A question. A task. Something they need to figure out before the kids wake up or in the middle of a busy day. ChatGPT was the model they found first, because it was there first, so it became the model they trusted first, one low-stakes task at a time.\nRead More: Gen AI: The Technology That Broke the Adoption Curve\nAs that habit deepens, it moves into work. The same model now shows up in higher stakes moments: drafting memos, summarizing documents, preparing for meetings. The personal habit now becomes the professional one. But as the work becomes more complex, and the stakes attached to the output become more related to job performance, the limits of that default start to show. Not enough to displace it, but enough to create a reason to consider something new.\nOften, the second model doesn\u2019t show up because the user goes looking for it. It\u2019s introduced. A colleague uses something different. A team adopts a tool for a specific workflow. A company standardizes on a platform. Or the task itself becomes demanding enough that the user is pulled toward something more precise.\nThis is how models like Claude gain ground. ChatGPT expands outward from the consumer, earning trust in low-stakes, high-frequency tasks and carrying that trust into the workplace. The habit comes first; the enterprise follows.\nClaude follows the opposite path. It is encountered in the context of work, where precision matters and the cost of getting it wrong is higher. Contract analysis, code review and complex research are not entry points for casual use. They are reasons to adopt something new. In this case, the enterprise is not the endpoint but the starting point.\nRead More: How Leading Enterprises Really Measure Gen AI ROI\nThese models earn trust in high-stakes moments rather than everyday ones, which is why its growth shows up differently. Not as a default, but as a deliberate choice tied to specific use cases.\nWhat emerges is not a fragmented market, but a structured one. One model becomes the default, the place users start without thinking, while others earn their place more narrowly, tied to tasks where performance justifies the switch. It looks like variety on the surface. In practice, it is a hierarchy.\nThat is the fork that matters. One path builds from habit outward. The other builds from necessity inward. Every platform in AI is now, whether intentionally or not, choosing which side of that fork to pursue.\nRead More: Big Tech Faces the AI Innovator\u2019s Dilemma\nGemini: the GenAI Dog That Hasn’t Barked\nOf every company positioned to own the daily AI habit, Google started with the most built-in advantages. Gmail has more than 1.8 billion active users. Google Search handles billions of queries per day. Google Calendar, Maps, Photos and Drive are the embedded infrastructure of daily life for more than a billion people. If habit formation is about being the first stop in a consumer’s day, Google was already there. It had been there for twenty years. And yet, when consumers name the AI model they use most for daily tasks, the answer isn\u2019t Gemini.\nChatGPT started in a different place. A conversational model that says, \u201cask me anything.\u201d Not the reflex of searching for something already known, but the instinct to think out loud and work through something more complex than a few keywords could capture. Google goes and fetches what consumers have already decided they want. ChatGPT became where consumers go to figure out what to do next with options and detail.\nIn some ways, the familiarity consumers have with Google may actually be working against Gemini rather than for it. Consumers do not associate Google with the kind of open-ended, generative, think-it-through experience that defines how they are now using AI. They associate it with looking things up with a bunch of links received in return. That association, built over two decades, is sticky in ways that a product rebrand cannot easily overcome. And for many so far, that their experiences with it have proved disappointing.\n Read More: Why AI Shopping Is Still Just a Smarter Search Bar\nTwo Different Tuesday Mornings\nSo, here\u2019s what a Tuesday morning now looks like for more than half of Americans.\nCharlotte wakes up in Baltimore and uses ChatGPT to help draft a text to her kid’s teacher. Before she’s had her first cup of coffee, she’s also asked it to find the best price on a coffee maker and pull the purchase link. By lunch, she’s used it to look up the symptoms of a rash that appeared on her arm. Not to self-diagnose, she’d tell you, just to know whether it’s worth running to urgent care. In the afternoon she used it to rewrite an email requesting a merchant refund, producing a draft that was considerably more diplomatic than her original. By evening, she’s back at the GPT prompt to plan a packing list for next weekend’s camping trip and build a grocery list based on the recipes she wants to make for the week.\nCharlotte never once stopped to think about whether she was using an agent, or whether AI was even the right word for what she was using. She just did what she needed to do, at a prompt she already trusted.\nThree hours later, Katie wakes up in Seattle and opens Gemini to scan her investment portfolio, asking it to summarize overnight moves in three sectors she’s watching. She switches to ChatGPT for the first draft of a memo she needs to circulate before ten, and to get suggestions for a ten-day European trip in July. At lunch she asks ChatGPT to draft an agenda and fundraising marketing plan for a non-profit board she chairs. By midday she’s in Claude, asking it to explain the meaning of a contract clause her lawyer flagged. She uses Copilot to create a travel itinerary around a business conference in two weeks, because she’s learned it surfaces hotel availability better than the others.\nKatie doesn’t think of herself as a consumer using several AI tools. She just thinks of herself as someone who uses the right tool for the job: a primary model for most things, specialists for the tasks where they perform better.\nTwo consumers. Same Tuesday. Very different relationships with AI, and with the platforms competing for their attention.\nThe Habit Hardens\nLet\u2019s end how we started. With shopping.\nYou probably didn’t sit down one day and decide that Amazon would be your default for everyday essentials. You just used it. Then used it more. Then stopped noticing you were making a choice. The habit formed because of the friction you stopped experiencing. The comparison shopping you no longer bothered with, the store you used to drive to that now feels like too much. Amazon didn’t win your loyalty. It won your reflex by being convenient and reliable and trusted.\nRead More: The First Chatbot Consumers Try May Be the One They Stick With\nThat is exactly what is happening in GenAI right now, and the window for platforms to shape habits may be narrower than most of them seem to appreciate.\nConsumers are not evaluating AI models. They are using them. And with each use, the mental calculus of where to start shrinks a little more. Charlotte in Baltimore isn\u2019t going to wake up one morning and reconsider her relationship with ChatGPT. She is just going to keep opening the app. The task will change. The prompt will change. The reflex won’t. As long as the output remains valuable.\nFor the platforms competing in this space, this is both the opportunity and the threat.\nThe opportunity is that habits at this stage are still forming. The commodity quadrant is not fully locked, the trust gap quadrant is wide open, and the AI-fluent consumer who becomes a power user is still building her AI stack. The threat is that every day a consumer completes a task with a model and walks away satisfied, that model gets a little harder to displace.\nAmazon’s lesson was not that it built the best online store. It was that it built the most reliable first stop, across the broadest range of everyday needs, before its retail competitors understood that the first stop was the thing worth competing for. By the time they did, the habits were already set.\nThe GenAI market is in the first few innings of that same game. ChatGPT holds the first-stop position for the everyday. Claude is earning its ground at the high-stakes end, task by task, credential by credential. Gemini is still looking for the moment when its structural advantages convert into behavioral ones. Copilot has inroads for the tasks where Microsoft integration produces an outcome worth switching for.\nWhat happens next will be determined less by product releases and more by how habits are formed and how embedded that reflex is in everyday life. And whether the platforms understand that they aren\u2019t just competing for users, but for the order in which those users show up at their prompt.\nThe consumer who opens ChatGPT before she\u2019s had her coffee isn\u2019t likely to be pulled away by a better feature set alone. If she changes her behavior at all, it will be because another model earns a specific role in her routine. Much the way a great wine shop earns its place, not by replacing Amazon, but by being worth the detour. That is the competitive reality of the next three to five years. Not a single winner. Not a fragmented free-for-all. A hierarchy, with one anchor handling the everyday and a short list of niche players competing for attention at the edges.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post The Battle for AI Isn\u2019t About Models. It\u2019s About Habits appeared first on PYMNTS.com.", "date_published": "2026-04-09T06:53:48-04:00", "date_modified": "2026-04-09T07:54:06-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/04/2026-Web-Hero-Image-KLW-NEXT-1200x780-update2.jpg", "tags": [ "Agentic AI", "AI", "AI models", "Amazon", "Anthropic", "artificial intelligence", "ChatGPT", "Claude", "consumer insight", "Copilot", "Karen Webster", "KLW Commentary", "Main Feature", "Microsoft", "News", "OpenAI", "PYMNTS Intelligence", "PYMNTS News", "artificial intelligence" ] }, { "id": "https://www.pymnts.com/?p=3600001", "url": "https://www.pymnts.com/news/retail/2026/davids-bridal-ceo-kelly-cook-on-betting-big-and-knowing-when-not-to/", "title": "David\u2019s Bridal CEO Kelly Cook on Betting Big and Knowing When Not To", "content_html": "Watch more: Monday Conversation With Kelly Cook of David\u2019s Bridal
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\r\n\r\n\r\nThe post David\u2019s Bridal CEO Kelly Cook on Betting Big and Knowing When Not To appeared first on PYMNTS.com.
\n", "content_text": "Watch more: Monday Conversation With Kelly Cook of David\u2019s Bridal\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nWhen I sat down with Kelly Cook, it didn\u2019t feel like an interview. It felt like we were getting right back into a conversation that never really stopped.\nCook has always been direct about what she\u2019s trying to do at David\u2019s Bridal. As she approaches her first anniversary as CEO, that clarity hasn\u2019t softened. If anything, it\u2019s sharpened. She didn\u2019t take the job to tweak a legacy business. She took it knowing that a company built around a single, emotional purchase had to be reimagined entirely. And quickly, with a totally different mindset \u2014 revolutionary, not evolutionary. A strategy that would also energize the team.\n\u201cI told the [team], sometimes when you’re in a dark place, you think you’ve been buried, but you\u2019ve actually been planted.\u201d\nThat line stopped me. I joked with Cook that she should put that on a cup or a mug.\nBut that line captures not just the ambition of what she\u2019s doing, but the standard she\u2019s holding herself and her team to, as they rebuild a brand that had already been through two restructurings in four years.\nRethinking the Problem Before Solving It\nCook\u2019s approach to the transformation of David\u2019s isn\u2019t to start with solutions. She starts by reframing the problem.\nDavid\u2019s, on paper, is a retailer. But as she and her team dug into the data, what they saw didn\u2019t look like a single-transaction business at all. It looked like a long, complex, emotionally loaded journey. One that extends over 18 months and involves hundreds of decisions.\n\u201cThe Big Day has about 300 tasks,\u201d she said. \u201cAnd she\u2019s buying 18 outfits,\u201d she added, still sounding slightly surprised by that realization.\nBut it was that insight that changed everything. If the company continued to organize itself around selling a dress, it would miss most of the value. And most of the relationship.\nWhat followed was what Cook calls the shift from \u201caisle to algorithm\u201d: using data not just to understand the customer, but to stay connected to her across multiple moments. It\u2019s also changed how decisions get made internally. The goal now is speed with accountability. Turning data into insight, insight into action, and then quickly measuring what worked and what didn\u2019t.\nThat last part matters as much as the first.\nHow She Decides: Instinct, Data \u2014 and a Willingness to Walk Away\nCook talks about \u201cbig bets,\u201d but what\u2019s interesting is how often those bets involve deciding not to keep going.\nWe talked about live shopping, which had an early moment of promise for the company. The first event performed well with strong engagement, strong results. By most conventional measures, it was a success.\nBut something didn\u2019t sit right with her.\nEven as the numbers came in, she had a sense that the broader trend was already peaking. That consumer excitement around live shopping wouldn\u2019t sustain at a level that justified continued investment.\nSo they stopped.\nThat\u2019s not an easy decision to make, especially after an initial win. But it reflects how Cook thinks about return on investment over time, not just in the moment. She\u2019s less interested in chasing spikes than in building systems that compound.\nIt\u2019s also an example of how she balances data with instinct. The data said \u201cgo.\u201d Her experience, and her read on where the market was heading, said \u201cbe careful.\u201d\nShe listened to both, but ultimately acted on the longer-term signal.\nExpansion Isn\u2019t a Strategy \u2014 It\u2019s a Consequence\nThe move into categories like prom, graduation and other formal occasions is often described as expansion. But in Cook\u2019s telling, it wasn\u2019t really a choice. It was an outcome of understanding the customer more fully.\n\u201cThe woman who comes to us for one occasion will come back,\u201d she said.\nProm became a particularly important entry point, not because the company set out to chase a younger demographic, but because it realized that identity, style and occasion don\u2019t map neatly to age.\n\u201cMom is an attitude, not an age,\u201d she said. Another one of those lines that feels like it belongs on that same coffee cup.\nWhat looks like category expansion is really a shift toward continuity. Creating multiple reasons to engage with the brand over time, and increasing lifetime value in the process.\nMeasuring What Actually Matters\nFor all the talk of transformation, Cook is disciplined about how she measures progress.\nFinancial performance is, of course, part of it. But she puts equal weight on engagement, repeat behavior and customer feedback. The signals that indicate whether the relationship is actually deepening.\nAnd then there\u2019s trust.\nShe comes back to that word often, and not in a vague way. Trust, for her, is both a brand metric and a business driver. If customers trust David\u2019s to show up for more than just the wedding day, the rest of the model works. If they don\u2019t, it doesn\u2019t.\nThat\u2019s why the shift to a broader ecosystem, including partnerships with Amazon, Walmart, DoorDash and independent boutiques isn\u2019t just about reach. It\u2019s about relevance.\nThe retailer\u2019s DoorDash partnership, by the way, is crushing it in Vegas, Cook said.\n\u201cWe want to serve every bride,\u201d she told me, \u201cwhether she buys from us directly or not.\u201d Or someone who needs a dress in 20 minutes.\nAs she put it in a way that stuck with me. The goal is to \u201cbe the engine in everybody\u2019s car,\u201d not just a single car on the road.\nMoving Fast \u2014 and Accepting the Misses\nOf course, not every bet works, and Cook doesn\u2019t pretend otherwise.\nArtificial intelligence is a good example. The company is using it across customer experience, operations, data analysis and content creation. But speed introduces risk. At one point, an AI-generated marketing asset went out with a visible error, the kind of mistake that reminds you how quickly things can go wrong when processes haven\u2019t caught up to ambition.\nHer response wasn\u2019t to pull back.\n\u201cI\u2019d rather fall forward going fast than fall backwards going slow,\u201d she said.\nThat doesn\u2019t mean being careless. It means accepting that in a period of transformation, some mistakes are the cost of momentum, and that the bigger risk is hesitation.\nOne Year in \u2014 and Still Pushing\nAs we wrapped up, what stood out wasn\u2019t just how much has changed in the past year, but how much Cook still sees ahead.\nShe\u2019s not talking about stabilization. She\u2019s talking about building a fundamentally different kind of company. One that doesn\u2019t depend on a single moment but participates in a lifetime of them.\nAnd she\u2019s doing it the same way she started: by making big bets, measuring them rigorously, and being willing to change course when the signal, whether from data or instinct, tells her to.\nNot buried.\nPlanted.\nAnd yes. I still think that belongs on a coffee cup.\n\r\n\r\nThe post David\u2019s Bridal CEO Kelly Cook on Betting Big and Knowing When Not To appeared first on PYMNTS.com.", "date_published": "2026-03-30T04:03:42-04:00", "date_modified": "2026-03-29T21:11:47-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/Monday-Conversation-Main-Feature.png", "tags": [ "David's Bridal", "digital transformation", "Main Feature", "monday conversation", "News", "PYMNTS News", "pymnts tv", "Retail", "video" ] }, { "id": "https://www.pymnts.com/?p=3565339", "url": "https://www.pymnts.com/artificial-intelligence-2/2026/why-ai-shopping-is-still-just-a-smarter-search-bar/", "title": "Why AI Shopping Is Still Just a Smarter Search Bar", "content_html": "More than a year ago, I asked AI to help me buy a toaster. Not to browse. I knew exactly what I wanted, down to the brand, and I gave the LLM every advantage a real buyer could offer. I wanted to test whether I could find and buy without leaving the chat. The AI produced a thorough, well-documented list, albeit somewhat skimpy. None of the brands listed included the toaster I already knew I wanted to buy.
