Retail Unwrapped from The Robin Report

Curate or Get Curated

Rich Honiball — Thu, 05 Feb 2026 05:01:00 +0000

We live in an age of abundance: more information, more options, more products, more channels, more noise. On the surface, this abundance appears to be a gift—greater access, freedom, and personalization. But beneath that promise lies a growing tension: complexity fatigue.

Choice as No Choice

Consumers are surrounded by choice, and increasingly exhausted by it. They download dozens of apps but use only a handful. They fill closets with clothing but rotate through the same limited number of familiar pieces. They buy groceries with good intentions, only to discard a third of what they bring home. They scroll endlessly, browse extensively, and yet often procrastinate before committing to a decision.

These are not isolated behaviors. They are signals. Signals that challenge the long-held retail assumption that more choice equates to more value. These signals also suggest that the future of retail is not about expanding selection endlessly, but about refining it intentionally. These are signals that point toward a deliberate focus on curation.

Is customer value in retail unlimited choice and abundance? And the answer is: Curation wins loyalty and relevance, aligns customers’ wants and needs, and diminishes complexity fatigue.

Complexity Fatigue

The average smartphone today holds roughly 80 installed apps. Yet only about nine are used daily, and fewer than half of those 80 are used even once a month. The rest sit dormant, available and irrelevant. Consumers don’t delete them because they no longer provide value, but because it requires too much effort. Whether busy, lazy, or overwhelmed, we all naturally seek the path of least resistance.

The same overabundance issue appears at home. The average American single-family house now exceeds 2,100 square feet, yet daily life typically takes place in a small fraction of that space. Entire rooms exist only for occasional use. Space is abundant; its utility is not.

Closets tell a similar story. Studies consistently show that people regularly wear only 50 to 60 percent of the clothing they own. The rest sits untouched. Many garments are worn fewer than ten times before being discarded, oftentimes with regret. The flip side is the upcycling apparel market, but it still doesn’t solve the habit of over-purchasing.

Often, aspirational buying doesn’t align with real behavior. Across categories, ownership exceeds usage and wants exceed needs. This is a mismatch between human psychological reward-driven yearnings and the conventional assumption that more is always better.

What Research Has Been Telling Us for Years

This wants and needs tension is nothing new. Behavioral science has been studying it for decades. One of the most cited examples is the well-known “jam study,” conducted by psychologists Sheena Iyengar and Mark Lepper. When shoppers were presented with 24 varieties of jam, they were more likely to stop and sample. On the other hand, shoppers presented with just six varieties were far more likely to stop and make a purchase.

That finding became a cornerstone of what later came to be known as the “paradox of choice,” a concept explored extensively by psychologist Barry Schwartz. His research demonstrated that excessive choice increases anxiety, regret, and decision paralysis. People fear making the wrong choice, so they delay making any choice at all. Subsequent studies reinforced this across many categories: retirement plans, consumer electronics, apparel, and even healthcare decisions. When options proliferate beyond a manageable threshold, satisfaction declines, confidence erodes, and conversion is sidelined. For years, marketers and the CPG industry have been aware of this research, but too often they continue to march in the opposite direction.

The Endless Aisle

Digital commerce unlocked the paradox of choice on steroids. The infinite shelf space of endless digital aisles became a competitive advantage. More SKUs delivered a broader reach and theoretically higher odds of appealing to individual preferences. For a time, this worked. Search was an exercise in discovery filtered by algorithms that promised personal preference relevance at scale.

But scale brought its own problems. As assortments ballooned, discovery became more opaque. Search results expanded beyond comprehension. Comparison shopping turned into an exercise in fatigue with cart abandonment and surging returns. Too much choice transformed into supply chain nightmares. Add to that, a significant percentage of shoppers now cite “too many options” as a reason for abandoning purchases, particularly in categories like apparel, beauty, and consumer electronics. What was once framed as an empowerment tool has increasingly become a burden. The conversation requires a shift away from “endless aisles” to curating “best aisles.” The goal is to no longer offer everything, but rather to offer the right things, replacing maximum choice with meaningful choice.

Technology Is a Steward, not a Substitute

While many frame AI and human curation as a tug-of-war, the real opportunity lies in partnership and harmony. Technology serves as a powerful retail steward—managing redundancy, detecting assortment drift, and supporting complexity at scale—but it cannot decide what matters. There is a critical distinction: AI and predictive analytics excel at forecasting what might happen based on the past. But the past is not always a prelude to the present, and often what worked in the past is no guarantee of what will work in the present. When it comes to curation, default prediction is high risk. The pathway to innovation isn’t to simply anticipate trends; it is to set them. This requires the human expertise—instinct, cultural nuance, and emotional drivers—that dashboards cannot replicate. Curation is a retailer’s foundational skill; waiting for a perfect technology to streamline that process is an abdication of leadership. Technology and specifically AI should be viewed as a partner to human judgment, with tech as a tool, not a proxy for human beings.

The Human Touch

Perhaps most importantly, curation re-humanizes retail. Historically, retail thrived on trust. Loyal shoppers return not for endless choices, but because the selection makes sense. The modern practice of curation is supported by data, scaled by technology, and guided by human judgment. The irony is self-evident. When consumers have access to everything, they increasingly reward those who offer fewer choices, provided that fewer is better. Healthy retail will not be decided by those who carry the most. It will be led by deft curation, because in a world overwhelmed by abundance, relevance wins.

The implications are clear. Retailers that continue to compete primarily on product breadth risk becoming background noise. Availability doesn’t equate to value. Curation isn’t a niche strategy reserved for luxury brands or personal shoppers. It is becoming table stakes for relevance in a crowded, fatigued marketplace.

Think You’re in Control Shopping for Groceries? Wrong!

Phil Lempert — Tue, 03 Feb 2026 05:01:00 +0000

You think you’re in control of what you choose in a supermarket? Well, sorry to report, but you’ve been played. You don’t decide what you buy at the grocery store. An algorithm does. Welcome to the brave new world of the Agentic AI shopping experience. Your grocery trip is an experiment—and you’re the test subject. You’re not in control and here’s why.

Pricing and promotions are manipulated just for you. You think the electronic shelf price is the same for everyone? The offers hitting your phone, your inbox, and your app are tuned into you—your income bracket, your brand loyalties, your preferences and soft spots. And you gave it all permission. That’s the promise of AI, as long as it doesn’t terrify you.

Think about it. You signed up for loyalty programs. But they aren’t just about rewarding you—it’s a brilliant way to deliver exactly what you want based on what the data has recorded. It’s a win-win if you think about it. You provide access to your personal data, and your local store becomes your personal shopper.

Every scan of your card, every clipped coupon, every “substitute” you accept when something’s out of stock is recorded. You’re not just earning points; you’re teaching the system how to give you what exactly you want.

This is not a bad thing if you want a seamless, stress-free shopping experience. The more you let AI know, the more your local shopping experience can make your life easier. We’re just entering this new world on steroids where AI data accelerates the information you give it to deliver an experience that makes you the most important priority.

