{"id":21749,"date":"2026-06-29T13:05:45","date_gmt":"2026-06-29T10:05:45","guid":{"rendered":"https:\/\/quicklizard.com\/?p=21749"},"modified":"2026-09-30T06:59:15","modified_gmt":"2026-09-30T03:59:15","slug":"pricecheck-podcast-jenseits-des-kaufen-buttons","status":"publish","type":"post","link":"https:\/\/quicklizard.com\/de\/blog\/pricecheck-podcast-beyond-the-buy-button\/","title":{"rendered":"PriceCheck-Podcast: Mehr als nur der \u201eKaufen\u201c-Button"},"content":{"rendered":"<p class=\"wp-block-paragraph\">What happens when AI stops being a tool and starts becoming a participant in commerce?<\/p>\n\n<p class=\"wp-block-paragraph\">In the debut episode of <strong>PriceCheck<\/strong>, Dr. Fabian Uhrich, CPO at Quicklizard, pricing expert, former BCG Partner, and business angel, joins Yedidya Schwartz, CTO at Quicklizard, to explore how AI is transforming retail.<\/p>\n\n<p class=\"wp-block-paragraph\">From agentic commerce and pricing intelligence to customer ownership and real-time decision-making, they unpack the challenges and opportunities retailers face in an AI-driven world.<\/p>\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe title=\"[Podcast] Price Check #1: Beyond the buy button\" width=\"800\" height=\"450\" src=\"https:\/\/www.youtube.com\/embed\/4Dj3eBF51aE?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n<details class=\"ql-transcript\"><summary>Transcript<\/summary>\n<div class=\"ql-transcript-body\">\n<p>Check the pricing podcast powered by Quick Lizard. &gt;&gt; Hey, welcome to the first episode of the Quick Lizzard podcast. I&#039;mid CTO at Quick Lizzard and I&#039;m joined today by Dr. Fabian Enri. In this series, we are going to explore the structural shift reshaping retail. Not just trends or headlines, but the deeper operating model changes that leaders need to understand. Today, we are starting with the big one. What happens to retail when AI stops being a tool and starts becoming a participant? Because that&#039;s where we are. Commerce is entering what we call the agent era where algorithms don&#039;t just recommend products but increasingly discover, curate, negotiate and even execute purchasing decisions. And that changes everything. It changes how products are discovered. It changes how pricing works. It changes who owns the customer relationships.<\/p>\n<p>and it changes what kind of infrastructure retailers needs in order to compete. So today we are going to unpack why AI is simultaneously the biggest challenge and the biggest opportunity retail has seen in decades and what kind of infrastructure mindset retailers need next. Fabian, from a pricing and retail strategy perspective, what breaks first? Well, Yidia, thanks for for for the good introduction. Uh, retailers typically talk about head and tail products. Head products or top sellers rotate fast on the shelves and contribute over proportionately to sales while the longtail items are sold less frequently, often to an extent that the top 20% of products contribute to 80% of revenues while the other 80% of the tail products only do the remaining 20%. respectively. This also is affecting how category and merge teams allocate their time.<\/p>\n<p>Head SKs are typically actively managed on a daily or weekly base while the tail items are best receive like a periodic review every now and then assuming that they are stable over the time. Anyways, now aentic discovery breaks that assumption. Any skew can trend short-term be it due to social media hype or very specific algorithmic surfacing. Just to give you an example, a month ago, I was using AI to research an espresso machine for my camper van that works with gas stove and is made out of stainless steel, so no plastics or aluminum to be in contact with my proper and nice coffee. And I found a perfect match for me, a device and brand I never heard of before, literally coming from a rocket scientist, a product I would not have found in any shelf.<\/p>\n<p>And I think that&#039;s the best example of longtail skews that suddenly surface due to very specific requirements and not due to how the retailers curate the shelves uh organize the visibility or spend marketing budget. So this means volatility spreads across the entire assortment and forecasting and manual pricing cycles become just too slow to handle that. &gt;&gt; Yeah. So I think the important point here is that this is structural uh not seasonal variability. Retailers used to operate with a certain hierarchy in mind. You had the head skews, the products, the items that received active attention and then you had the long tail which was reviewed more periodically periodically. But algorithms do not operate according to that hierarchy. They surface products based on signals, relevance, availability, price, social momentum, how complete the product data is, and many other factors.<\/p>\n<p>So a skew that was invisible yesterday can suddenly become visible today. And when that happens, speed is no longer just operational. It becomes strategic. So if volatility is now distributed across the entire catalog, what does that do to how retailers allocate control and attention? This has indeed various implications. First of all, and I said before, it&#039;s not enough anymore to focus on the top sellers. Pricing needs to be much more granular, deveraged, and nuanced. But second, behavioral psychological pricing designed for human browsing, price perception is losing its power. More and more charm pricing, price anchor, strike through discounts of psychological price steps, the magic numbers, all this becomes less effective when an agent instead of a human is evaluating the offer. Exactly. Agents don&#039;t receive, they pulse.