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\r\n\r\n\r\nThe post Why AI Shopping Is Still Just a Smarter Search Bar appeared first on PYMNTS.com.
\n", "content_text": "More than a year ago, I asked AI to help me buy a toaster. Not to browse. I knew exactly what I wanted, down to the brand, and I gave the LLM every advantage a real buyer could offer. I wanted to test whether I could find and buy without leaving the chat. The AI produced a thorough, well-documented list, albeit somewhat skimpy. None of the brands listed included the toaster I already knew I wanted to buy.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nBut that wasn\u2019t the most interesting part of my experiment.\nEven if the AI had identified the right toaster, it still couldn\u2019t confirm the actual price, verify that the item was in stock or tell me whether it could be delivered within any reasonable timeframe. It could not place the order. To do that, I was referred to a merchant site I had never heard of to navigate the checkout.\nSo, I ended up doing what most people still do instead. \u00a0I went to Amazon and bought the toaster there.\n\n\n\n The infrastructure required to actually curate all of the available options, and then execute the purchase, is a big gap yet to be filled.\n\n\n\n\nI have repeated my Toaster Test periodically ever since. The LLMs are smarter, faster and more conversational than they were a year ago. They have added a few better brands to the list they produce. They still cannot produce the brand I wanted to buy, confirmed in stock, at a real price, available for purchase right now. The research loop is impressive. The transaction loop is anything but.\nMy test is not evidence of a technology failure. In fact, the AI part worked amazingly well. It is a marketplace failure.\nAnd it illustrates the core challenge of agentic commerce in 2026.\nThe models are breathtakingly amazing at helping consumers do the research and the shortlisting of options. The infrastructure required to actually curate all of the available options, and then execute the purchase, is a big gap yet to be filled.\nWhat is billed as a revolution in commerce is, for now, mostly a highly intelligent search bar. A better one than Google. A more conversational one. But still, at its core, a tool that finds the answer and then hands the consumer off to someone else to close the deal.\nThe Consumer Shift Is Real\nThe behavioral shift behind this problem is no longer theoretical. It has gone mainstream quickly.\nPYMNTS Intelligence research from January 2026 found that 41% of consumers have already used dedicated AI platforms for product discovery. More striking is that a third say they have fully replaced their prior methods. They are not layering AI on top of old habits.\nThey shut the door on the old way and left.\nRead More: Why 30 Million US Consumers No Longer Search\nAI adoption crossed 54% of U.S. adults in January 2026, up ten points in a single month.\nAmong millennials, two in three have used a conversational AI assistant for research.\nThat\u2019s just the tip of the behavioral iceberg. \u00a0In December 2025, 34% of AI power users relied on native AI interfaces as their primary method for shopping discovery.\nOne month earlier that share was 22%. Among light users, reliance on AI models \u00a0jumped from 5% to 16% in the same period.\nThese are not gradual shifts. They are new habits forming at a pace the industry did not anticipate.\nRead More: Gen AI: The Technology That Broke the Adoption Curve\nThe experience driving this shift is genuinely different from traditional search. Instead of scrolling through pages of links and sponsored listings, consumers receive a structured answer that explains the tradeoffs between competing products and can be refined through conversation until it matches the actual buying decision. It is something keyword searches could never deliver with any sort of precision.\nBut then the consumer leaves the conversation and goes somewhere else to complete the purchase.\nThe question that matters now is not whether agentic commerce will eventually close that gap. It is who becomes a casualty on the agentic highway, and who benefits. \u00a0And how.\nGoogle and Shopify on the Defense\nLet\u2019s start with Google, because the damage there is real and already underway, even if their headline numbers still look healthy.\nIn Q4 2025, Google Search revenue grew 17% year over year. Gemini has 750 million monthly active users. Alphabet crossed $400 billion in annual revenue for the first time. This is not a wounded player. It is also not the dominant one it used to be.\nGoogle has been trying to become a commerce destination since it launched Froogle (a play on \u201cfrugal\u201d) in 2002. It rebranded that effort multiple times, built Shopping tabs, launched Google Express, acquired Pointy\u00a0and embedded Gemini.\nDespite all of it, Google Shopping remains a listing service that shows products and sends consumers somewhere else to buy. The transaction, the customer relationship and the post-purchase experience all happen in someone else’s ecosystem.\nWhat has changed is the top of the funnel. Consumers who once opened Google to research a product are now opening ChatGPT, Claude or Perplexity instead. The PYMNTS data makes that shift quantifiable. A \u00a0third of consumers who have tried AI for shopping discovery have fully replaced their prior methods.\nRead More: What Happens to Stores When AI Agents Do the Shopping?\nThat is not a marginal shift in behavior. It is a structural fracture of the search functionthat Google has monetized for two decades.\n\n\n\n Consumers who switch to AI for research are already bypassing Google at the top of the funnel.\n\n\n\n\nDefending a position is not the same as expanding it. The highest-value transactional queries, consumers who already know what they want and are ready to buy, may still run through Google’s standard channels.\nBut the middle of the funnel, the research and comparison phase where consumer intent is shaped, is moving to AI platforms that Google does not own and cannot easily monetize, despite Gemini\u2019s headline user numbers.\nConsumers who switch to AI for research are already bypassing Google at the top of the funnel. If Google cannot capture them at the bottom with a transaction, it loses the journey, and that customer, entirely.\nGoogle’s answer is the Universal Commerce Protocol, an open standard built with Walmart, Shopify, Target, and two dozen other partners, designed to let AI agents complete full shopping journeys inside Google’s own products. The logic is that if Google can plug trusted commerce players into its AI surfaces, consumers can transact through Gemini while the actual commerce relationship belongs to the retailer. Google becomes the front door without building the back end.\nRead More:\u00a0The Protocol Power Struggle Reshaping AI-Driven Commerce\nThis is more or less what it does today with Google Shopping, except with agents. It is also an acknowledgment that Google cannot build what a commerce network requires. It is borrowing the trust it does not have from partners who do.\nThen there\u2019s Shopify. Their situation is more complicated and more consequential for the merchants who depend on it.\nTwelve months ago, Shopify had the most credible claim to being the open-web alternative to Amazon. Millions of merchants, a high-profile AI commerce partnership with OpenAI that sent competitors scrambling and a narrative about the future of direct-to-consumer commerce that the industry largely accepted.\nRead More: Can Shop Cash Turn Shopify Into an Amazon Challenger?\nThat narrative is now in trouble on two fronts.\nThe OpenAI native checkout partnership is gone. OpenAI pulled it after fewer than 30 merchants went live with a product that had not built the systems to collect state sales taxes. The Shopify landing page built specifically for ChatGPT now redirects to its homepage. Catalog syndication still exists, so Shopify merchants can be discovered inside ChatGPT. But discovery without a transaction is a referral, not a commerce relationship.\nOpenAI is now a discovery layer that sends consumers somewhere else to buy. That is exactly what Google has always been. It’s not where Shopify needs to play.\n\n\n\n Shopify has no fulfillment network, no consumer credentials at scale, and no native AI agent with meaningful adoption.\n\n\n\n\nRead More:\u00a0From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce\nThe second front is Amazon. Shop Direct, launched in February 2025 and expanded to more than 100 million products from over 400,000 merchants, allows Amazon Prime customers to buy from brand websites using their stored credentials through the Buy for Me capability.\nAmazon is now offering independent brands something Shopify cannot match: Prime subscribers, Amazon payment rails, Amazon logistics and an AI agent already in the consumer’s pocket.\nShopify’s strategic response is to position itself as open-protocol infrastructure for agentic commerce, building integrations that make merchants on its platform discoverable across any AI surface. That is a reasonable long-term play.\nRead More: Shopify Bets Big on Agentic AI\nIt\u2019s also coming from a fundamentally weaker position than the one Shopify held a year ago. With an uncertain timeframe for when commerce, at scale on AI models will happen.\nWalmart’s Two-Step Agentic Commerce Play \nWalmart has made a deliberate and smart decision. It has opened its full product catalog online to Google’s Gemini through its Sparky assistant, which surfaces a Walmart-branded experience inside external AI platforms. When a Gemini user searches for a product, Gemini calls Sparky, which opens what Walmart’s head of AI describes as a window inside Gemini where the Walmart shopping experience takes over.\nThis has been packaged as a bold bet on open agentic commerce. But is it?\nWhat Walmart is doing with Gemini is not meaningfully different from what it has always done with search advertising. It\u2019s funneling traffic to Walmart.com through a different front-end interface, hoping to snag net new customers who might not have found Walmart through a traditional search query.\nThe transaction still happens in Walmart’s commerce system. The consumer relationship still belongs to Walmart. The agent is the channel, not the commerce infrastructure. Calling it agentic commerce is a generous description of what is, in practice, a search query that renders inside a chatbot window owned by Google that funnels queries to Walmart.\nRead More: Why the \u2018Person\u2019 of the Year in 2025 Should Be the Chatbot\n\n\n\n The consumer relationship still belongs to Walmart.\n\n\n\n\nThe more interesting and strategically significant bet Walmart is making is Sparky and the opportunity it represents to convert its 100 million per week physical store shoppers into online customers it can monetize through agents. This is where Walmart’s agentic story becomes genuinely compelling.\nWalmart has a physical footprint and, with One Pay, an online wallet and payment method that\u2019s akin to Amazon\u2019s in the physical world. \u00a0If Sparky can move even a meaningful fraction of those in-store shoppers into a digital commerce relationship where Walmart can apply personalization, subscription economics and agentic purchasing, well let\u2019s just say that the opportunity is substantial.\nRead More: Walmart Rolls Out Agentic Advertiser Assistant\nThe risk in the Gemini relationship is also real. Openness means the agent can send consumers to a competitor when the competitor’s offer is better. Walmart is trusting that it wins those comparisons often enough and that Google surfaces its products without letting its own commercial interests shape the ranking. That trust has not been tested at scale. And it runs directly into the structural problem that has undermined every search-based commerce experiment for two decades.\nHow intent and eyeballs get monetized.\nThe Right-Now Winners: Amazon and the Status Quo\nThe reality of agentic commerce in March 2026 produces a short list of those in the pole position right now.\nAmazon is at the top.\nThe status quo of how most people actually shop online is a close second.\nAmazon didn\u2019t wait for the industry to agree on rules. It spent three decades building out a marketplace and AI-enabled it inside its own ecosystem. It\u2019s now extending that solution outward on its own terms.\nRufus, Amazon’s AI shopping assistant, handled 250 million shoppers in 2025, with monthly active users growing 140% year over year. Amazon says that its Rufus users are 60% more likely to complete a purchase than non-Rufus shoppers. When the conversation ends, the transaction completes immediately because the marketplace already exists behind it.\n\n\n\nAmazon is making its marketplace bigger, more open, and more valuable, while ensuring that every transaction, wherever it originates, runs through Amazon’s infrastructure on Amazon’s terms.\n\n\n\n\nShop Direct, however, is the more significant development. By expanding to more than 100 million products from over 400,000 merchants and enabling Prime subscribers to buy from brand websites using stored Amazon credentials, Amazon is doing something strategically important.\nIt\u2019s not just defending its marketplace. It\u2019s making its marketplace bigger, more open and more valuable, while ensuring that every transaction, wherever it originates, runs through Amazon’s infrastructure on Amazon’s terms.\nThe Prime subscriber base is the strategic asset that makes this possible. Prime members spend significantly more than non-Prime customers. Their purchasing intent is high. Their payment credentials are stored. Their trust in Amazon’s fulfillment is established. When Amazon brings consumers to a brand’s website through Buy for Me, it is not sending a casual browser. It is delivering a buyer with an intent to complete a purchase. That is a value proposition that competing platforms cannot replicate without the same combination of payment infrastructure, logistics capability and consumer trust that Amazon has spent 30 years building.\nThat\u2019s despite the recent Perplexity lawsuit, which, within the space of a few days, clarified the legal landscape\u00a0in Amazon’s favor, as another judge just yesterday (March 17) reversed it pending an appeal, according to Bloomberg.\u00a0 For now, the bots are there, free-riding on an ecosystem that they didn\u2019t build, and according to Court filings, they allegedly disguised their bots in order to gain access.\nRead More: Amazon Injunction Could Change the Future of Agentic Commerce\nThe second winner is the status quo of how online shopping actually works.