Is this creeping you out? Don’t panic. You can limit what AI can access and control the world curated just for you. If it does creep you out, be aware, be informed. But when you think about it, we’re entering a social contract with Agentic AI that can make our lives easier. That’s not so bad.

Intelligent Retail: Mastering Agentic AI

Sarah Holbrook — Mon, 02 Feb 2026 05:01:00 +0000

Agentic AI is an amorphous catchall that is tricky to define but managed to eclipse any other discourse at the recent 2026 NRF Big Show. Brands including LVMH, Home Depot, Warby Parker, Target, and others discussed their understanding of an Agentic AI future. They were joined by executives from Microsoft and OpenAI. Google’s CEO, Sundar Pichai and new CEO John Furner discussed AI integration with Walmart. Additionally, Pichai announced Google’s Universal Commerce Protocol (a set of global standards to facilitate Agentic AI retail activity worldwide).

There was an abundance of chatter, but retailers and brands seemed unable to deliver a concise, consistent definition of Agentic AI and how to use it. Admittedly, Agentic will change the AI conversation dramatically, and as with any introduction of the next best AI tool, its applications and implications are generally misunderstood.

Will Agentic AI dramatically change the course of retail? And the answer is: If retailers learn what Agentic is and how it really works, it’s a direct path to efficiencies and customer delight.

Chasing Agentic Windmills

With retailers seduced by the lustrous orb of Agentic AI, we thought it would be useful to clear the air with a broad definition of the concept and describe how brands apply (or are building applications for) the technologies. At best, Agentic AI is a system’s ultimate thinking tool and takes on the aptitudes of LLM-powered AI in retail.

Customer behavior
Inventory flows
Supply chain rhythms
Pricing strategy
Store operations
Security oversight
Digital interactions
Loyalty patterns
Customer support
Weather activity impacts

Agentic AI Accelerates Retail Operations

Agentic AI upgrades its AI ancestors as it performs tasks autonomously within the retail funnel. New models blend with CRM and POS systems from discovery and checkout to post-purchase support. Agentic AI is still evolving and always improving as it operates at a higher level tackling these complicated tasks.

Influencing discovery
Optimizing inventory
Mitigating supply chain disruptions
Applying dynamic pricing
Facilitating customer purchase activity capture, checkout, and attribution
Leveraging chatbots built from large language models to interpret user input and generate adaptive responses
Coordinating staff scheduling
Identifying and mitigating dark pattern activity and crime
Identifying points of failure
Customizing loyalty incentives
Resolving support issues
Generating catalog, marketing, and advertising content
Adapting based on outcomes
Mitigating weather disruption costs

Omnichannel Evolution

The omnichannel customer experience stitched together stores, ecommerce, mobile, social, marketplaces, and call centers, but the customer moved among these channels as discrete silos. With Agentic AI, the promise is that the channels will dissolve. This near-future experience is emerging as a novel retail ecosystem that will treat every touchpoint as part of a unified system.

The customer journey is continuous and customized
Context follows the customer everywhere
The brand behaves like a single, coordinated organism, not a set of channels (your call is never transferred to another representative)
Data, identity, inventory, and personalization function across devices, whether online, in-store, or both simultaneously
A website can be a living store with the thrill of discovery, and a store can be a living website that directs you to what you are searching for
The experience adapts to the customer, not the other way around

Metachannel retail (no connection to the Big 7 tech company) is an apt reengineering of omnichannel. Metachannel reflects a quasi-scientific interpretation of systemic metapsychology, which applies concepts of self-examination (of a company, not a personality), interdependence (within the organization and between trusted partners), systems-level behavior, and self-repair or mitigation. It is a retail channel that learns from itself, tests and evaluates alternatives, executes decisions, and automatically improves.

Operations and Aspirations

While the pace and intensity of adoption may vary, an AI transformation is underway across the retail industry. Granted, Agentic AI is the latest hot topic, and there is evidence of early stages of early adoption. Plans and aspirations outweigh practical applications in retail. However, there are a few pioneers.

LVMH executives Gonzague de Pirey and Soumia Hadjali have announced an initiative called AI for All, which spans the company’s portfolio of Maisons. The aim of the program is to elevate the user experience across all touchpoints in the system, associates, management, clients, artisans, and operations, through AI. De Pirey emphasizes the importance of an AI that is aligned with the brand’s culture as the company implements what it calls “quiet AI.” He stresses, “The technology should be everywhere, and invisible.” Each Maison at LVMH develops unique AI activations, but the company shares best practices and technology to scale the implementation across the organization. An AI activation at Dior will look very different than one at Sephora, but many of the systems interconnect.
At Louis Vuitton, Soumia Hadjali, Global Vice President, Client Development & Digital, applies AI to leverage LV’s many cultural initiatives. “Our digital concierge is both deeply personal and context aware. It will know how our clients shop; not just what they buy, but who they are, and where they live.” She continues, “Louis Vuitton has cafés, restaurants, and exhibitions. We can organize a private table at our restaurant when it is hard to get a table. We can book tickets for an exhibition. We can become part of our client’s life.”
Target is on an accelerated trajectory. CIO Prat Vemana describes the retailer’s partnership with OpenAI. He is clear about the retailer’s AI ambitions: “Target has moved from using AI to running on AI.” The new OpenAI integration includes many Agentic AI functions aimed at efficiency both online and in-store. Customer service centers and associates have gained two Agentic AI superpowers via the Agent Assist and Store Companion devices developed with OpenAI. The devices allow price matching and can start returns on the spot in-store. Vemana adds, “Target is not a marketplace; it is a curated retailer. The process of vetting vendors used to take thousands of hours; now we get it done in minutes.”
Albertsons’ Agentic AI shopping assistant (which doesn’t seem to have a very catchy name) is jockeying for relevance as grocery options multiply. In a field test AI was challenged with a complicated prompt that required analysis from diverse streams of data and information. It delivered a menu designed to satisfy a group comprising a vegan, lactose-intolerant, peanut-sensitive, and gluten-free group of guests. The bot produced a list of recipes from which to choose and eventually filled the virtual shopping cart with the necessary ingredients, including a few private-label items.
Ralph Lauren’s Ask Ralph chatbot is a more primitive version of Agentic AI that shares sophisticated fashion advice from a virtual facsimile of the fashion icon himself. Chief Branding and Innovation Officer, David Lauren, was an early adopter of OpenAI’s ChatGPT through the brand’s tech integration with Microsoft Azure. Understandably, Lauren is an advocate of the shoppable, conversational version of his Dad steering potential clients toward a sale.

Eyes Wide Shut

AI’s stealth infusion into everything has been stunning. User adoption, exploration, and exploitation are fully turbocharged. Both business and society (including consumers) have had little time to consider the implications of these emerging technological shifts as the world races to retool and keep up with the pace of change. The shiny object that is AI, and the even shinier possibilities promised by Agentic, are alluring, but there are vexing implications that can’t be ignored.