<\/p>\n<p>A human shopper may respond to charm pricing and crows or strike through discounts because those tactics shape perception. But an AI agent evaluates the offer. It looks at structured criteria, total cost, shipping speed, availability, data completeness, return conditions, and the overall reliability of the offer of the offer. So the shift is from persuation to comparability. Structural competitiveness becomes much more important than visual framing. Buen still matters but it matters farther upstream. When the agent is making the comparison the offer has to be clear, complete and machine readable 100%. This means a shift from persuasion to comparability. not the retailer trying to convince the shopper about the top shelf product. The other way around, the shopper eventually finding what he really needs and what he really wants, even if it&#039;s at the very back of the shelf.<\/p>\n<p>And therefore, structural competitive matters much more than visual framing. Having the right products with the right prices according to the features and as you said, brand still matters, but more in the upstream. So if persuasion loses power at the decision layer, what does that require from the underlying commerce The customer ownership question also changes. Traditionally, retail was shaped by the relationship between the retailer, the brand and the customer. But now we have a third actor entering the acquation agent and platform intermediators. That means discovery may not begin inside the retailer&#039;s own ecosystem anymore. It may begin in a chart interface, a search experience, a recommendation engine, or an AI shopping assistant. So, retailers are no longer only competing for human attention. They are competing for algorithmic selection. And that changes what the offer has to be.<\/p>\n<p>It cannot only be attractive to a person. It also has to be understandable to a machine. The product data has to be structured. The price has to be accurate. Availability has to be reliable and the offer has to be easy for an agent to evaluate. So if discovery, pricing and ownership are mediated, what replaces the traditional retail retail playbook? &gt;&gt; Actually, this is where AI is not only the problem but also the solution. Most retailers actively optimize 5 to 20% of the SKUs, but do blanket pricing across the rest of the assortment. Human decision-m is not scalable, but volatility now touches the full catalog. To win means scaling the rigor applied to the top sellers or the smartness or the experience put into that and roll it out and scale it up for the rest of the &gt;&gt; All right. And and this is where the answer is not simply more analysis.<\/p>\n<p>The issue is not that retailers retailers lack expertise. The issue is that human expertise does not scale across the full assortment at the speed this new environment requires. So the opportunity is to build systems that actually scale. That&#039;s expertise. Human still needs to define the strategy, the constraints, the priorities and the guardrails. But the system needs to execute that logic continuously across the entire catalog. So at what point does human judgment stop being an advantage and start becoming a bottleneck? So human judgment of course should always be at the center of pricing from defining the strategy to revealing the outliers and exceptions. But in order to deal with hundreds or thousands of decisions in parallel continuously and not periodically and accounted for every single SKU&#039;s individual role in the assortment, humans need help. Exactly.<\/p>\n<p>And this is why the strategic logic has to be encoded. Machines execute rules, not intuition. In if the system needs to make hundreds or thousands of decisions in parallel, it had to understand the role of each ske. For example, is this product a price image item? Is it a traffic driver? Is it a basket completer? Is it a merging generator? The logic cannot stay only in someone&#039;s head or inside the spreadsheet. It has to become That is where automation becomes more than speed. It becomes the way strategy gets translated into action. If competitive advantage now depends on micro decisions at scale, what kind of system if architecture makes that &gt;&gt; So this is where pricing has to shift from being a task to becoming an operating system capability. Commerce is becoming machine legible. Offers are becoming more structured. Flows are becoming more APIdriven.<\/p>\n<p>Agents are increasingly curating and transacting programmatically. So pricing cannot just be a dashboard decision anymore. Pricing logic has to become executable logic and pricing system needs to respond to machine legible signals in milliseconds, not simply update prices more often. Those signals are increasingly event driven rather than periodic. For example, sentiment velocity, skew level, behavioral shifts, demand spikes, competitive triggers, inventory pressure, and even agent originated request. It is not enough to observe those signals in dashboards. The system has to act on them instantly, but with guards. That means realtime signal You are looking at sentiment, skew level behavior, inventory pressure, competitive signals and all of it has to be processed continuously and that requires event-driven architecture rather than batch based pricing logic.