\nMost consumers, most of the time, in 2026, still shop the way they did before AI agents arrived. The search and purchase journey is largely disaggregated. They search Chat or Google or Amazon, then buy from wherever they can find the right price and the fastest delivery.\nRead More: Legacy Business Models Break\nWhat Still Has to Be Built\nThe gap between what AI can figure out and what it can actually do about it is not a small one.\nClosing that gap requires things that do not yet exist in combination. It requires a real business model for catalog access, not just technical protocols. Retailers need a commercial reason to make their live inventory available to AI agents, a compensation structure that makes that exposure worthwhile, and governance that protects them from being used as price comparison tools that send consumers to competitors. The open standards being developed are the pipes, not the economics.\nRead More: What Happens to Stores When AI Agents Do the Shopping?\nIt requires careful thinking about those economics. Every commerce network built on a discovery foundation eventually faces the same crossroads. Merchants pay for visibility. Promoted products rise above better-matched ones. The consumer notices the results look like ads. Trust flies right out the window.\nRead More: Why Trust is Data\u2019s Only Real Currency\nThe AI agent that tells a consumer it found the best product for their needs while receiving compensation from the merchant whose product it recommends is not working in the consumer’s interest and looks like a more sophisticated ad disguised as advice.\n\n\n\nClosing that gap requires things that do not yet exist in combination.\n\n\n\n\nThat requires brands to make hard choices and some big bets.\nMeanwhile, Amazon isn\u2019t waiting.\nIt is using every month of industry delay to extend its position. Every new merchant added to Shop Direct, every consumer who uses Buy for Me, every improvement to Rufus deepens an ecosystem that is already the hardest thing in commerce to replicate. The open AI platforms are not competing against a static target. They are competing against a moving one.\nWhat\u2019s Next \nI have written about the promise of agentic commerce many times. The thesis has always been the same. Consumers want an agent that works for them. One that knows their preferences, understands their constraints, does the research, makes the comparison and closes the deal on their behalf. Not a better search bar. Not a more conversational listing service. An actual agent that shops.\nRead More: How Consumers Want to Live in a Conversational Voice Economy\nThe consumer demand is not in question. It never was. A third of consumers have already abandoned their prior shopping methods entirely. More than 70% say they want to use AI agents to shop. These are not early adopters playing around with the shiny new object. They are mainstream consumers who\u2019ve found something better than what they had and voted with their behavior. Those habits are sticking.\nWhat the past year revealed is that the gap between consumer readiness and commercial infrastructure is wider than most of the predictions of 2024 acknowledged. The technology moved faster than anyone anticipated. The commerce plumbing did not.\nMerchant agreements, live inventory access, payment rails connected to the conversation, governance frameworks, return policies, tax infrastructure, the whole invisible machinery that makes a purchase feel effortless rather than terrifying. None of that gets rebuilt in a product cycle.\nThat doesn\u2019t mean it will take decades. The pace of AI development alone collapses the timeline. The models are improving every few months, the protocols are being written, the coalitions forming, the early experiments producing hard-won, even painful, knowledge about what the market actually requires. And all of it is laying the foundation faster than any prior generation of commerce infrastructure was built.\nRead More: AI Doers Drown Out AI Naysayers\nThe question isn\u2019t whether the loop closes. It is who closes it, under what terms, and who is left holding the better position when it does.\nThis is where time matters in a way the optimists tend to underestimate. Igniting commerce networks is more science than art, more strategic than wishful thinking. Every merchant recruited makes the next recruitment easier. And more consumers more likely to give it a try. Every consumer transaction deepens the trust that makes the next one more likely. Every improvement to fulfillment raises the bar that competitors must clear. Getting to that point is a tedious slog\nThe players who are building real infrastructure now, actually solving the hard coordination problems, are accumulating advantages that will be very difficult to displace once the market tips.\nThe opportunity on the other side of this infrastructure gap is unlike anything commerce has seen since Amazon proved that consumers would trust a website with their credit card if the experience was reliable enough and the selection was wide enough.\n\n\n\n The gap between consumer readiness and commercial infrastructure is wider than most of the predictions of 2024 acknowledged.\n\n\n\n\nWhat it produced is a marketplace that accounts for more than 60% of all online sales and a Prime membership that reshaped how an entire generation thinks about buying things. Not to mention the analog for how online transacting must behave. Agentic commerce, done right, with an agent that is genuinely aligned with the buyer, represents a comparable reset. Not an incremental improvement on search. A different model entirely.\nRead More: How Time Became the Next Great Asset Class\nMy Toast Test still disappoints. The answers are better but still missing my go-to brand. Yes, I am picky. And the AI still can\u2019t buy it for me. But the day that changes is the day I\u2019ll buy, and then do it again, and build the trust that, next time and the time after that, an agent can do all of that for me.\nUntil then, agentic commerce remains just a smarter search bar. A better one than what came before. And one that I will continue to use that way.\nBut a search bar, nonetheless.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post Why AI Shopping Is Still Just a Smarter Search Bar appeared first on PYMNTS.com.", "date_published": "2026-03-18T07:00:46-04:00", "date_modified": "2026-03-17T18:47:07-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/2026-Web-Hero-Image-KLW-NEXT-1200x780-03162.jpg", "tags": [ "agentic commerce", "AI", "AI Agents", "AI shopping", "AI shopping assistants", "Amazon", "Google", "Karen Webster", "KLW Commentary", "Main Feature", "News", "next newsletter", "Product Discovery", "PYMNTS News", "shopify", "walmart", "artificial intelligence" ] }, { "id": "https://www.pymnts.com/?p=3546356", "url": "https://www.pymnts.com/credit-cards/2026/the-next-battle-in-credit-wont-be-for-top-of-wallet/", "title": "The Next Battle in Credit Won\u2019t Be for Top of Wallet", "content_html": "The credit industry has spent decades competing for something consumers were never actually optimizing for.
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\r\n\r\n\r\nThe post The Next Battle in Credit Won\u2019t Be for Top of Wallet appeared first on PYMNTS.com.
\n", "content_text": "The credit industry has spent decades competing for something consumers were never actually optimizing for.\r\n\t\r\n\t\t\r\n\t\r\n\r\n\r\n\t\nTop of wallet.\nIssuers built elaborate rewards architectures to win it. Travel points. Cash back ladders. Category bonuses. Limited time offers designed to nudge one card ahead of another in the consumer\u2019s mental stack.\nBut the data tells a different story about what consumers are actually trying to do when they reach for a card.\nMany aren\u2019t trying to maximize rewards. They\u2019re trying to manage their money.\nEvery household in America is running a balance sheet, whether they think about it that way or not. Paychecks arrive on one schedule. Bills arrive on another. Unexpected expenses show up whenever they feel like it. Consumers are constantly managing the gap between those three things, sometimes consciously, often on autopilot.\n\n\n\n The credit industry has spent decades competing for something consumers were never actually optimizing for.\n\n\n\n\nCredit cards, Buy Now Pay Later, store cards, debit-linked installments, promotional financing. None of these are competing products in the consumer\u2019s mind. They\u2019re tools. Different tools for the same job. Keeping the household financial engine running.\nThe industry built an ecosystem around winning a choice that consumers weren\u2019t ever really making the way the industry assumed they were.\nAnd now, just as intelligent agents are about to take over the payment decision entirely, that assumption is about to be cracked wide open.\nThe Consumer Is Already There\nThe conventional narrative frames credit as a battle between BNPL upstarts and \u201cold school\u201d credit cards. Disruption versus legacy. New versus old.\nThe data doesn\u2019t support that story.\nPYMNTS Intelligence research of a national sample of 2,980 consumers in January 2026 shows that 31% of U.S. consumers used credit card installment plans in the prior three months. Just 12% used BNPL. Seven in 10 used a general-purpose credit card for at least one purchase.\nThat three-to-one \u201cBuy Now Pay Later\u201d gap has held across multiple survey waves and across every generation, including Gen Z, the generation BNPL was purpose-built for. More than four in ten Gen Z consumers report using credit card installments. Millennials show a similar pattern.\nIf consumers were chasing the newest, shiniest thing at checkout, BNPL would dominate. It doesn\u2019t.\n\n\n\n Consumers are choosing options to best manage their cash flow.\n\n\n\n\nWhy? Because consumers aren\u2019t chasing novelty. They\u2019re optimizing for flexibility at checkout and choosing options to best manage their cash flow. Installments inside a credit card account let consumers restructure a purchase into predictable payments, against an already approved credit line, while keeping the broader credit relationship intact. BNPL does that too, especially for smaller purchases or consumers who want to keep their credit lines available for emergencies. But it\u2019s one tool in a kit, not the whole kit.\nConsumers behave like sophisticated financial managers. The industry just hasn\u2019t thought of consumers and their use of credit in much the same way.\nThe Myth of Top of Wallet\nHere\u2019s where the top-of-wallet strategy goes sideways in an agentic world.\nMost consumers don\u2019t have one card. They have a portfolio. A high-limit card for working capital flexibility. A corporate card for travel. A store card for promotional financing. A BNPL account used like a debit card for everyday essentials and smaller purchases that don’t need to touch existing credit lines. They move between these tools depending on the size of the purchase, where they are in the pay cycle, and what the merchant is offering. Which card has open credit to buy? Which one has the promotional rate? Is it worth opening a \u00a0a new account to get the 10% off at checkout?\n\n\n\n Top of wallet becomes irrelevant when the agent doesn\u2019t care which card is on top.\n\n\n\n\nThe industry called winning that mental game \u201ctop of wallet.\u201d Rather than decide, just default to the card that does the best job most of the time.\nThat assumption is about to be upended. Once intelligent agents start executing payment decisions, they won\u2019t optimize for habit or brand loyalty, unless the consumer puts it in the prompt. They\u2019ll optimize for the best financial outcome. The best rate. The best terms. The best fit for the consumer\u2019s finances at that specific moment.\nTop of wallet becomes irrelevant when the agent doesn\u2019t care which card is on top.\nConsumers Already Built the Stack\nLong before anyone in the industry started talking about agentic commerce, consumers had already jerry-rigged their own manual version of it.\nFrom the industry\u2019s perspective, credit cards, installment features, store cards, BNPL and merchant promotional financing compete with each other. From the consumer\u2019s perspective, they solve different problems. A revolving line absorbs unexpected expenses and bridges pay cycles. Card installments convert big purchases into structured payments without opening a new account. BNPL typically handles smaller-dollar purchases with predictable repayment. A grocery purchase late in the pay cycle goes on one account to preserve cash. A furniture purchase gets converted to an installment plan. A merchant promotion routes the purchase to embedded financing.\n\n\n\n Consumers are already doing sophisticated cash flow routing.\n\n\n\n\nConsumers are already doing sophisticated cash flow routing. They\u2019re just doing it manually, with incomplete information, under time pressure.\nWhat looks like fragmentation to the industry is a consumer\u2019s personal credit stack. The next phase of the ecosystem won\u2019t replace it. It will automate it.\nOne Credential, Many Jobs\nHere is where the real disruption sits.\nThink about what a smart credential could actually do. A single account could create access to the credit optionality that consumers already collect in their physical or virtual wallets. And at the moment of purchase it could evaluate every available credit alternative and pick the right one for that moment in time. Pay now and take the cash back. Convert the purchase to an installment and protect short-term cash flow. Apply the merchant\u2019s promotional financing and pay nothing for six or 12 months.\nDraw on a revolving line when flexibility matters more than cost. Sort of like a credit waterfall, using real-time underwriting and intelligence intended to improve cash flow for the consumer.\nThe same checkout moment. Completely different economics depending on what the consumer needs and what the merchant, issuer or brand is willing to offer to close the sale.\n\n\n\n This is not a thought experiment. The building blocks already exist.\n\n\n\n\nThis is not a thought experiment. \u00a0The building blocks already exist. Issuers already embed installment features inside credit card accounts. Merchants already offer promotional financing to drive conversion on big-ticket purchases. Acquirers are enabling agentic underwriting for BNPL players to make more predictive credit offers and decisions. Card networks are enabling issuers to create flex accounts that turn purchases into \u201cpay now, a little later or a lot later\u201d based on user-established parameters. BNPL players are enabling access to their rails and merchant networks for existing banks and credit union debit cards.