Agentic AI reflects engineering advancements that continue to encroach on unique human capabilities. In a capitalist economy, public corporations beholden to shareholders will choose efficiency over the workforce. These will not be layoffs; It is unlikely that jobs lost to AI will ever return. The economic impact of job losses writ large will impact society, culture, governance, and naturally slam into the retail industry.

The computing power needed to support the massive data centers that power AI currently, compounded by the exponentially increasing energy demands of the more complex Agentic AI, will strain aging energy grids in the U.S. and elsewhere. High power demands drive up electricity costs, particularly in the U.S., as renewables stall. Obviously, higher electricity costs reduce discretionary spending for consumers across many income segments.

Agentic AI requires deep, personal data sharing with service providers. For a consumer-facing Agentic agent to be effective, trust is critical. Consumers will be required to share personal and financial information, including personal characteristics (age, weight and size), location data, biometric information for identity verification, routines, habits, pricing sensitivity and logic, and more. In other words, Agentic AI needs to understand who you are, what you want, how you behave, your constraints, and which actions you are comfortable authorizing the agent to perform. It goes without saying that undisclosed data sharing, selling, leaks, and security breaches will shatter the trust between the customer and the brand.

AI Axiom

As retail’s AI transformation marches forward, it should be approached thoughtfully. Brands and retailers need to anchor every AI decision in their culture, identity, and strategic intent. Ask Ralph, LVMH’s AI for All, and other luxury AI applications should deepen the bespoke, high-touch relationship between client and brand; anything that feels like a generic chatbot breaks the spell. For Target, where speed, scale, and efficiency fulfill the brand’s AI intent, intelligent automation that delivers faster, more personalized service is exactly on‑brand. And when operational AI unlocks cost savings, passing those savings on to shoppers reinforces Target’s value-driven identity. The rule is simple: Innovation only works when it authentically amplifies the brand and delivers meaningful benefit to the one stakeholder who matters most, the customer.

AI Is an Ally, not the Enemy

Retail Unwrapped — Fri, 14 Nov 2025 05:01:00 +0000

The retail frontline sales staff is often an untapped, but highly valuable resource. Everyone wants to know that they matter and can make a difference. So, how do retailers empower their store associates, their most critical customer-facing ambassadors? In an increasingly tech-driven business, new digital tools are designed to create lasting customer relationships that match loyalty with employee job satisfaction. This is especially powerful in small retail businesses where personal loyalty is the gateway to beating the competition. Join Leigh Sevin, co-founder of Endear, and Shelley as they discuss how modern CRM platforms transform data into authentic customer connection. They explore how AI in partnership with employees can enhance personal outreach and make a brand relevant and meaningful. They also discuss the future of personalization that pairs the message with the individual, informed by sales associates who know their customers best. Their conversation reveals how software, analytics and data can propel a retail business through the insights of its frontline sales ambassadors. Learn from this actionable discussion how tech empowers and inspires store associates to level up service, play a more positive strategic role, and find renewed purpose by using AI to make their jobs easier and strengthen their customer relationships.

Special Guests

Leigh Sevin, Co-Founder of Endear

Transcript

How Digital Technology Is Revolutionizing Merchandising

Phil Lempert — Thu, 11 Sep 2025 04:01:00 +0000

Walk into any grocery store today, and you’ll witness a revolution happening right before your eyes—though it’s subtle and you might not even notice it. Those prime promotional real estate spots we’ve called “end caps,” (AKA end of aisles) for decades, are transforming from static displays into dynamic, data-driven experiences that would make Don Draper’s head spin.

The death of the traditional end cap isn't just about technology—it's about data. These digital displays are collection engines, gathering information on dwell time, interaction rates, and conversion metrics that we could only dream about with cardboard and plastic.

Site Specific

I’ve been watching this marketing evolution for the past five years, since the pandemic, and frankly, it’s about time for radical change. The traditional end cap—you know, that pile it high at the end of an aisle with maybe a wobbly sign if we were feeling fancy—has been limping along like a dial-up modem in a 5G world. But what’s replacing it isn’t just prettier; it’s fundamentally changing how we think about category management and promotional strategy. There is a lesson to be learned here for any retailer to not ignore what’s hidden in plain sight: prime, site-specific merchandising opportunities.

Here’s what caught my attention: Kroger Precision Marketing (KPM) shared that their digital end cap displays are generating 37 percent higher engagement rates than traditional signage. That’s not a marginal improvement—that’s a seismic shift. These aren’t just fancy TV screens slapped onto existing displays. We’re talking about AI-powered systems that adjust messaging based on time of day, weather conditions, local events, and even demographic data from loyalty programs. Imagine an end cap for soup that automatically switches from featuring gazpacho at 2:00 PM on a 95-degree day to promoting hearty chicken noodle when the temperature drops and rain starts falling. Use your imagination to substitute soup with sweaters for apparel retailers.

Data Driven

Here’s what this means for legacy marketers. The death of the traditional end cap isn’t just about technology—it’s about data. These digital displays are collection engines, gathering information on dwell time, interaction rates, and conversion metrics that we could only dream about with ancient cardboard and plastic. I recently spoke with a category manager at a major CPG company who told me they can now A/B test end cap messaging in real-time across 200 stores simultaneously. They discovered that their protein bar sales increased 23 percent when the display emphasized “plant-based energy” versus “workout fuel”—and they learned this in 48 hours, not months.

This kind of agility is forcing a complete rethink of how we plan and execute promotional calendars. The days of printing 10,000 pieces of point-of-sale material and hoping for the best are numbered. Finally! From a consumer perspective, the transformation is even more dramatic. Digital end caps make a retailer relevant and valuable. In grocery stores, the signage can provide recipe suggestions, nutritional information, and even connect to shopping apps—turning what was once a passive promotional moment into an interactive experience. Similar applications for beauty, apparel, and home are equally impressive and effective.

I watched a family at a Walmart in Palm Desert, California spend around three minutes at a digital soup display that was suggesting recipes based on items already in their cart (which was detected via their Walmart shopping app). The mother left with four different varieties of soup and the ingredients for a slow-cooker meal she’d never tried before. That’s not just category management—that’s category brilliance.

The Price of Relevance

Of course, this digital revolution isn’t without growing pains. The technology costs are significant—we’re talking $1,000 to $3,000 or more per installation, versus maybe $200 for a traditional setup. ROI calculations become complex when you’re weighing in hardware costs, content creation, and ongoing maintenance against increased sales velocity and margin improvements.

Costs aside, traditional marketers need to think like digital marketers, creating campaigns that can adapt and respond in real-time. And let’s address the elephant in the aisle: digital fatigue. Consumers are already bombarded with screens everywhere they turn. The key isn’t more digital—it’s smarter digital. The most successful implementations I’ve seen focus on utility and relevance rather than flashy animations.