<\/p>\n<p>It also requires scalable SAS infrastructure because the system has to process thousands of parallel decisions across the full cut continuously. SAS scale architecture is found is not optional. If pricing logic cannot scale horizontally and respond instantly, retailers cannot compete at machine speed. So this is not dynamic 2.0. This is continuous eventbased decision infrastructure. &gt;&gt; So how is this different from traditional automation? Where do governance and guard set them? &gt;&gt; So the real competitive advantage in the agent era is not simply moving is not simply having the AI that everyone will have access to AI. Okay. The real advantage actually comes from having better inputs for AI. Agents have public data but retailers have contextual intelligence that external agents usually do not have.<\/p>\n<p>They know local inventory, fulfillment constraints, merging guardrails, loyality logic, purchase history, and customer context. The context is the real mode. But context only becomes advantageous when it is unified and structured. At the end of the day, this only works if everything sits in a single source of truth. All of it, catalog, pricing, inventory, customer data, identity, it all has to be synchronized in real time without a single source of truth. AI operates blind. It may optimize, but it may optimize against the wrong constraints and that creates a risk instead of advantage. So you&#039;re basically saying fragmented systems destroy decision quality and AI amplifies the weak foundations instead of fixing them. Right? So if unified context is foundational, why do so many real world retail stacks still fail to operationalize it?<\/p>\n<p>So as I see it, the reason is that many retail stack were built for a different world. uh they were built around batch updates, static checkouts flow and limited mid session recalculation. But agent becommerce creates different requirements. Prices may need to be recalculated during the session. Inventory and availability needs to stay synchronized across channels. Offers has to remain consistent even when demand, competition or customer intent changes quickly. So in many organizations the strategy is ahead of the infrastructure. Retailers know where they need to go but they the stack was not designed to support that level of spend synchronization and decisioning. &gt;&gt; Okay.<\/p>\n<p>So synchronization latency challenges pose the risk of inconsistency across challenge gener so synchronization latency challenges pose the risk of inconsistency across channels we have to address the major shift regarding open AAI&#039;s recent move to drop direct checkout inside check GPT this is a massive signal for retailers while some platforms like Google still aim to own the buy button natively within the search interface. Open AAI is moving towards a referral model. They are spending they are sending users back to the retailer&#039;s own environment such as their website or customer GPT app to complete the transaction and that creates a very different strategy that&#039;s create a very different strategic reality if AI becomes the discovery layer but the retailer still owes the transaction, then the retailer has a chance to preserve reality, post purchase data and the customer relationship.<\/p>\n<p>But that opportunity only matters if the retailer can recognize the intent and respond in real time. So this creates basically two opposite bets on the future of retail. On the one hand, the open AI model where AI becomes the high efficiency discovery engine at the top of the funnel while retailer keep the mode of loyalty and post purchase data. On the other end, if the native check off model wins elsewhere, retailers risk becoming back-end fulfillment centers for a third party The challenge now is not just being machine readable. It is having a pricing braid. that brain that can recognize a high intent user coming from an AI referral and instant personalizing the offer to convert them into your own And this means protocols like UCP and ACP are no longer just optional standard for machine to- machine commerce.<\/p>\n<p>They are the tools retailers use to assert their commerce sovereignty to assert their commercial Whether the purchase happens in a chat or retailer site, the retailer must maintain a single source of truth for price, inventory, and identity. Otherwise, the customer relationship shifts towards whoever controls the interface and the checkoff experience. But if Open AI is sending these users back to the retailer, it puts a massive spotlight on their ability to identify them. Once agents begin referring on behalf of users, what new complexities enter the identity layer? So identity becomes one of the most important and most complicated layers. It if an agent is acting on behalf of a user, a retailer needs to know who the user is, what the agent is authorized to do and what level of personalization is allowed. That introduced constant complexity. It introduced fraud risk.