\nThe next step is having a credential that becomes smart enough to assemble those options in real time, present the best one, and execute it without the consumer having to figure it all out.\nSo, What Happens to Rewards in an Agentic World?\nRewards programs were built on a single promise. Give consumers something they value, and they will change their behavior. Carry this card. Use it here. Reach for it first. The points and miles and cash-back percentages were about influencing behavioral preference at scale.\nIf agents make the payment decision, that behavioral lever largely disappears. The agent isn\u2019t susceptible to aspirational travel advertising. It doesn\u2019t have a favorite airline or a loyalty to a particular hotel chain, unless it is mentioned in the user prompt. It runs the numbers.\nThat doesn\u2019t mean rewards go away entirely. It means they have to earn their place in the decision differently. The rewards programs that survive this shift will be the ones that convert from lifestyle marketing into real-time financial value that improves cash flow for the consumer.\nFor merchants, the agentic payment model could become the most direct line to conversion they\u2019ve ever had. Today, merchants spend billions trying to influence consumers before they get to checkout, through advertising, promotions, loyalty programs, and brand building that may or may not move the needle. Most of that spending happens before the consumer has decided to buy, which means most of it simply hopes for the best, even with using the best of targeting tech.\nIf the agent is making the payment decision based on what produces the best outcome for the consumer, merchants have a direct path to influencing that decision through offers embedded in the transaction itself. A promotional rate that makes a big purchase affordable. An instant rebate that improves the consumer\u2019s cash position today. A financing structure that converts a hesitant browser into a buyer. Rewards that show up as real money at checkout rather than as aspirations to be redeemed someday.\nBrands get a version of this too, and something even more valuable on the side: first party data on what actually drove the purchase. Not survey data. Not modeled attribution. Real transaction data showing which offer, at which moment, converted which consumer. For brands that have spent years trying to measure whether their marketing actually works, that is worth considerably more than the cost of funding the offer.\n\n\n\nMerchants bid to be the most attractive option at the moment of payment.\n\n\n\n\nThe issuer that builds that credential becomes the platform that sits between merchant intent and completed transaction. Think of it as the Google AdWords model applied to checkout. Instead of bidding to appear at the top of a search results page, merchants bid to be the most attractive option at the moment of payment, funding offers that make their product the one the agent selects.\nGoogle built one of the most profitable businesses in history by owning that auction dynamic for attention. The issuer who owns it for transactions is sitting on something just as valuable. And unlike a traditional rewards program, this model turns merchant and brand marketing budgets into revenue. That is a very powerful place to sit.\nThe Cash Flow Buffer Is the Strategy\nTotal consumer credit outstanding has surpassed $5 trillion. The usual headlines call it a debt crisis. Look more carefully and you see something else.\nRevolving credit growth has slowed, but more consumers are maintaining balances rather than paying them down completely. That\u2019s not a sign of financial distress. It\u2019s a sign of deliberate financial management. Consumers are keeping credit available, maintaining a buffer they can draw on when something unexpected happens. In corporate finance, you\u2019d call that maintaining access to a working capital facility. The household version looks exactly the same.\n\n\n\nConsumers are keeping credit available.\n\n\n\n\nPYMNTS\u2019 Consumer Expectations Index shows a roughly 20-point confidence gap between financially stable households and those living paycheck to paycheck. That gap isn\u2019t just about sentiment. It has real-world implications. \u00a0Consumers with cash flow constraints rely more heavily on credit tools that help them smooth cash flow between pay periods.\nWhich means this isn\u2019t niche behavior or a sign of financial mismanagement. Managing cash flow with credit is what everyone is doing, all the time, across every income level and every generation. Whether that person is living paycheck to paycheck or not.\nWhy the Future of Credit Is Cash Flow\nConsumers have been running their own cash flow optimization engine for years. They do it with imperfect tools and incomplete information. They do a pretty good job of it despite the friction.\nIn an agentic commerce world, what\u2019s changing isn\u2019t the consumer\u2019s job. The household CFO isn\u2019t getting replaced.\nWhat\u2019s changing is the assistant they now have to help them.\n\n\n\nThe next era belongs to the credential they never have to think about.\n\n\n\n\nFor decades, the credit card industry competed to sit at the front of the wallet. Rewards were lures. Win the top of the stack, and you win most of the spend.\nAgents will call balls and strikes differently. Based on how well they improve the cash flow position of the consumer making the purchase. Not where a card is in the wallet stack.\nThe industry spent decades competing to be the card consumers remembered. The next era belongs to the credential they never have to think about, because it is always doing the thinking for them.\n \nUntil NEXT time.\nJoin the 21,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post The Next Battle in Credit Won\u2019t Be for Top of Wallet appeared first on PYMNTS.com.", "date_published": "2026-03-11T07:00:02-04:00", "date_modified": "2026-03-10T14:48:25-04:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/2026-Web-Hero-Image-KLW-NEXT-1200x780-03102.jpg", "tags": [ "Agentic AI", "AI", "artificial intelligence", "buy now pay later", "consumer insights", "credit", "debit", "Karen Webster", "KLW Commentary", "loyalty", "Main Feature", "News", "Payment Methods", "PYMNTS Intelligence", "PYMNTS News", "Rewards", "Credit Cards" ] }, { "id": "https://www.pymnts.com/?p=3526239", "url": "https://www.pymnts.com/payroll/2026/welcome-to-the-transactional-economy/", "title": "Welcome to the Transactional Economy", "content_html": "![\"Karen](\"https://www.pymnts.com/wp-content/uploads/2026/02/2026-KLW-LinkedIn-Banner-1200x240-1.jpg\")
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Index: Side Work Patterns in the Labor EconomyThe post Welcome to the Transactional Economy appeared first on PYMNTS.com.
\n", "content_text": "There is a young woman in Chicago who earns money five different ways before noon on a Tuesday. She drives a rideshare app in the morning, drops a delivery on the way home, sells a handmade item from her Etsy shop, completes two micro-tasks on a gig marketplace and picks up a two-hour hospitality shift that evening. She has no payroll department, no direct deposit hitting every other Friday. What she has is a continuous stream of small disbursements, real-time transfers that function, collectively, as her paycheck.\nShe is not an outlier. She is the emerging archetype of the American worker.\nI\u2019ve started calling what she represents the transactional worker, operating within what we at PYMNTS Intelligence now call transactional payroll. It\u2019s an income architecture defined not by schedules but by transactions, not by employment status but by output, not by the calendar but by the clock.\nThe data tells a story that most corporate payroll departments, legacy banks and enterprise HR platforms are only beginning to recognize. We\u2019ve surveyed more than 60,000 U.S. consumers across multiple waves of research to document it.\nThe way tens of millions of Americans earn money has fundamentally changed. The way the financial system pays them has not kept up.\nRead More: From Payroll to Pay Now: How Real-Time Earnings Are Rewriting Work\nThis is not a payroll innovation story. It is evidence of a deeper structural change: the shift from a batch-based financial system built around periodic wages to a transactional economy built around continuous income flow in a real-time economy.\nThe Scale of the Shift\nAccording to PYMNTS Intelligence research commissioned by Ingo Payments, roughly 21% of all U.S. disbursements recipients in 2020 most often received their payouts instantly. By May 2025, that share had nearly tripled to approximately 41%.\nBut the headline figure spans every category of payout. Insurance claims, gaming winnings, loan proceeds, investment returns. The story that matters for the future of work lives inside one specific slice.\nWages.\nWhen we isolate transactional payroll, the disbursements workers rely on as earnings and income, the shift is even more pronounced. Today, 46% of U.S. workers report receiving payouts instantly most of the time. Among those who depend on earnings from gig platforms, tipped shifts, construction sites and long-haul routes, the demand isn\u2019t a preference. It is a structural requirement.\nThe cohorts driving this shift aren\u2019t hard to identify.\nAmong Gen Z workers, 84% received at least one instant disbursement in the past year, and 40% now rely on instant methods more than any other payout channel. For this generation, waiting several business days for earnings to hit their account doesn\u2019t feel like a minor inconvenience. It feels like a design failure, a relic of a system built for a different kind of work.\n\nFour Use Cases Where This Hits Hardest\nThe wage use cases driving this shift are concrete. Our research has examined the specific industries where transactional payroll is most acutely felt, and where the gap between when work is done and when money arrives creates the most friction.\nFour stand out.\nConstruction. Construction workers, particularly day laborers, trade subcontractors and project-based crews, have long operated in an environment where the gap between work completed and wages received can stretch days or weeks. Paper checks and ACH batch cycles are still the norm on many job sites, creating liquidity pressure for workers carrying out-of-pocket costs for tools, transportation and materials before a paycheck clears.\nOur research found that construction is among the industries where instant wage access produces the most immediate lift in worker retention and shift acceptance, precisely because the cash-flow timing risk is highest.\nRead More: Real-Time Payments Give Contractors Back 2 Workweeks a Month\nHospitality and Tipping. As digital payments have replaced cash at restaurants, hotels and event venues, tip disbursement has become a genuine operational problem. Workers who once left a shift with cash in hand now wait days for tips to be batched, processed and cleared. Our research tracked hospitality as one of five key industries rapidly adopting instant payment rails to resolve this mismatch.\nAmong tipped workers, 65% say they need their earnings instantly or within a single day. This reflects the financial architecture these workers live in, where the shift-by-shift tip cycle is the primary income cadence and delayed access disrupts every downstream obligation.\nTransportation and Trucking. Trucking represents one of the clearest cases for per-trip, per-load transactional payroll. Owner-operators and independent drivers carry upfront costs such as fuel, tolls and maintenance before they\u2019re paid for the haul. When settlement takes days, those costs create working capital gaps that small operators cannot easily absorb. Instant disbursements serve a dual purpose here: they reduce driver turnover for carriers and fleets, and they eliminate the paper check dependency that has historically characterized the industry.\nGig Platforms. The gig economy remains the most digitally advanced sector for instant wage disbursement. Nearly six in 10 payouts made by corporate senders to consumers for gig work now move via instant methods, the highest rate of any industry in our research. But the critical insight is that gig platforms aren\u2019t the outlier. They are the template. The same logic driving instant wage adoption in the gig economy is now migrating into property management, event staffing, healthcare support, retail and logistics.\nRead More: Why Gig Economy Companies See Payables As Key To Success\nMany of the workers across these four use cases sit within what we call the Labor Economy: approximately 60 million U.S. workers concentrated in hourly, hands-on roles across logistics, hospitality, retail, construction and healthcare support.\nThese workers account for nearly 37% of the entire U.S. workforce and drive more than $1.7 trillion in annual consumer spending. But their savings sit roughly 40% below the national average. When their income is delayed or unpredictable, the consequences don\u2019t stay contained to their households.\nA 1% wage change across this population translates to approximately $17 billion in GDP impact. That is not a rounding error. That has a non-trivial economic impact.\nTransactional Payroll: A New Architecture of Earning\nIt\u2019s worth pausing on what transactional payroll actually means, because it captures something that the phrase \u201cgig economy\u201d doesn\u2019t fully convey.\nTransactional workers are not workers who are scraping by, stuck between real jobs or failing to find traditional employment. In many cases, they\u2019ve actively chosen a different relationship with income, one built around autonomy, flexibility and the ability to match earnings to obligations in real time.\nFor one-third of millennials, transactional work is now the primary source of cash flow, not a supplement to a traditional salary. For roughly half of Gen Z, core income arrives through online selling, freelance services or platform-based work. These workers are not fringe cases of the labor market. They are increasingly its center.\nRead More: Instant Payouts Become the New Paycheck in a Real-Time Economy\nAnd the financial system\u2019s continued insistence on treating them as exceptions, routing their earnings through legacy payroll cycles, ACH batch processes and clearing windows designed for a different era, is creating friction with measurable economic consequences.\nConsider the math. A worker who earns $200 on a Tuesday and can\u2019t access those funds until Friday or the Friday after that isn\u2019t merely inconvenienced. That worker may pay a late fee, miss a payment, borrow at high cost, or decline a subsequent shift because they can\u2019t front the gas money to take it. Each of those outcomes has a price, to the worker, to the platform that depends on their availability, and to the broader economy that depends on her spending.