Where is this technology heading? For any marketer, substitute your products for this grocery use case. I predict we’ll see three major developments:

Voice Integration: Imagine asking an end aisle display about ingredient sourcing or allergen information and getting immediate, accurate responses.
Augmented Reality: Point your phone at a product display and see recipe suggestions, user reviews, or even how the item would look in your own kitchen.
Predictive Placement: AI systems \automatically optimize which products appear on which end caps based on predictive analytics, not just historical sales data.

The traditional end cap served us well for many decades, but it was essentially a static, outdated billboard in an interactive world. Digital signage isn’t just changing what we show shoppers—it’s changing how we think about the entire promotional ecosystem.

For traditional marketers, this means developing new skills, embracing new metrics, and thinking more like media planners than just merchandisers. The companies that figure this out first won’t just see better performance—they’ll gain a competitive advantage that extends throughout their entire category strategy.

Embrace and accelerate change, or become irrelevant

You’ll Need a Video Game Strategy for Next Gens

Glyn Atwal — Wed, 10 Sep 2025 04:01:00 +0000

Smart brands see online/video gaming as a shortcut to connect with young (and mercurial) consumers who are otherwise so difficult to reach. To put it into perspective, there will be $100 trillion in wealth transfer to Gen Z. For next gen marketers — and now those targeting Gen Alpha – gaming has become a go-to strategy to tap into those funds. We’re referring to the growing universe of platforms like Roblox which blend interactive gameplay, social connection and user-generated content. Games as a platform are reshaping the cultural definition of ‘play.’ One of the latest hits on Roblox is Steal a Brainrot that revolves around collecting and stealing as many Brainrots as possible. While this may sound bizarre to some, it feels completely normal to millions of young players.

Games and play by association are child’s play, but it might be surprising that video gaming represents a higher share of total media consumption than in any other age group. Video gaming is for children of all ages, and any brand executive can do the math. The Roblox 3D platform alone has more than 380 million monthly active users, growing 19 percent annually from 2021 to 2024. Minecraft grew 12 percent annually, while Fortnite followed with 10 percent. The scale is impossible to ignore.

And this is the edge of the gaming universe; the numbers are impressive. McKinsey & Co. reports that gaming accounts for the largest share of U.S. media consumption at 23 percent, compared with 21 percent for streaming and 20 percent for social media. According to Bain & Co., the global video game market reached $219 billion last year and is projected to grow by 4 percent annually through 2028. What’s more, according to Pew Research Center, 85 percent of U.S. teens report playing video games, and 41 percent say they play them at least once a day. Four-in-ten identify as gamers.

The key to tapping into gaming communities is authenticity -- the most successful brand activations don’t just show a potential customer a product. They embed the brand into core memories that they’re already creating with their friends.

Attention Immersion

The brands that play in the video gaming space know it’s not just about reach but about locking in the user’s attention. On TikTok, engagement is often measured in seconds or even milliseconds. According to Laila Nasr, Strategic Insights & Partnerships Manager at GEEIQ, “Console and PC gaming are the only digital mediums that get close to live-event levels in focus. It’s the difference between scrolling past a social media post and playing a game for hours.” Think about what this means for brands trying to reach next gens in a place they want to be.

McKinsey & Co. measures attention as valuable time spent, driven by focus and intent. Gaming’s advantage is immersion. Leaderboards matter, but the real difference is narrative. As Bain & Co. notes, “Increasingly, gamers and fans are falling in love with universes, stories, and characters rather than the entertainment format itself.”

Gamers invest in creating and co-creating digital identities. A Roblox survey found that 56 percent of Gen Z users agree that styling their avatar is more important to them than styling themselves in the physical world. As Nasr observes, “When you hang out with friends in the real world, you express yourself in specific ways, to show personality, interests, style, and so on. In social gaming environments, like Roblox and Fortnite, players do the same, but here, they’re not constrained by reality.” According to survey data reported in Bain & Co.’s Gaming Report 2025, customization and playing with friends are just as important for gamers on platforms like Fortnite, Roblox, and Minecraft. This relationship immersion leads to the most important currency of any brand: trust.

Trust by Association

According to dentsu’s 2024 State of Gaming Report, 78 percent agree that video games are where they connect with friends and meet new people. For brands, this creates a unique opening to build relationships in gaming communities rooted in authenticity.

“The key to tapping into these communities is authenticity — the most successful brand activations don’t just show a potential customer a product. They embed the brand into core memories that they’re already creating with their friends.” says Nasr.

Brands can build trust and engagement at a community level. For example, e.l.f. Beauty partnered on Love, Your Mind World, a Roblox game supporting teen mental health, and collaborated with Chime to launch Fortune Island: Earn. Learn. Flex, teaching financial literacy, which according to Patrick O’Keefe, chief integrated marketing officer at E.l.f. Beauty aims “to equip our community with the skills and swagger to be their best E.l.f. selves.”

Gaming platforms can be an effective virtual shop window. Vans dropped the Mixxa on Roblox before selling it anywhere else. And a source of valuable advice. Maybelline took over Roblox’s Paradise RP to demonstrate its Sunkisser Blush.

A bigger game-changer, however, is how these platforms are doubling down to create a virtual selling ecosystem. Beauty brands such as e.l.f. Beauty sell real products via Roblox while Fenty launched a shoppable game where players could unlock an exclusive shade of its Gloss Bomb lip gloss.

Gaming platforms are an ideal playground for users to discover and experiment with new products, brands, styles, and influences. For example, L’Oréal Paris lets users play around with the style and color of their avatar’s hair. Skateboarding fans can customize their own Vans shoes for their avatar. It’s a smart move. According to a Roblox survey, 84 percent say their physical style has been inspired by their avatar’s style. However, Nasr warns that success won’t come from brands simply adding a checkout button, “The winners will build experiences where shopping feels like play — where discovery feels earned, products feel native, and purchase is a natural extension of the game.”

A New Game Plan

Gaming is a medium where users commit time, focus, and emotion. As Nasr observes “Social media is saturated, but gaming offers the deeper engagement needed for brand love and loyalty to grow.” This depth of engagement also translates into conversions. YouGov data report that nearly a third of U.S. sandbox gamers agree with the statement, “I am a sucker for anything branded, even if it’s expensive,” compared with just 22.3 percent of the general population.

Creative content underpinned by strategic reasoning will be critical to successful brand activations, particularly in crafting aspirational experiences. For example, Maybelline’s Roblox game Glamhattan: Colossal Bubble (live from June 15 – October 15, 2025) invites players to explore New York neighborhoods, experiment with virtual makeup looks, unlock exclusive branded rewards, and purchase products directly in-game.

Savvy C-suite marketers understand that virtual immersion experiences drive a high level of engagement and know how brands can both disrupt and redefine the customer journey. It’s a game that brand executives can’t afford to lose. Kids don’t stop playing games when they become adults. It’s a market for life.