<\/p>\n<p>It creates cost system reconciliation and all of these matters because loyality loyality and personalization depend on identity. If the retailer cannot connect the user, the agent, the session, the offer and the loyality profile, then the customer experience breaks down. And if identity breaks, personalization breaks and trust breaks. A lot of risks I I see to anticipate that I think retailers need to manage a lot of technical aspects. Latency, making sure that there is no delay reaction to demand or competition changes, data inconsistency, as we said, conflicting signals across a system creating conflicting and wrong decisions. For example, inventory price mismatches when pricing does not reflect the actual stock levels leading to loss sales or over discounting.<\/p>\n<p>This configuration of the rules, small logic errors can scale up largely in fast thanks to machine across thousands of SKs instantly. Signal noise overreacting to to wrong signals uh again can destroy value. Or I&#039;m thinking about something like agent arbitrage kind of as you said fraud or like email like a cyber security risk to some extent that agents systematically exploit these inconsistencies across channels if not managed and correct and orchestrated correctly. This is clearly a business risk in terms of margin leakage uh loss growth or race to the bottom if kind of mutually matching and beating prices downwards in a spiral thanks to machine speed.<\/p>\n<p>and more long-term I guess strategic drift having the wrong setup and the wrong rules changing actually what the retailer stands for in terms of price perception price image uh and this eventually also affects the shoppers uh that can get the wrong perception of a retailer lose the trust into the retail and also diluting the brand values of all the the retailers and the products sold. So the path forward starts with the foundation. Retailers need unified data first. They need encoded guardrails. They need pricing logic that can operate in real time while still reflecting business strategy and they need to test this through pilot deployments before scaling it across the organization. This is also not something retailers solve completely alone. It requires an ecosystem of vendors, platforms, integrators and internal teams working together around the new operating model.<\/p>\n<p>The retailer that succeed will not be the ones that simply automate faster. They will be the ones that build the infrastructure to make better decision continuously, safely, safely and at scale. I think this brings us to the central question for retail leaders. Are you upgrading tools or are you upgrading your operating model? Because if AI is treated as another feature, another dashboard or another optimization layers, retailers will miss the bigger shift. That challenge is not just pricing volatility, it is mediation. It is the fact that decisions are increasingly being shaped by agents, platforms, and systems outside the traditional retail environment. So retailers need to shift from feature adoption to infrastructure first operating models including a pricing tool of course built for exactly that per situation. Exactly. AI is not a fixture. It changes how decisions are made.<\/p>\n<p>It changes who participates in those decisions and it changes the speed and scale required to compete. The opportunity is infrastructure-driven intelligence. That means building pricing intelligence, unified context and governance discipline into the core of how business operates. Retailers who treat AI as a plug-in will struggle. Retailers who treat it as an operating shift will win. Thanks. Thanks all for listening in and speak soon in our next episode. Thank you. [music] Price Check the Pricing Podcast powered by Quick Lizard.<\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Erfahren Sie, wie KI den Einzelhandel neu gestaltet \u2013 von \u201eAgentic Commerce\u201c und \u201ePricing Intelligence\u201c bis hin zur Infrastruktur, die Einzelh\u00e4ndler ben\u00f6tigen, um im \u201eAgent Era\u201c wettbewerbsf\u00e4hig zu bleiben.<\/p>","protected":false},"author":52,"featured_media":21792,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","_blog_editorial_updated_date":"","footnotes":""},"categories":[61],"tags":[56],"blog_author":[],"class_list":["post-21749","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-resources-video","tag-resources"],"acf":[],"_links":{"self":[{"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/posts\/21749","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/users\/52"}],"replies":[{"embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/comments?post=21749"}],"version-history":[{"count":1,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/posts\/21749\/revisions"}],"predecessor-version":[{"id":23236,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/posts\/21749\/revisions\/23236"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/media\/21792"}],"wp:attachment":[{"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/media?parent=21749"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/categories?post=21749"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/tags?post=21749"},{"taxonomy":"blog_author","embeddable":true,"href":"https:\/\/quicklizard.com\/de\/wp-json\/wp\/v2\/blog_author?post=21749"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}