\nOur behavioral data, spanning multiple waves of study across the U.S. workforce, makes the stakes tangible.\nAmong gig workers, 54% say they need same-day access to their funds. Among tipped workers, that urgency climbs to 65%. Freelancers and contractors, whose work tends to follow longer billing cycles, still show meaningful urgency, with 48% requiring same-day access once work is completed.\nFor these workers, real-time access to pay is not a nice-to-have. Given thin savings, variable income and fixed obligations, same-day access is a liquidity requirement.\nThe Infrastructure Gap\nIf the demand for transactional payroll is so clear, why does the infrastructure to support it remain so incomplete?\nThe answer isn\u2019t primarily technology. The rails for instant payments exist and are operational. The issue is adoption, incentive and institutional inertia on the sender side.\nRead More: Fee Sensitivity and the Opt-In Economics of Instant Payouts\nDespite overwhelming worker demand, only 36% of gig platforms currently offer instant payments consistently. The remaining 64% offer them only sometimes, rarely or never. That gap represents both a significant friction point in the labor market and a significant commercial opportunity for providers and platforms willing to close it.\n\nThe adoption data reveals something important about how workers respond when platforms do offer instant payments. It is not gradual. When instant payouts become available, 59% of all disbursements on that platform go instant almost immediately. Workers don\u2019t need to be educated or incentivized. They\u2019ve been waiting for the option.\nAnd the stickiness is equally compelling.\nOnce a consumer receives an instant payment, 57% make it their primary payout method, up from 39% in 2020 and continuing to rise. For transactional payroll workers specifically, that stickiness reaches 68%. Once the expectation of real-time access is set, it does not reset.\nThe competitive implications are direct. Payout speed is a platform selection criterion. Workers operating across multiple transactional platforms, which is increasingly common, allocate their time toward platforms that pay faster. A platform that pays on Friday competes at a disadvantage against a platform that pays Tuesday night.\nSolving for Employer Liquidity\nThere is a structural tension in the move toward on-demand pay that the business case for instant disbursements does not always address directly. Traditional biweekly payroll is not merely an administrative convention. For employers, it is a working capital tool. When wages are batched into a single disbursement every two weeks, the employer retains the use of that capital during the intervening period. Moving to on-demand disbursement replaces that predictable float with a continuous, variable draw on operating cash. Smaller platforms, staffing operators and hospitality groups may struggle to absorb that shift.\nThis is not a theoretical concern. It is one of the most consequential and underappreciated reasons that the adoption gap in instant payroll persists. Employers and platforms that want to offer real-time pay often find themselves caught between genuine worker demand and real treasury constraints. Solving for one without accounting for the other produces a half-measure that the market won\u2019t sustain.\nThe model gaining traction, and the one that unlocks widespread adoption without forcing employers to restructure their working capital management, is the earned wage access intermediary. In this architecture, a third-party provider advances earned wages to workers in real time, fronting the capital the employer has not yet disbursed. The employer continues to settle on its existing payroll schedule. Workers receive access to funds when work is completed. The timing gap is bridged, but the employer\u2019s cash flow cadence is preserved.\nRead More: From Perk to Necessity: On-Demand Pay Brings Predictability to Paychecks\nThis decoupling is the key structural insight. Workers get real-time access; employers retain their working capital rhythm; and the intermediary earns revenue through a combination of small per-transaction fees, subscription arrangements with platforms, or employer-funded access offered as a workplace benefit. In the employer-funded model, the cost is typically offset by measurable reductions in turnover, higher shift acceptance rates, and improved worker availability. These outcomes carry their own return on investment.\nThe model also resolves a credit and underwriting question that has historically slowed adoption. Because the capital being advanced represents wages already earned rather than credit extended against future income, the risk profile is fundamentally different from consumer lending. The intermediary is not underwriting the worker\u2019s ability to repay. It is bridging a timing gap on income that already exists, and recouping from the employer\u2019s scheduled payroll settlement. The advance is not a loan. It is an acceleration.\nWhat has lagged behind the model is integration. Earned wage access capabilities should be embedded directly into the payment infrastructure that platforms use to manage disbursements so that instant pay becomes a default feature rather than a separate product the worker must seek out. The platforms and financial institutions that achieve that integration, and offer it at the point where work is completed rather than as an add-on enrollment flow, will define what modern transactional payroll infrastructure looks like for the decade ahead. The employer liquidity problem is solvable. The business model to solve it already exists. What remains is execution.\nThe Real-Time Cash Flow Mindset\nThe behavioral shift among transactional workers isn\u2019t just about speed. It reflects a fundamentally different relationship between earning and spending, one in which income management is an active, continuous practice rather than a passive, periodic event.\nPYMNTS Intelligence research into how transactional payroll workers describe their own financial behavior reveals a pattern economists and financial planners would recognize as sophisticated real-time cash flow management.\nThese workers don\u2019t smooth income across a pay period. They match income to obligations directly, often on a same-day or next-hour basis. They\u2019ll describe choosing a particular shift because they need money tonight, or completing a specific delivery run because a bill is due in the morning.\nRead More: America\u2019s Workers Got Left Behind in the FinTech Boom\nThat mindset creates a set of product requirements that traditional banking and payroll infrastructure was not designed to meet. The worker managing cash flow in real time needs real-time visibility into earnings, real-time access to those earnings and financial tools that operate at the same cadence as their income.\nThe generational profile matters here. Workers who have grown up in this real-time income environment, primarily millennials and Gen Z, have not experienced traditional biweekly payroll as a default. For them, the expectation that income arrives when it is earned is not a request. It is an assumption.\nThat product requirement is now getting a serious answer from both banks and FinTechs. As I wrote at the start of the year, Buy Now, Pay Later is stepping in as an emerging working capital tool for the modern middle class, migrating out of its original discretionary-spending niche and into the everyday financial plumbing of households that live close to the edge of their checking accounts.\nRead More: BNPL\u2019s Next Act Is as Consumer Working Capital\nFor transactional workers managing variable income against fixed obligations, the fit is intuitive. BNPL\u2019s installment structure lets workers authorize a necessary expense today and match its repayment to income they know is coming, without incurring the penalty economics of an overdraft. It converts the timing gap from a crisis into a schedule.\nI see BNPL as a direct complement to the real-time payment rails that transactional payroll depends on. Instant disbursements get money into workers\u2019 hands faster; BNPL tools help them extend that liquidity intelligently across their obligations.\nThe Side Hustle as Financial Infrastructure\nThe phrase \u201cside hustle\u201d entered mainstream vocabulary as a way to describe supplemental income, something workers did in addition to a primary job. That framing no longer captures reality for a significant and growing share of the workforce.\nRecent PYMNTS Intelligence data, in collaboration with WorkWhile, shows that nearly one in five hourly workers now reports regular side work. And critically, the majority of that income is used not to build savings or fund discretionary spending, but to cover essentials: rent, groceries, utilities, transportation. What was once supplemental has become foundational.\nRead More: Wage to Wallet Index: Side Work Patterns in the Labor Economy\nThis has real consequences for how the financial system should think about risk, liquidity, and the definition of a customer. A worker who earns $42,000 a year across three platforms and a tipped hospitality job is not a gig worker in the colloquial, marginal sense.\nThat worker is running a complex, multi-channel income operation that requires the kind of financial tooling that used to be reserved for small business owners. And risk profiling and underwriting that reflects their financial reality.\nThe financial stress that characterizes this population is not primarily about the level of their earnings. These workers are not earning nothing. They are earning enough, or close to enough, but that income is arriving at the wrong time, in the wrong cadence, with the wrong access characteristics. The stress is liquidity stress, not income stress. It is the stress of having earned money that is not yet available.\nPYMNTS Intelligence research has found that the gap between salaried and hourly workers in terms of financial confidence and perceived mobility is driven less by income level than by access to liquidity tools.\nSalaried workers experience the economy as a set of expanding options because they have predictable, smoothed income that gives them a planning horizon. Hourly and transactional workers, even those earning comparable amounts on an annualized basis, experience it as a treadmill, because each pay gap is a potential crisis.\nLate fees, overdraft charges and clearing delays consume a disproportionate share of transactional workers\u2019 income. A $35 overdraft fee on a $200 paycheck is not a minor inconvenience. It is a 17.5% penalty on earned wages, imposed not because the worker is financially irresponsible but because the infrastructure around them doesn\u2019t sync with their reality.\nThis is precisely why I believe BNPL will become a structurally sound replacement for overdraft and late-fee dependency. Overdrafts are backward-looking: the penalty arrives after the worker has already stumbled. BNPL is forward-looking: the obligation is priced, scheduled and disclosed before the transaction is authorized. For transactional workers running their finances like a small business, that predictability will become a basic planning tool.\nWhat the System Owes the Transactional Worker\nThe business case for faster payments to transactional workers is well established.\nPlatforms that pay faster attract more workers, retain them longer and compete more effectively for labor supply. Workers who receive instant payouts are far more likely to continue using the same platform. Workers who are not managing acute financial stress make fewer avoidable decisions, fewer missed shifts, fewer last-minute cancellations. And workers who are paid in real time have more money to spend in the real economy, because they\u2019re not handing it over in late fees and penalties.\nRead More: Instant Payouts Pull Workers Toward Rival Platforms\nBut the business case, while important, is not the whole argument. There is something more fundamental at stake in how the financial system treats earnings that belong to workers who have already done the work.\nFor the transactional workforce, timing is not an administrative detail. It is the difference between financial stability and financial fragility. The same earnings, arriving one day sooner, produce materially different outcomes for a worker managing thin savings against fixed obligations. The technical capability to deliver those earnings in real time exists. The rails are built.\nThe question is whether the institutions and platforms that sit between the worker and their money will treat that delivery as a baseline expectation or a premium feature.\n\nThe woman managing five income streams before noon on a Tuesday is already functioning as her own CFO. What she needs from the financial system is a product stack that matches that sophistication. Instant disbursements on the earning side, and intelligent short-term liquidity tools on the spending side. BNPL, offered through the debit card tied to the checking account where her income lands, gives her a way to smooth obligations without incurring the blunt-force penalty of an overdraft.\nThose who bring these tools together, pairing real-time payroll with embedded installment options, will define what transactional banking looks like for the next decade.\nFor the banks and FinTechs that see around that corner, the upside is significant. Workers who experience real-time access to their earnings form durable relationships with the platforms and financial tools that provide that access.\nLoyalty, it turns out, moves at the speed of money, too.\n \nUntil NEXT time.\nJoin the 19,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post Welcome to the Transactional Economy appeared first on PYMNTS.com.", "date_published": "2026-03-04T07:00:48-05:00", "date_modified": "2026-03-03T15:07:24-05:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/03/2026-Web-Hero-Image-KLW-NEXT-1200x780-03032.jpg", "tags": [ "earned wage access", "gig economy", "instant payments", "Karen Webster", "KLW Commentary", "Labor Economy", "Main Feature", "News", "next newsletter", "payroll", "PYMNTS News", "transactional economy", "Payroll" ] }, { "id": "https://www.pymnts.com/?p=3509114", "url": "https://www.pymnts.com/consumer-insights/2026/the-three-blind-spots-in-how-consumer-sentiment-is-measured/", "title": "The Three Blind Spots in How Consumer Sentiment Is Measured", "content_html": "
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The post The Three Blind Spots in How Consumer Sentiment Is Measured appeared first on PYMNTS.com.