The AI Agent Survival Guide for Retailers

Barry McGeough — Thu, 21 Aug 2025 04:01:00 +0000

Recently, I sat down with Steve Statler, CEO of AmbAI, expert in Ambient AI technologies, author of “Beacon Technologies,” and host of the Mr. Beacon Ambient IoT Podcast, to discuss the dramatic advances in AI. This report is the distillation of our wide-ranging conversation that we offer as a survival guide for retailers and brands.

The irreversible shift is already happening. ChatGPT is answering 1 billion searches per week. Traffic from AI to retail websites jumped 1,200 percent in a single month. Nearly 40 percent of consumers now use AI as a shopping assistant for researching products or planning purchases.

But research is just the beginning. The next wave of AI will be “agentic,” moving beyond answering questions to taking actions on our behalf, including making purchases. This represents a fundamental disintermediation threat from AI agents acting as intermediaries between brands and customers. Will this help or harm the brand and retailer connection to the consumer? Will this enhance or impede sales?

Once AI has unfettered access to product data, it becomes much harder to control the terms of engagement. The brands that survive won't be those with the biggest marketing budgets or the most shelf space. They'll be the ones who recognized that in an AI world, controlling the data bridge between digital intelligence and physical products is the ultimate competitive moat.

AI Agent Advantage

Like any procurement professional, this new generation of agents will seek to weaken the position of suppliers by restricting information flow, to get the best possible deal. Imagine a world where your AI agent negotiates directly with a supplier’s AI agent to purchase groceries, select insurance, or replace your T-shirts, jackets, worn-out shoes—all without you ever seeing a brand website, video, or advertisement. The negotiation strips away the positioning information and storytelling, which can bring value to products, by commoditizing the product that the brands are selling. Agent-to-agent transactions could spell the death of the branded experience, limiting all transactions to the lowest common denominator transactions, with us being none the wiser as to what we are being served. Clearly, this is a problem for brands, and the time is ripe for a proactive solution.

The solution lies in building a strategic alliance of brands, retailers, and technology partners that controls the data flow between consumers using AI to buy and the retailers and brands that supply them. This alliance would ensure brands maintain direct customer relationships while leveraging AI’s power to enhance rather than commoditize the shopping experience and storytelling that makes products valuable.

The Coming Disintermediation

Consider the structural and cultural shift we’re facing. AI is positioning itself between brands and customers, armed with three unprecedented advantages:

Intimate customer knowledge: While Amazon knows what you’ve bought, AI knows why you bought it, what you use it for, and whether you really needed it. Through conversations about relationships, health, finance, and daily life, AI platforms are building the most comprehensive customer profiles ever assembled.
The power of consumers’ automation bias. As termed by the National Institutes of Health, this bias is the very real and prevalent phenomenon when people, consumers, favor or give greater credence to information supplied by technology like AI agents and ignore contradictory evidence. When agents talk, with uncanny knowledge of our preferences, better mimicking human nuance and empathy and with more authority, we will listen.
Complete product intelligence: AI can parse every specification, review, and data point about every product from every competitor, then analyze, compare, and negotiate faster than any human buyer.

This combination of customer knowledge and product intelligence is potent. Users won’t need to navigate websites or deal with limited store staff. AI will understand requirements, shop around, negotiate, and even create personalized content to tell the story of why Product X is perfect for Customer Y.

Traditional merchandising tools—placement, positioning, brand relationships—become irrelevant when a superintelligent purchasing agent is making decisions based purely on data and customer intimacy.

Walmart just announced it will focus its AI efforts on agency, hoping to attract more shoppers away from Amazon with four new super agents, with the goal that AI will drive its ecommerce growth, aiming for online sales to account for 50 percent of its total sales within five years. Designed to operate with minimal human oversight and execute complex tasks across Walmart’s vast ecosystem, the four agents include:

Sparky (customer-facing)
Associate (employee support)
Marty (supplier automation)
Developer (AI testing)

Walmart may be the first to announce such a fully automated decision-making platform for both brands and consumers, but it certainly won’t be the last.

AI: Disruptor in Chief

We can already see the evidence of agents’ disintermediation. Website traffic is falling measurably across the board as AI summaries are presented on the Google home page. These summaries satisfy a user’s question with zero click throughs to brand websites. Existential question: How can you directly influence consumers if they never see your messaging?

The AI powerhouses are actively preparing to take on purchasing tasks on behalf of your customers. Over one in four of the integrations recently announced by Anthropic was with payment systems PayPal, Square, and Stripe.

OpenAI announced its first services for brands, enabling purchases directly from ChatGPT. There will be no shortage of brands that accept the offer to participate in this pilot, desperate to show shareholders that they have an AI strategy that goes beyond writing marketing copy more efficiently.

The Data Fortress Strategy

The solution lies in controlling something AI desperately needs but currently can’t access at scale: real-world product data. Today, AI’s view of the physical world is surprisingly limited. Show ChatGPT a photo of a product, and it sometimes guesses wrong. AI typically can’t navigate auto-ID systems, struggles with barcodes and QR codes, and has no connection to RFID or emerging ambient IoT data streams.

But this data represents the largest untapped information pool in the world—trillions of physical items, each with massive datasets about ingredients, provenance, authenticity, location, and lifecycle history. This isn’t just defensive data—it’s the foundation for new business models around transparency, sustainability, and customer engagement.

This isn’t theoretical. The EU is mandating Digital Product Passports (DPPs) for apparel, toys, and furniture within two to three years. Products entering the world’s largest regulated market will need digital IDs with comprehensive provenance data. Like EMV chip-and-pin standards, what starts as regulatory compliance becomes a competitive advantage through reduced fraud, improved efficiency, and enhanced customer trust.

Any brand wanting to sell into Europe will need to develop its DPP systems. This will eventually be a formidable war chest of data that the AI companies won’t control. Brands already have an amazing cache of product information that they can use to differentiate a direct, brand-controlled, AI-enabled, shopping experience, which can make for a better, more consultative retail experience. Think of all the instruction manuals, recipes, videos and product information that can be fed into a brand-controlled AI.

Building an Alliance Moat

No single brand can defend against AI disintermediation alone. A single brand app with AI, no matter how good it is, will not stem this tide. We’ve seen most brands fail when they attempt to drive meaningful usage of an app that works with their products exclusively.

An alliance must include three key constituencies working in concert:

Leading brands across categories (CPG, fashion, electronics, automotive) that control rich product data
Progressive retailers who understand the value of customer relationships over pure efficiency
Technology partners who can build and operate the neutral infrastructure required

If we are to offer utility, get frequency of use, and the intimacy that is key to an effective selling experience, a multi-tenant platform will be required that helps consumers manage their pantry, wardrobe, drinks cabinet and tool chest with a one-stop shop. This kind of platform lends itself to being used regularly, having utility, and building trust if it helps customers keep control of their data in a way that the AI giants are not disposed to do.

Timing is one crucial advantage: AI’s connection to the physical world is still being built. Like setting smartphone rules for teenagers, we get one chance to establish ground rules before the AI floodgate opens. Once AI has unfettered access to product data, it becomes much harder to control the terms of engagement.