\n", "content_text": "In March 2020, government officials and epidemiologists were telling Americans to brace for a two-month shutdown. PYMNTS Intelligence went into the field and asked 1,923 consumers directly what they thought. They said five months. In May 2020, the experts said recovery would come by fall. Consumers said February 2021. In December 2020, when officials were projecting a return to normalcy by March 2021, consumers put it at January 2022. They were right every single time.\nAnd no, this wasn\u2019t luck. These weren\u2019t epidemiologists with access to better models. And they didn\u2019t have Dr. ChatGPT to help advise them.\nThey were ordinary people doing something that spreadsheets can\u2019t. They were factoring in their own fear, their changed habits, their distrust of public spaces and the fact that no one had given them a reason to go back. They knew where they were going to spend: online, away from the physical world. And they knew it was going to be for a lot longer than anyone in Washington or on Wall Street wanted to accept.\nMost of those habits stuck.\nThe inflation story has played out the same way. When the Fed and most economists were calling price pressures \u201ctransitory,\u201d PYMNTS Intelligence consumer surveys were already telling a different story. In late 2022, consumers predicted that inflation wouldn\u2019t return to pre-2021 levels until late 2024. And then pushed that forecast out further with each passing month of data. Again, they were closer to right.\nAnd then there are tax refunds.\nForecasters and retailers reliably predict that every spring consumers will use their refunds on discretionary purchases. A little treat, something nice. PYMNTS Intelligence data kept showing otherwise.\n\n\n\n If consumers have been right, repeatedly, ahead of schedule and across multiple economic cycles, why does conventional wisdom about consumer sentiment keep getting blindsided?\n\n\n\n\nAmong households living paycheck to paycheck and struggling to pay their bills, which represents about 68% of American households, roughly two-thirds of any refund goes straight to everyday expenses or debt repayment. About 16% save or invest it. That \u201ctreat yourself\u201d shopping spree doesn\u2019t happen for most American families.\nSo, here\u2019s the obvious question. If consumers have been right, repeatedly, ahead of schedule and across multiple economic cycles, why does conventional wisdom about consumer sentiment keep getting blindsided?\nThe answer may be that the instruments we use to measure sentiment were designed for an economy that no longer quite exists.\nThree Blind Spots in How We Measure Consumer Sentiment\nThe foundational index for measuring U.S. consumer sentiment goes back to 1946, when University of Michigan psychologist George Katona started surveying 500 consumers monthly about their economic outlook, not just their spending behavior. It was a real methodological breakthrough. For the first time, economists had a systematic way to track the mood behind the money movement. The University of Michigan Index of Consumer Sentiment has been doing that job ever since.\nBut the economy has changed in ways the original methodology couldn\u2019t have anticipated. Three structural gaps have opened up between what the classic measures capture and what\u2019s actually driving consumer behavior today.\nBlind Spot 1: The Damage From Inflation Was Permanent, Not Transitory\nEconomists will point to the data and say that inflation has cooled, wages have recovered, unemployment is low. On the surface, things look fine. So why do so many people still feel squeezed?\nBecause prices never went back down.\n\n\n\n When experts say inflation \u201cslows,\u201d that means goods are getting more expensive at a slower rate, not that they\u2019re getting cheaper.\n\n\n\n\nWhen experts say inflation \u201cslows,\u201d that means goods are getting more expensive at a slower rate, not that they\u2019re getting cheaper. Groceries, rent, utilities, and car insurance are \u00a0still dramatically more expensive than they were in 2020. A household that budgeted around 2019 prices, with no extra savings to cushion the blow or with wages that haven\u2019t kept pace, has absorbed years of cumulative damage that the current CPI reading glosses right over. CPI doesn\u2019t reflect the prices people actually remember paying.\nREAD MORE: Why Consumers Don\u2019t Care About Monthly CPI \u2013 and Why it Matters\nTraditional sentiment surveys ask consumers whether things are getting better or worse right now. They\u2019re not designed to capture how deep the hole already is after a multi-year price reset. A consumer whose grocery bill has climbed 25% over four years, and whose wages recovered slowly, isn\u2019t confused when they say they still feel financially stressed. They\u2019re accurately describing their situation. The survey instrument is the one missing the finer points.\nThis is why PYMNTS Intelligence data kept showing consumers pushing their inflation recovery timelines out further, even as pundits were declaring victory. Consumers weren\u2019t ignoring macroeconomic data. They were describing a lived experience that macroeconomic data simply wasn\u2019t built to capture.\nBlind Spot 2: Income Has Stopped Being a Reliable Predictor of Spending Behavior\nMost economic models are built on a sensible assumption: higher income means more spending. In a world where household balance sheets are stable and monthly obligations are modest relative to what people earn, that holds up.\nBut that relationship has weakened. And it\u2019s become far more conditional on liquidity, debt load and fixed monthly costs. All these things determine whether a paycheck actually has room in it, regardless of the number on the stub.\nIn December 2021, PYMNTS Intelligence found that 61% of Americans reported living paycheck to paycheck. By August 2025, that figure had climbed to 71%. What makes this more than a poverty story is who\u2019s inside that number. Nearly half of people earning over $100,000 a year now report the same condition, stretched thin by fixed obligations, debt service and the cost of maintaining a lifestyle that looked affordable before the inflation surge.\n\n\n\n It\u2019s the structure of someone\u2019s financial life that determines whether there\u2019s actually room for discretionary spending.\n\n\n\n\nIncome still matters, of course, but it\u2019s the structure of someone\u2019s financial life that determines whether there\u2019s actually room for discretionary spending. Whether their fixed obligations leave any margin. Whether an unexpected expense would mean going into debt.\nTwo people earning identical salaries will make completely different spending decisions if one has three months of savings and the other has none. The number on the pay stub looks the same. The behavioral response to any economic signal does not.\nThis also explains why the tax refund narrative keeps failing. For a household with a financial cushion, a $2,000 refund is a windfall, something to enjoy. For a paycheck-to-paycheck household, that same $2,000 is a partial offset against overdue bills and high-interest debt. Aggregate income models can\u2019t tell those two households apart. Their spending decisions couldn\u2019t be more different.\nBlind Spot 3: Employment and Job Security Are Two Entirely Different Things\nA low unemployment rate tells you what percentage of people who want jobs have them. It tells you almost nothing about how secure those people feel in the jobs they hold, whether they believe they could find comparable work if they were let go, or whether technology is quietly eroding the value of their skills in ways that haven\u2019t shown up yet in anyone\u2019s data.\nThat distinction matters enormously for how people spend.\nDecades of research consistently shows that people begin pulling back the moment income feels uncertain, often well before anything bad actually happens. It\u2019s not job loss that triggers spending contraction. It\u2019s the anticipation of possible job loss and the perceived difficulty of replacing it that does. By the time a drop in consumer spending shows up in sales data, the behavioral shift has often been underway for weeks or months.\n\n\n\n It\u2019s not job loss that triggers spending contraction. It’s the anticipation of possible job loss.\n\n\n\n\nThere\u2019s also a specific kind of worker who represents a real but underappreciated vulnerability here. Someone who feels personally secure in their current role, but who doesn\u2019t believe they could quickly find equivalent work if they had to. They\u2019re not spending freely. They\u2019re spending cautiously, keeping one eye on an exit they\u2019re not sure exists. A headline employment rate captures none of that.\nThe rise of AI in the workplace makes this more urgent.\nWorkers whose skills are adjacent to automation are experiencing something with no real precedent in postwar labor economics: a slow, uncertain degradation of occupational value that doesn\u2019t trigger unemployment benefits, doesn\u2019t show up in job-loss statistics and doesn\u2019t register on any existing confidence measure. But it\u2019s absolutely shaping how they think and spend.\nA New Instrument for a Different Economy\nThese three gaps are what drove the development of the PYMNTS Consumer Expectations Index, or PCEI.\nOn March 2, 2026, PYMNTS Intelligence will launch the PCEI, a monthly, census-balanced survey of more than 2,000 consumers designed to measure U.S. consumer sentiment in a way that\u2019s directly useful for business decision-making. PYMNTS Intelligence Senior Analyst Matt Albrecht has led this effort, bringing three years of experience overseeing the Florida Consumer Sentiment Index at the University of Florida\u2019s Bureau of Economic and Business Research.\nThe PCEI isn\u2019t meant to replace the University of Michigan Index or the Conference Board Consumer Confidence measure. Those instruments have decades of data behind them and serve real purposes. What the PCEI does is go deeper, and specifically on the structural constraints and risks that classic measures don\u2019t capture as directly.\nIt builds on traditional sentiment measures such as household finances, economic outlook and purchase climate while extending into the structural constraints that determine whether consumers can actually act on their confidence: debt manageability, savings capacity, emergency readiness and labor-market security. The index maps these across 11 dimensions on a 0 to 100 scale, with 50 as the neutral baseline.\nThe core idea is straightforward.\n\n\n\n Sentiment only drives behavior when households have room to act on it.\n\n\n\n\nSentiment only drives behavior when households have room to act on it. Optimism without financial capacity isn\u2019t really optimism, it\u2019s a feeling that won\u2019t translate into spending. The PCEI is designed to capture both how consumers feel and how free they actually are to follow through on those feelings.\nThe framework draws on more than six years of PYMNTS consumer research, spanning hundreds of thousands of respondents across dozens of studies. That body of work is where the pattern became undeniable. Consumers aren\u2019t confused about their own situations. They\u2019re accurately describing conditions that existing measures weren\u2019t built to detect.\nWhat the February Data Says\nThe headline number for February is 51.5, which is technically above the neutral reading of 50, technically pointing toward cautious optimism. Stop there, though, and you miss the real story.\nThe more revealing finding is where that aggregate number breaks apart. The spread between consumers who live paycheck to paycheck and those who don\u2019t is more than 10 points. The spread between the highest and lowest generational cohorts is about 7 points.\nIn a single data point, that comparison captures the whole thesis of the PCEI.\nGeneration explains how high you\u2019re sitting. Financial structure explains how far you can fall.\n\nThe job security numbers deserve a close look because they illustrate the blind spot perfectly. Workers score their personal job security at 83.5. which is solidly positive. They\u2019re not lying awake worrying about layoffs next month. But when asked how quickly they could find a new job at the same pay, that score drops to 48.0, just below neutral.\nSecure in place. Not confident about what happens if they need to move.\nToday\u2019s spending is holding up not because consumers feel genuinely resilient (a word that the media bandies around freely), it\u2019s holding up because they feel safe staying put. The moment that changes, the calculus shifts fast.\nThe takeaway isn\u2019t panic, but another measure of fragility. Consumers can feel secure enough to stay in place while still lacking the confidence to take discretionary risks. That\u2019s how a headline number can look stable while the underside remains tight.\nDebt confidence at 71.4 is the strongest reading in the index, and it\u2019s worth being precise about what that does and doesn\u2019t mean.\nConsumers feel they can manage what they owe, which is actually pretty good in an environment where credit card balances have been climbing steadily. But current financial conditions score only 51.5. People feel like they\u2019re keeping up with obligations. They\u2019re less sure they\u2019re actually getting ahead. That\u2019s stability through management, not through progress. And there\u2019s a ceiling on that kind of confidence.\nGenerationally, Millennials lead at 60.7, the most optimistic cohort by a meaningful margin. Baby Boomers and Seniors sit at 53.5, closest to neutral. But the more notable pattern is how synchronized the movement has been across all generations. Every cohort softened in November, rebounded in December, eased in January, and improved again in February. The same rhythm, at different altitudes.\nWhen Consumers Know Best\nFebruary\u2019s data describes a consumer who is holding on. They\u2019re managing debt, keeping their job, not panicking, but doing so carefully, selectively and with a clear eye on what could go wrong.\nTheir resilience is real. So is the shaky foundation underneath it. Something that has less to do with what people earn than with how their financial lives are structured.\nWhether they have a cushion. Whether their obligations leave room to maneuver. Whether one unexpected expense tips the balance.\n\n\n\n Consumers feel the constraints in real time.\n\n\n\n\nConsumers told us the pandemic would last longer than the scientists projected. They told us inflation wouldn\u2019t vanish on the Fed\u2019s timeline. They told us the tax refund wouldn\u2019t become a shopping spree. They were right every time. Not because they\u2019re better forecasters, but because they feel the constraints in real time.\nWhen permanent structural shifts and short-term policy uncertainty are reshaping how people think and spend at the same time, asking only how the consumer is \u201cfeeling\u201d may not be the most useful question.\nThe more practical one is how well the consumer is positioned to act on those feelings.\nThe data suggests consumers have understood their own situation clearly all along. The question has always been whether the tools used to measure them were designed to actually listen.\n \nUntil NEXT time.\nJoin the 19,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post The Three Blind Spots in How Consumer Sentiment Is Measured appeared first on PYMNTS.com.", "date_published": "2026-02-26T07:00:48-05:00", "date_modified": "2026-02-25T22:11:43-05:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/02/hero-image.png", "tags": [ "consumer finance", "consumer ingishts", "Consumer Spending", "economy", "Index of Consumer Sentiment", "Karen Webster", "KLW Commentary", "Main Feature", "News", "PCEI", "PYMNTS Consumer Expectations Index", "PYMNTS News", "Consumer Insights" ] }, { "id": "https://www.pymnts.com/?p=3486820", "url": "https://www.pymnts.com/artificial-intelligence-2/2026/what-happens-to-stores-when-ai-agents-do-the-shopping/", "title": "What Happens to Stores When AI Agents Do the Shopping?", "content_html": "
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The post What Happens to Stores When AI Agents Do the Shopping? appeared first on PYMNTS.com.
\n", "content_text": "Fifty years ago, Susan was one of more than a hundred million Americans who drove to a shopping mall an average of once a week to buy things. By 1976, a third of all retail sales happened at the mall.\nSusan shopped exactly the way the mall designers intended. The department store anchors were the magnets motivating her to make the trip in the first place. The smaller shops lining the corridors between them turned her walk from point A to point B into a series of serendipitous purchases that kept the whole ecosystem humming, including a stop or two at the food court to grab lunch or dinner.\nFifty years before that, Susan\u2019s grandmom was one of the millions who found the lure of the department store irresistible. A New York Times article published on February 2, 1927 cited Federal Reserve data showing department store sales setting new retail records in 1926. (If you do happen to click on the article link, check out the ad for life insurance on the same page.)\nFor the first time, curated retail items could be found under one roof for granny to touch, feel and try on. Knowledgeable salespeople demonstrated products and answered questions, turning store browsers into buyers. Stores offered credit to make buying easier, and retail sales took off. And like shopping at the mall, going to the department store was an experience that was part social, part shopping, but mostly lots of fun.\nJulie Satow’s book When Women Ran Fifth Avenue\u00a0documents the role department stores played in shaping commerce and influencing fashion in their heyday. It’s a great read.\nToday, Susan’s granddaughter, Ellie, starts her shopping trip with a scroll.\nHer smartphone and apps give her access to the equivalent of every mall and every department store ever built with a tap and a swipe. PYMNTS Intelligence data shows that nearly two-thirds of American consumers start their shopping trips that way, making purchases 12 days each month on average and window shopping for another 12. For retailers, this is pretty good news. Mobile window shoppers convert to a purchase at a rate three times higher than the casual mobile phone user. Younger consumers and parents skew that number even higher.\nRead More: The 2025 Global Digital Shopping Index: The Rise of the Mobile Window Shopper and What It Means for Payments\nThe digital world gives Ellie tons of options to buy whatever strikes her fancy without ever visiting a store. She has certainty about product quality and inventory availability even if she decides to make the trip just to see the item for herself. If she doesn\u2019t, the logistics of getting her items delivered are transparent, predictable and efficient.\nThe latest PYMNTS Intelligence data finds that 48% of clothing and accessories, 61% of electronics purchases and 75% of sporting goods and hobby purchases made online now happen at Amazon. Overall, almost two in every ten retail purchases now starts and ends online.\nRead More: Walmart Aims at Closing Amazon Online Sales Gap\nBut Ellie’s generation may be the last to make even that digital journey themselves.\nDo Consumers Still Want to Shop at a Store?\nWarren Buffett said in a 1977 Wall Street Journal interview that the best business to own is a toll bridge. His thesis: Once capital is invested to build one, you can keep collecting revenue and raising prices since you control access to the places people want to go.\nIf you can\u2019t be the destination and monetize it, the next best thing is to own the metaphorical bridge everyone has to cross to get to one. Platforms are a great example of the art and science of being a massively successful toll booth. Or a complete disaster, if you get the platform economics wrong.\nBuffett\u2019s aphorism is particularly appropriate to shopping in an age of AI agents.\nThe retail industry is in the throes of a heated debate about whether to open storefronts to AI models so that consumers (or their agents) who start their product journeys there can find their merchandise and buy. That\u2019s the OpenAI, Stripe, Adyen, Fiserv, Perplexity, Shopify, Google, PayPal and Walmart thesis.\nRead More: Department Stores of the Future Are AI Agents\nThe other side of the argument: if you\u2019re already the destination, what\u2019s the point of the bridge? If consumers already come to you to search, discover, compare and buy, then connecting to someone else\u2019s toll bridge is not in your best interest. As the destination, you\u2019re the bridge and the toll booth all wrapped up into one. That\u2019s the Amazon and eBay thesis. (Amazon, I get. eBay is the ultimate head-scratcher, if you ask me.)\nBut there\u2019s a more fundamental question at the heart of this issue even before getting to the strategic question of open versus closed.\nIn a world where agents can buy, do consumers still want to shop at a store? \nWill Ellie, her kids or her grandkids need to start at \u201cthe store\u201d in order to gather product information, read reviews, evaluate options and decide what, when and whether to buy so that they will have confidence the order will be right and show up on time? If so, consumers won\u2019t be willing to fully delegate to an agent because too much can go wrong.\nOr will shopping, as we know it, shift from stores and apps to a collection of hundreds of billions of digital SKUs that agents shop and buy on a consumer’s behalf? In that scenario, consumers say shopping across endless aisles and multiple drives to physical stores is too much of a hassle. Agents can do it smarter and more efficiently.\nThis turns out to be the $5.5 trillion question for retail in the U.S.\nBecause if AI agents do the shopping, nobody (or many millions of nobodies) will \u201cgo\u201d anywhere. Instead, shopping\u2019s theoretical bridge leads to another bridge and another bridge and another with their own tollbooths.\nThe destination that was once the store becomes irrelevant because it becomes invisible. The new destination becomes the prompt and the agent dispatched to do the shopping and buying. The bridges and the tollbooths connect to and from this new agentic storefront.\nThe Shift That\u2019s Already Here, Sort Of \nThe data says this isn\u2019t so much of a theoretical debate anymore.\nPYMNTS Intelligence research shows that more than six in ten U.S. consumers used AI in the past year to do something. More than a third of Gen Z consumers and power users now start their daily tasks on dedicated AI platforms first, including content discovery. And not in addition to Google search, but as a replacement.\nAs of January 2026, not only have 41% of consumers used dedicated AI platforms for product discovery, but 33% say they have fully replaced their prior methods. They\u2019re not layering AI on top of old habits. They\u2019re shutting the door and leaving them behind.\nRead More: Smart Agents Replace Super Apps\nThis behavior is more predominant among the early adopters who are all in on AI and agents, with 51% having replaced their old methods of product discovery. True to their early adopter roots, they are willing to tolerate untold friction to try something new. Among AI power users, the 24 million Americans who use AI and agents to do everything from building shopping lists to doing research on what stocks to buy, the share who reported replacing their previous search and discovery methods continues to increase since November 2025, when it stood at 46%.\nFor them, the front door of commerce is already in motion.\nBut here\u2019s the caveat.\nThe growth is being driven overwhelmingly by use cases that are not yet letting agents make high-stakes, complex purchasing decisions on their behalf.\nIn part that’s because the inventory of products and merchants to shop remains nascent. In part it’s because the experience is largely circa the early days of internet commerce: functional, but not exactly one-click amazing. And mostly it’s because consumers still have to trust that their purchase discovery and outcomes are as good as the content discovery and outcomes when the starting point is the physical or virtual store.\nFor the moment, the interest in doing these things is higher than the reality of actually doing them.\nAnd yet about 82% of these power users, who are the most likely to have replaced their old discovery methods, say they would use AI agents for big, complex purchases where the stakes are high and getting it wrong has financial consequences. These are not impulse or everyday buys. They are the high-consideration, high-research decisions that used to take hours, even days or weeks. And where a lot of spend hangs in the balance.\nThat\u2019s consumer intent at the very leading edge of AI and agents. And it\u2019s pointed directly at the heart of how retail works today.\nWhy Shopping is Not a One-Size Fits All Experience \nThe mistake in the current debate about agents versus stores is treating \u201cshopping\u201d as a one-size fits all activity. It isn\u2019t. There are several distinct reasons for how, why, when and where people buy. And the relevance of AI agents will vary tremendously based on those buying triggers.\nTake replenishment, AKA subscriptions.\nThe dog food. Laundry detergent. Paper towels. Toothpaste. The purchases where the consumer made the real decision once, maybe even years ago, and has been on autopilot ever since. There’s no discovery here. No joy. Just the mild annoyance of remembering to place an order before it runs out. And the catastrophe of running out when they forget something important.\nThis is where an AI agent doesn’t just help, it can take over entirely and reinvent the subscription experience along the way.\nNo more boxes of paper towels that pile up in the basement because someone in the household forgot to pause an order. Instead, an agent can notice and nudge.\n“It’s been six weeks since you ordered dog food. Time to reorder?” “You haven’t refreshed your white short-sleeve t-shirts for the summer. Want the same ones you bought last year, or take a look at the five most popular styles in your size?” “Time for new flip flops. Same pair as last June, or want to see what’s out there?”\nIn truth, consumers don’t want to spend their time shopping for these things. They want the buying process to disappear. That\u2019s what the agent can do, with a prompt and one-tap confirmation.\nRead More: Why 30 Million US Consumers No Longer Search\nAmazon already does a version of this with Subscribe & Save, which I live by. Alexa+ attempts to take it further by adding context, knowing that summer is coming, that the dog food bag lasts roughly this many weeks, that last year’s flip flops ran a half-size too small because of a return and a reorder.\nEarly access data from Amazon on Alexa+ finds that users tripled their shopping activity and had two to three times more conversations compared to the original Alexa. Amazon made Alexa+ fully available to all U.S. users this month (February 2026), and it’s free for its 250 million Prime members. That’s a lot of people who already live with an agent on their kitchen counter or inside an Amazon app.\n\nWalmart’s play with its One Pay banking, credit, shopping and rewards app tries to capture a piece of this layer by turning the grocery trip into a financial flywheel that expands into replenishment. It remains an aspirational goal. Walmart\u2019s ecommerce business has seen strong growth over the last year (from 16.4% to 19.9%), with much of that growth coming from groceries, which drive nearly 60% of their sales. Its Subscribe and Save service, which launched in 2023, is positioned as a counter to Amazon\u2019s in capturing recurring sales for groceries and essentials.\nIn an agentic world, whoever owns this layer owns the most frequent, most predictable and most invisible transactions in a consumer’s life.\nThen there are the bigger-ticket, once-every-so-often, more considered purchases. \nA new camping tent. A stroller. A laptop. A dishwasher. A new car. These are the decisions where people currently spend hours reading reviews, comparing specs, toggling between browser tabs and going back and forth over whether the extra hundred dollars is worth it. And this is exactly the layer where the PYMNTS Intelligence data gets most interesting. It\u2019s precisely these complex, high-stakes decisions where consumers are most eager to hand the tedious task of evaluation those options to AI.\nRead More: From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce\nIt makes sense. Typing in (or speaking) a detailed prompt is just easier than scrolling through forty-seven open tabs. A consumer can describe what they need in plain language and the agent does in seconds what used to eat up an entire afternoon or more.\nAmazon’s Rufus is the most visible example of what this looks like inside a closed ecosystem. The numbers Amazon reports tell you why Amazon is pushing so hard on it.\nThey report that some 250 million shoppers used Rufus in 2025, with monthly active users growing 140% year over year. Amazon says it’s on pace to drive more than $10 billion in incremental annualized sales with it. Rufus users are 60% more likely to complete a purchase than non-Rufus shoppers. During Black Friday 2025, sessions involving Rufus that ended in a purchase doubled compared to the trailing thirty-day average, while non-AI sessions grew just 20%, they say. That doesn\u2019t feel like a marginal improvement. It suggests that a fundamentally different shopping behavior is happening inside its ecosystem.\nThen there are the LLMs such as ChatGPT, Claude, Gemini that want to be the destination where that journey starts and ends, routing consumers to the right merchant with the right product at the right price. It\u2019s also where the strategic tension begins to get, well, a little tense.\nRead More: Why the \u2018Person\u2019 of the Year in 2025 Should Be the Chatbot\nAmazon can end the shopping journey because it owns what I’d call logistics certainty. A consumer knows when the product is arriving: same hour, same day, next day. They know what the shipping costs are: mostly free with Amazon Prime, which is a bonus and eliminates the uncertainty of not knowing the final cost. A consumer knows how to return an item if there\u2019s a problem. And where to do it for free. The entire post-decision experience is part of their destination\u2019s appeal.\nPerhaps one of the most underrated variables in the entire agentic commerce debate: What exactly happens once an agent clicks \u201cbuy.\u201d\nThen there are the highly complex, product/service blended purchases that support some of life\u2019s biggest moments. \nThe 25th anniversary trip. Having a baby. Sending a kid to college. Buying a first house. Getting a puppy. Planning a wedding. Moving to a new city. Each of these involves dozens of purchases, but none of them is really about filling a cart. They are about making a life passage happen.\nIt\u2019s also where the question of whether consumers still want to go to stores gets interesting. The answer might be yes, but in a different way than it happens today.\nNobody wants to spend a week researching car seats, strollers, nursery furniture, baby monitors, bottle warmers and the thirty-five other things your friends, relatives and in-laws insist you absolutely must have before the baby arrives. Then there\u2019s the physical infrastructure necessary to support the baby. The diaper service. The pediatrician. The day care. The preschool. It\u2019s exhausting, often contradictory and largely anxiety-producing.