The brands that survive won’t be those with the biggest marketing budgets or the most shelf space. They’ll be the ones who recognized that in an AI world, controlling the data bridge between digital intelligence and physical products is the ultimate competitive moat.

The Crossroads

Brands face an immediate binary choice: Build an alliance now while you still can or become a commodity product in an AI-driven marketplace where you have no direct customer relationship and compete solely on AI-optimized metrics. Europe is already on the case building a defense that will provide rich data about products that should not be handed over to the AI giants lightly.

While big tech races to capture consumer data, they’re conveniently overlooking a fundamental issue: giving consumers actual control and portability over their product ownership data, usage habits, and preferences.

The question is whether brands will proactively shape a system that leverages these opportunities in a way that consumers can trust or reactively comply with whatever emerges from Silicon Valley. This Alliance needs founding members now—brands willing to lead this new model of cooperative competition. The window for establishing ground rules is closing rapidly. Every day that passes without coordinated action is a day closer to permanent disintermediation.

Winter is coming. The question is whether retailers will work together to build the alliance moat or find themselves on the wrong side of it when the battle for market dominance begins.

Amazon’s Showrunner Bet for Retail Media

Brian Delp — Wed, 20 Aug 2025 04:01:00 +0000

Amazon’s recent investment in Fable’s Showrunner platform positions the company not just as a retailer or streaming provider but as an emerging player in generative retail media entertainment. While many retailers are still working to scale their retail media networks (RMNs), Amazon is already exploring what may come next: AI-powered, user-generated storytelling. This is a strategic bet on engagement, and a recalibration of what media means in a retail context. In an increasingly fragmented attention economy, where traditional advertising no longer guarantees results, Amazon is looking to reinvent the retail media ecosystem.

Showrunner brings new risks. How will Amazon manage IP ownership for user-generated content? Can it maintain quality control while scaling content creation? Will creators embrace the platform or push back, as they have with other generative AI tools? And what guardrails will Amazon apply around moderation, misinformation, and monetization?

The Rise of Retail Media Networks

Retail media networks have become one of the fastest-growing segments in advertising. Following in Amazon’s footsteps, retailers like Walmart, Target, and Kroger have built their own media groups. Platforms including Walmart Connect, Target Roundel, and Kroger Precision Marketing let brands monetize first-party data through sponsored search, display, and programmatic advertising within closed-loop attribution models.

This shift was sparked by Amazon’s launch of the Amazon Ads Platform (AAP) in 2012. In its first year, the retailer reportedly generated approximately $600 million in ad revenue. Since then, it has seen consistent double-digit growth. Last year, the company reported $56.2 billion in advertising revenue, making it the third-largest digital ad platform in the U.S., behind only Google and Meta. By contrast, Walmart generated $4.4 billion and Target $649 million from their respective RMNs. These networks are growing, but they’re still following the same predictable template: banner ads, search results, and programmatic placements. Amazon, meanwhile, is already moving beyond its own original blueprint.

Amazon’s Generative Media Strategy

Despite building a dominant RMN, Amazon’s investment in Showrunner suggests a new long-term path built on participatory advertising. Showrunner lets users generate animated episodes using AI prompts and then insert themselves into the content. Contributors become influencers and can earn a share of revenue when other creators iterate on their episodes, such as adding new characters, alternate endings, or spin-offs within the Showrunner environment, similar to how user-generated game mods work with games like Roblox and Minecraft. It’s a remixable, creator-first approach.

The platform aligns with Amazon’s broader ecosystem. Content generated in Showrunner is intended to integrate with Alexa, Fire TV, and Prime Video, while also unlocking new pathways for shopping, discovery, and engagement. Rather than simply producing content, Showrunner is an integrated commerce platform. It serves as a tool for product discovery by linking creative content to Amazon’s retail offerings. Imagine a creator producing a fashion show where every outfit links directly to Amazon listings. Or an animated series where a robot chef teaches recipes that sync with cookware and grocery orders. Or a travel documentary where the host visits street markets and every handmade item can be purchased instantly via Amazon.

Amazon’s media ambitions aren’t new. They’ve taken major chances on content before, often at great cost. The company has poured billions into content production through Amazon Studios, including a reported $715 million on the first season of The Lord of the Rings: The Rings of Power, a project that drew sizable audiences but faced creative criticism and questions around return on investment. The Showrunner investment is a departure from these past efforts, aligning more closely with Amazon’s legacy of building scalable transactional platforms over producing top-down content.

Consumer Behavior Has Already Moved On

Amazon’s pivot to participatory content doesn’t come out of the blue. Consumer behavior is shifting, and audiences are choosing creator-led, short-form content, and niche communities over traditional studio-created storytelling. YouTube was recently ranked as the most popular media platform in the U.S. by time spent. Consumers spend over 11 billion minutes on the platform every day. That’s more than Netflix, and more than Facebook and Instagram combined.

In response to this shift, companies are approaching the convergence of media and retail from different angles. Netflix is going physical. In 2025, it plans to launch Netflix House, immersive retail spaces tied to shows like Stranger Things and Squid Game, complete with branded merchandise, themed food, and experiential attractions. While Netflix is embedding content into physical retail, Amazon is embedding retail into user-generated content. Both aim to blur the line between media and commerce, but from entirely opposite directions. Whether starting from retail or media, everyone is chasing the same outcome: more meaningful engagement and more monetizable attention. The future of retail and media lies in who can best merge storytelling with real-time transactions. Amazon’s strategy stands out because it is building content-native commerce from the ground up. Other retailers have experimented with blending media and commerce, but often as collabs rather than fully integrated models. Walmart’s experience offers a telling example.

How Walmart Failed to Do Hollywood

To understand how different Amazon’s media approach is, it’s worth looking at retail leader Walmart. Walmart’s media efforts have spanned multiple formats; in 2010, it acquired Vudu, a direct-to-consumer video platform meant to rival Netflix. But Walmart never fully integrated Vudu into its retail experience and ultimately sold it to Fandango in 2020. In 2023, Walmart partnered with Hallmark to launch a shoppable holiday movie. The campaign embedded clickable Walmart products into the film, blending entertainment and commerce. Clever in execution, but limited in scope, it was a one-off branded campaign, not a platform.

Walmart’s growing partnership with Zepeto, a virtual social platform where users create avatars and explore 3D spaces, and its acquisition of Vizio suggest it isn’t giving up on interactive formats. In fact, it sees the long-term value of owning both the screen and the signal. But where Amazon is betting on participation and creation, Walmart is still chasing distribution and display. Both investments align with social commerce trends and Gen Z behavior. Yet, the contrast is clear. Walmart is retrofitting entertainment with commerce. Amazon is building a new layer of content-native commerce infrastructure.