\nThe agent’s role in these moments isn’t to shop for those parents. It’s to be the smart and efficient concierge that helps to simplify the massive complexity around this important life moment.\nToday preparing for the new baby (or any of life\u2019s biggest moments) looks like parallel processing: researching across endless tabs, juggling competing recommendations, stitching together a plan from fragments.\nTomorrow it might look like this.\nCongratulations! You’re having a baby! Here’s a curated registry across eight retailers based on your budget, your house or apartment size, and what parents with similar lifestyles actually used and loved. Here are the retailers that have most of these items in stock if you want to go in and check them out IRL. Here are the three pediatricians near you accepting new patients with strong reviews. Here’s a timeline of what to buy to avoid the panic-ordering purchase of any car seat in stock at 2 a.m. three days before the due date.\nThe agent handles the things that take time and create frustration. The hassle of research, comparison and logistics coordination is stripped away, and what’s left is the part that actually matters to people. The choosing, experiencing and satisfaction of making a good decision in the context of something very important about to happen.\nWho Plays Where and Why It Matters\nSo, who owns the toll bridge and who becomes the destination? That depends on which version of shopping you’re talking about. And the four most consequential players in this space, Amazon, Google, Walmart and the LLM platforms, are each building from very different starting positions.\nAmazon: The Everything Store wants to become the Everything Concierge.\nAmazon pulls in roughly 2.5 billion visits a month to its site, commands roughly 9.1% of all U.S. retail spending according to the latest PYMNTS Intelligence report, serves more than 300 million active customers, with 250 million-plus Prime members locked into an ecosystem that has become the defacto starting point for product purchases. Amazon reported Q4 2025 revenue hit $213 billion, up 14% year over year. These are not the numbers of a company that needs to reinvent itself. These are the numbers of a company that can afford to make big bets from a position of massive retail strength.\nThat appears to be what Amazon is doing.\n\nSubscribe & Save handles replenishment. Alexa+ is hoping to extend that by contextualizing it, learning the consumer\u2019s purchase velocity, anticipating what they need before they think of it and eventually ordering ahead of time with its auto-buy feature.\nFor more considered purchases, Amazon has built the perhaps most complete environment in retail: search, reviews, product information, price comparison, and then the killer, logistics certainty, all in a one-stop shop. Consumers start and end the journey on Amazon and reliably know that the product will arrive when promised. Rufus and Help Me Decide don’t replace the shopping journey, but instead compress it.\nAnd at a nearly 10% advertising conversion rate, roughly five times Google Shopping’s rate, the math for brands selling on Amazon is hard to argue with.\nAmazon’s bigger bet with Alexa+ is to push beyond shopping and into life. The ambition is not to be just a shopping assistant but a life operating system.\nAlexa+ already integrates with Uber, Grubhub, Ticketmaster, Vagaro for spa and fitness, Thumbtack for home services, Square for merchant services, Expedia for travel, Yelp for local discovery, and Amazon Autos for car sales. Amazon’s Panos Panay has described a vision where Alexa becomes the consumer\u2019s personal shopper, butler and home manager. The more she understands about the consumer\u2019s life, the better she can serve the customer.\nAdd Amazon’s healthcare play through One Medical and pharmacy, its grocery infrastructure through Whole Foods, Fresh, and the Uber Eats delivery partnership, Prime Video for entertainment, and Alexa embedded in hundreds of millions of homes, and you start to see the outlines of a company that could plausibly become the life concierge, not just the destination as the store. What we once called the Super App.\nHere’s the catch. Amazon may make more trips to my home in a week than I\u2019d like to admit, but Alexa+ still has a long way to go, at least in my experience, before she can live up to the claim of a personal assistant. She is what most consumers say they\u2019d trust, according to PYMNTS Intelligence data. For me, it\u2019s a crap shoot as to whether she can reliably turn on Bloomberg TV in the morning on my Fire TV, never mind organizing my day-to-day.\nThe conundrum is that Amazon is trusted today for efficiency, reliability and price. But no one is thinking of Amazon curating their wedding registry. The Everything Store may struggle to become the Everything Concierge precisely because life moments require warmth and judgment that an efficiency machine may not inspire. And Alexa+ hasn\u2019t proven she can deliver.\nThen there’s Google, which is playing a fundamentally different game.\nGoogle is a massive advertising machine that generated more $400 billion in revenue in 2025, with advertising driving the lion\u2019s share of those numbers. Google says its Shopping Ads drive 76% of all retail search ad spending. Google is, by a wide margin, the world’s largest digital advertising platform.\nBut Google’s position in commerce is that it is really nowhere. It\u2019s always been the bridge, never the destination. Consumers search on Google, discover products, and then leave to buy somewhere else. Google gets paid for the referral.\nThat’s what Google is trying to change.\nIn January 2026, CEO Sundar Pichai unveiled the Universal Commerce Protocol at NRF, an open standard designed to let AI agents navigate the full shopping journey from discovery through checkout, all within Google’s own ecosystem. Google co-developed UCP with Shopify, Etsy, Wayfair, Target and Walmart, and got endorsements from Visa, Mastercard, Stripe, American Express and Best Buy. The message was clear.\nGoogle wants to stop being the bridge and become the destination.\nRead More: The Protocol Power Struggle Reshaping AI-Driven Commerce\nThis announcement came shortly after Google rolled out agentic checkout in November 2025, letting users set a target price and authorize Google to auto-purchase via Google Pay when the price drops. Its Shopping Graph now indexes more than 50 billion product listings, with 2 billion updated every hour. The new Business Agent feature lets retailers like Lowe’s, Michaels, Poshmark, and Reebok deploy branded AI assistants directly inside Google Search to chat with shoppers and close sales. Direct Offers, a new ad pilot, offers exclusive discounts to high-intent shoppers in AI Mode.\n\nIt’s nothing if not ambitious. But Google’s challenge remains closing the gap between intent and conversion by turning itself into the internet\u2019s marketplace.\nGoogle Shopping Ads convert at roughly 1.91%. Amazon’s marketplace converts at nearly 10%. That’s a five-to-one ratio, and it tells you everything about the difference between a platform where people go to browse and a platform where people go to buy.\nOpen standards and broad retailer partnerships are a compelling pitch, but until more shoppers are completing purchases inside Google rather than clicking away to finish somewhere else, the toll bridge metaphor still holds. And merchants need to consider how much, and when, to put effort into exposing their entire product catalog to Google, without a clear understanding of how it plans to monetize those sales. And who owns the customer relationship. More on that point later.\nWalmart is the wildcard with one truly irreplaceable asset: its physical storefront.\nRoughly 100 million people walk into Walmart for the most frequent, most habitual, most non-discretionary purchase in retail: their groceries. That foot traffic is massive. But it’s also their greatest Achilles’ Heel.\nFew people who go to Walmart for groceries buy anything else. The foot traffic is enormous, but their slice of the retail basket is narrow. PYMNTS Intelligence data shows Walmart\u2019s share of retail declining in nearly every category except food.\nWalmart’s AI strategy seems two-fold. With its AI partnerships, Walmart appears comfortable being a destination where the LLM toll bridges send traffic because its physical infrastructure is something no AI platform can replicate. And digital is where they lack meaningful share.\nPhysically, the One Pay credit and rewards play is an attempt to expand what the in-store shopper buys \u2014 to turn a grocery trip into a broader financial relationship. If you are already in the store buying food and the Walmart app knows you need new school supplies for your 10-year old, \u00a0it can offer you credit and rewards to buy them right then, and might expand the basket. It\u2019s a theory, for now.\nLike Amazon, the challenge for Walmart is in playing the life concierge role.\nWalmart is not a lifestyle brand. It is not where a consumer goes to curate their baby registry or furnish their first apartment or plan their wedding. It is not a Super App. \u00a0Unless Walmart can leap from “where I buy groceries” to “where my Walmart agent manages my life,” it remains powerful but confined to a single, if highly defensible, position as the world\u2019s biggest grocery store. With a little retail eCommerce on the side.\nAnd then there are the LLM platforms that are building the toll bridges that want to own the road. And become the destination. \nConsumers are increasingly starting their discovery journeys on these platforms, and the agents can route them to any merchant. That brings with it enormous disintermediation power. The PYMNTS Intelligence data confirms it. Consumers are replacing traditional search and discovery methods with AI-first approaches, and the replacement rate is accelerating.\nAdobe reported that AI-driven traffic to retail sites surged 693% during the 2025 holiday season, and shoppers arriving from AI services were 38% more likely to convert. This sounds amazing. But transaction volume is miniscule; this is still very early days.\nOpenAI has already launched its own Instant Checkout feature. Microsoft Copilot is partnering with Shopify for embedded checkout. Perplexity was the first to launch one-click checkout within its app. Everyone wants in on commerce.\n\nBut the LLMs have a structural vulnerability that the current hype obscures. They have no logistics. They can send you to a store, but they cannot guarantee when the product arrives, what shipping costs, or how returns work. Every merchant on the other side of their toll bridge is a different experience with a different uncertainty profile. And the selection of those merchants is pretty limited right now.\nBeing discoverable by an agent does not solve the fulfillment gap. And that is why Amazon’s position is structurally stronger than the LLM toll bridge position, at least for now. Amazon is the one destination where the post-agent experience is already solved.\nMy sense is that the LLMs’ real aspiration is the life concierge.\nNot to sell you things, but to be the agent that manages complexity across your entire life.\nGetting a puppy? It finds the vet, orders the food, finds and books the trainer and the dog walker,\u00a0 schedules the groomer, arranges for doggie daycare and the transportation to and from. The LLM becomes the relationship layer. Merchants, including Amazon, become the inventory.\nTheir advantage is being platform agnostic, smart and a massive time saver. The disadvantage is that a concierge without logistics and without fulfillment is ultimately dependent on others to deliver. And without access to all of the right products, creates buyer uncertainty.\nIn a world where the uncertainty tax on fulfillment determines which agent recommendation the consumer actually trusts, that dependency may be the LLMs’ defining limitation.\nAnd \u00a0why \u201cjust open your storefront to the AI agents\u201d is not the simple answer it appears to be for merchants.\nWho Owns the Relationship When No One Goes Shopping Anymore?\nLet\u2019s close with how we started. The destination and bridge/toll booth metaphor assumed a world with fixed destinations and fixed paths to get to and from. The destination was valuable because there were a finite number of destinations, and lots of \u00a0people wanted to get there. There were only a few ways to \u00a0get in and out. The bridge was valuable because it made the trip possible. And people and businesses paid the tolls.\nThe agentic world makes the destination and the bridges with tollbooths pretty fluid. For the replenishment shopping example, the destination is invisible, handled by an agent on autopilot that noticed a consumer was running low before they did.\nFor considered purchases, the destination is whoever collapses the research journey fastest while maintaining the certainty that the product will arrive when promised and can be returned if it\u2019s wrong.\nFor life moments, the destination is wherever the people feel a connection and a sense of trust to start the conversation.\nThe real strategic question comes down to how consumers view the shopping experience for each of those use cases.\nDo they want an agent that makes shopping disappear? Or do they want an agent that makes the shopping experience at a store they know and trust \u00a0better?\nThe answer, almost certainly, is both. The company that figures out how to deliver both will own the most valuable thing in commerce.\nNot the product, not the price, not the storefront, and not the toll bridge.\nThe relationship. The real starting point of any shopping journey.\n \nUntil NEXT time.\nJoin the 19,000 subscribers who\u2019ve already said yes to what\u2019s NEXT.\n\nPYMNTS CEO\u00a0Karen Webster\u00a0is one of the world\u2019s leading experts in payments innovation and the digital economy, advising multinational companies and sitting on boards of emerging AI, healthtech and real-time payments firms, including a non-executive director on the\u00a0Sezzle\u00a0board, a publicly traded BNPL provider.\nShe founded\u00a0PYMNTS.com\u00a0in 2009, a top media platform covering innovation in payments, commerce and the digital economy. Webster is also the author of the NEXT newsletter and a co-founder of Market Platform Dynamics, specializing in driving and monetizing innovation across industries.\n\r\n\r\nThe post What Happens to Stores When AI Agents Do the Shopping? appeared first on PYMNTS.com.", "date_published": "2026-02-18T07:00:31-05:00", "date_modified": "2026-02-19T13:26:14-05:00", "authors": [ { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" } ], "author": { "name": "Karen Webster", "url": "https://www.pymnts.com/author/karen-webster/", "avatar": "https://secure.gravatar.com/avatar/82cde720c07270077111addc8197f8d7eecb8bea90d0cdc2a6bad5576898483d?s=512&d=blank&r=g" }, "image": "https://www.pymnts.com/wp-content/uploads/2026/02/2026-Web-Hero-Image-KLW-NEXT-1200x780-02172.jpg", "tags": [ "Agentic AI", "Amazon", "artificial intelligence", "Google", "Karen Webster", "KLW Commentary", "Main Feature", "News", "OpenAI", "PYMNTS News", "Retail", "subscriptions", "The Prompt Economy", "walmart", "artificial intelligence" ] } ] }