What It Means for the Future of Retail Media

Retailers have spent the last few years building ways to monetize attention. Amazon is now investing in how customers can create it, unlike the others that remain tethered to traditional marketing tactics. Target leans into curated seasonal drops for attention, Sephora blends commerce with community tools, Nike experiments with gated co-creation via .Swoosh, and LVMH has high-production AR campaigns. All are tightly managed, brand-led ecosystems. Walmart, for its part, continues to invest in acquisitions, but its efforts still focus on distribution and display, not participatory creation.

This shift toward participation isn’t happening only in retail. In the agency world, R/GA’s work with Google’s Veo for luxury brand Moncler shows how AI is transforming storytelling itself. The campaign compressed production to just four weeks, replacing the traditional linear process with a fluid, iterative model where human creative direction blended with AI’s unexpected outputs. R/GA’s first-ever acquisition of AI studio Addition signals a deeper commitment to building proprietary systems for next-generation, participatory storytelling. For brands and retailers alike, it’s a reminder that the creative infrastructure supporting retail media is evolving just as quickly as the platforms themselves.

Amazon, by contrast, is transforming customers from targets into co-creators. Showrunner signals that the next wave of RMNs may not look like media buys or display ads. They may look like customer-created worlds, stories, and tools, crafted not just for watching, but for remixing. And they’ll be built into ecosystems where transactions, content, and data aren’t siloed but synchronized.

As promising as the model is, Showrunner brings new risks. How will Amazon manage IP ownership for user-generated content? Can it maintain quality control while scaling content creation? Will creators embrace the platform or push back, as they have with other generative AI tools? And what guardrails will Amazon apply around moderation, misinformation, and monetization? These are not small concerns. Unlike others testing the waters of content-commerce convergence, Amazon has a decade of media experience to build on, even if some of those efforts missed the mark.

If the future of commerce is about who captures and sustains attention, then building the infrastructure for co-creation may be retail’s most powerful move yet. Retailers can no longer rely solely on data-driven targeting or traditional media placement. The next competitive advantage lies in enabling customers to generate the stories themselves.

Amazon’s bet on Showrunner isn’t just an experiment in generative entertainment. It reflects a broader shift as the boundaries between media, commerce, and technology are collapsing into a single participatory ecosystem. Platforms that effectively support co-creation may better influence how consumers interact with brands and navigate the shopping journey.

Dove Takes an Anti-AI Oath

Dana Wood — Mon, 18 Aug 2025 04:01:00 +0000

Of all the people to lead me down the Big Beauty AI rabbit hole, I wasn’t expecting Malcolm Gladwell. But then he swung by the global IBM Research headquarters in Yorktown Heights, New York, to de-brief with two major L’Oréal honchos – Matthieu Cassier and Gabriel Bertoli – about the 1.5-year-old AI collaboration between the two corporate giants, and then it was off to the races.

In its new ad campaign, “The Code,” Dove explores the explosive impact of AI in shaping perception and estimates that up to a whopping 90 percent of the content consumers currently engage with is generated by AI. And with AI-generated imagery in particular, the depiction of beauty is obviously skewed toward an unobtainable level of perfection. Dove is renewing its vow to champion real beauty, with a commitment to never use AI to create or distort women’s images.

AI’s Beauty Play

Cassier and Bertoli, both long-time L’Oréal guys, hold similar sounding but slightly different titles. Cassier is VP Research & Innovation, Chief Transformation & Digital Officer. Bertoli is Chief Digital Transformation Officer, Research & Innovation, and is also an AI professor at the Paris-based ESCP Business School. In other words, Cassier and Bertoli already had their fingers on the pulse before Gladwell came knocking for the latest episode of the podcast Smart Talks With IBM.

Dubbed “L’Oréal and IBM: AI-Powered Beauty,“ the episode is a fun, sometimes playful peek into the way the world’s biggest beauty company has been harnessing the power of AI. As the three men toggle between serious business and bravely applying long-wearing Maybelline hits like Vinyl Ink lipstick and Superstay 24-hour compact powder foundation to their faces, it becomes instantly clear that legacy beauty companies have a massive leg up on even the hottest digital-native brands.

The legacy superpower? Data. Decades and decades of information about not only their proprietary product formulations and the ingredients that fuel them, but their also customers as well. Sure, Rhode may gotten itself acquired for a cool billion, but it hasn’t been in business for 116 years like L’Oréal.

Eschewing One-Size-Fits-All AI

Of course, L’Oréal is not alone in recognizing that treasure troves of company-specific info bytes can be paired with powerful AI tools to shape future business. Take Estée Lauder Cos. for example. Although it’s pairing with Microsoft and not IBM, it too is deploying AI to shorten the timeline of getting personalized, customer-specific products to market, as well as streamlining internal ops.

Though she recently stepped down from her roles as Chief Data Officer and Executive VP Enterprise Marketing, Jane Lauder was a major catalyst in helping EL Cos. leverage the power of AI. At a conference staged last year by The Next Web — “Beyond Skin Deep: AI’s Impact on Beauty and Bias” — Lauder told the crowd that while she knows people don’t readily associate the 75-year-old EL Cos. with technology, in fact it drives every product it makes. “Beauty is one of the most personal [consumer goods] categories,” she said. “And personalization is driven by data. That’s how we bring AI into this whole equation.”

After studying how other industries work with AI, Lauder developed what she calls “The 5 Ts” – five takeaways that can help companies integrate the constantly changing technology:

Create an internal task force to ensure all employees stay on the same page in terms of AI use
Ensure trust that all company data remains secure and protected
Hire the most AI-savvy talent
Train senior leaders so they know the right questions to ask
Trial, i.e., keep iterating with AI until the desired outcome is achieved

For massive global beauty conglomerates, off-the-shelf AI offerings won’t cut it. They demand – and can pay for – custom technology shaped by their own unique needs.

Fulfilling Both Current and Future Needs

In an extremely in-depth January 2025 article on its website, McKinsey & Company guesstimated that GenAI could bolster the global economy by up to $10 billion through adaptation by the beauty industry alone. While the research firm didn’t pinpoint a timeframe, no matter when it happens, it’s still a lot of coin, even for a consumer goods sector that’s already expected to garner approximately $677 billion this year.

Dividing AI-fluent beauty companies into two camps – “Takers” that buy off-the-shelf technology and “Shapers” that bake in their own proprietary data – McKinsey rattled off multiple ways in which AI can change the game. Here are just a few:

Trend identification and social listening
Micro-segmentation based on consumer data
Summarized customer information for retail sales associates
Creative content versioning
Media optimization
Rapid package-concept development
Store design and planning
Code automation

As for internal ops, McKinsey says AI can help with all of that, including talent search, recruiting, performance analytics, onboarding and sales and customer support.

Core Ways Beauty Consumers Are Tapping AI Tools

When it comes to product purchases, there are three main ways beauty retailers are already deploying AI: virtual try on, skin analysis, and product personalization.

Virtual Try On: Since it’s largely confined to makeup shade selection, virtual try on has its limitations. But for anyone who has wasted hard-earned cash on a lipstick or blush that looked great in the clear package or box shade swatch, but god awful in real life, having the option to “try before you buy” has its benefits.

Of course, consumers can’t assess texture via virtual try on, or how well a particular makeup product performs. But they can at least get a sense of whether a color works with their skin tone. And in doing so, possibly save themselves a few bucks. Not surprisingly, Gen Zers, who are no fans of in-store “shade-matching” by beauty advisors, are especially keen on virtual try on.

Skin Analysis: According to Precedence Research, the global AI skin analysis market is sitting at roughly $1.8 billion right now and is expected to jump to a not inconsiderable $7 billion by 2034. Evidently, AI skin analysis helps consumers not only assess the condition of their complexion, but also serves up product recommendations to address whatever it is that ails them: fine lines, dark spots, acne, etc. In addition, some AI tools can help consumers track progress over time.

Like so many other professionals, I’m sure dermatologists are positively thrilled that AI tools are performing tasks that have long been their bread and butter.

But guess what the more forward-thinking and tech-savvy skin docs are now doing? Incorporating AI skin analysis tools into their practices. After all, if these tech platforms can save time and “recommend” more and more products sold in the retail area of a derm’s office, why not?

Product Personalization: Brands like Prose and Function Of Beauty have shaped their entire business models around having consumers fill out extensive online questionnaires prior to purchase. From preferences around shade, fragrance and product texture to specific ingredients that target a consumer’s precise needs and beauty goals, if executed well, personalization not only helps stem the overwhelm attached to the sheer size of the market it also instills loyalty. Talk about a win-win for brand and consumer alike. After all, who doesn’t love a bespoke experience? And from a brand perspective, who doesn’t love a repeat purchase?

How Dove Is Still Keeping It Real

In its decades-long quest to celebrate “real” women – curves, freckles, frizzy hair and all – Dove has made it its mission to help beauty consumers learn to love themselves exactly as they are. In its new ad campaign, “The Code,” the beauty giant explores the explosive impact of AI in shaping perception and estimates that up to a whopping 90 percent of the content consumers currently engage with is generated by AI.

And with AI-generated imagery in particular, the depiction of beauty is obviously skewed toward an unobtainable level of perfection. This isn’t sitting well with Dove, nor, one assumes, its multinational parent, Unilever. “The rise of AI poses one of the greatest threats to real beauty in the last 20 years, meaning representation is more important than ever,” reads some of The Code copy. “That’s why Dove is renewing its vow to champion real beauty, with a commitment to never using AI to create or distort women’s images.”

To help like-minded brands and consumers turn back the AI bias tide, Dove has generously supplied a 72-page PDF of its “Real Beauty Prompt Playbook” on its website. Targeted to “creators of any kind, plus parents, guardians and anyone interested in learning more about prompting,” the playbook is an anti-AI manifesto of sorts, and a much needed one at that.

Taking Cues From Next Gens

No matter how laudable Dove’s efforts are to turn back the beauty bias tide, the fact is, AI isn’t going anywhere. And rather than try to resist it, beauty brands and retailers should probably take a closer look at how Gen Z is deploying it.

Digital-savvy to the nth degree, Gen Z gathers its lengthy list of product recommendations from TikTok and Snapchat and then dives right into AI for custom-fitting. According to beauty-tech purveyors Revieve, 58 percent of Gen Z relies on selfie-based AI product try ons, such as its industry-first digital hair color experience or its makeup advisor. And it’s not just a lip or eyeshadow shade; in many cases, it’s an entire “look” crafted from multiple makeup products.

Personalized skin analysis is another fixation for younger beauty savants. Because they’re highly unlikely to head to a dermatologist at the first sign of skin issues, they’re tapping AI for complexion help and to research ingredients and formats they’re wary about, such as face oils.

While beauty mega-retailers Sephora and Ulta have had solid AI games for years now, and Revieve already has a lengthy list of brands and merchants using its technologies (Shiseido, Murad, Living Proof and Walgreens, to name just a few), it’s probably time that every single player in the space to step up to the AI plate. Take a cue from Gen Z: we’re never going back to a time when the customer was completely disempowered; tap into the AI technology that’s literally at our fingertips.

Warnings and Revelations about AI

Barry McGeough — Thu, 07 Aug 2025 04:01:00 +0000

Editor’s note: We are taking this opportunity to preview an AI-enhanced future that is closer than you think. Although not specifically related to retail, this report is required reading to be forewarned and forearmed about emerging tech breakthroughs that will change all our lives. Knowledge is power, and in this AI Era, the more you know, the better decisions you can make.

We Are All Dreamers…Perfect Dreamers

When it comes to technology, regardless of the consequences, we humans are inexorably drawn to optimism, and we have been consistently proven right to do so. Using our dystopias as guardrails, technology has always delivered outcomes that extend the quantity and quality of human life.

From simple stone tools to complex genetic engineering and information technology, the history of technology is the history of human invention. The speed at which the invention of these tools and technologies is being delivered now has become exponential as the application of machine learning and AI becomes the ubiquitous open-source ingredient.

Tech History as Prelude

Take for example, the cotton gin. Invented in 1793, the cotton gin used a combination of wire teeth on a rotating cylinder and a grate or screen to pull the cleaned cotton fibers through, significantly increasing the speed and efficiency of cotton processing. This had a major impact on the cotton industry, particularly in the Southern states, where cotton was a major, labor-intensive crop and whose commercial use would force huge percentages of the labor force out of work. In the face of this radically disruptive technology, the cotton gin’s inventor, Eli Whitney, wrote this to his father: “One man and a horse will do more than fifty men with the old machines. ‘Tis generally said by those who know anything about it, that I shall make a Fortune by it.”

And indeed, fortunes were made, and the potential catastrophic implications to society were far overshadowed by the entire industry that was born from this simple invention that fueled regional growth in the South for over a century.

And so, it continues into this century. Andrew Ng, founder and lead of Google Brain and currently an Amazon Board member, recently said the same. “Just as the Industrial Revolution freed up a lot of humanity from physical drudgery, I think AI has the potential to free up humanity from a lot of the mental drudgery.”

The Mother of Invention

From simple stone tools to complex genetic engineering and information technology, the history of technology is the history of the human invention of tools and techniques. The speed at which the invention of these tools and technologies is being delivered now has become exponential as the application of machine learning and AI becomes the ubiquitous open-source ingredient layer to everything and is mutually accelerated by the arms race for processing speed. We now measure success in petaflops (a quadrillion floating-point operations per second) and trillions of parameters (the internal, numerical values that LLMs learns during training to process and generate text).

As knowledge-seeking animals, the potential value of these tools to increase our understanding of the world around us and solve some of our most pressing problems around the quality and quantity of life is as tempting as Pandora’s Box, which, while it contained all the evils of the world, it also contained hope. As Marc Andressen said, “Technology is the glory of human ambition and achievement, the spearhead of progress, and the realization of our potential.”

While decades in the making, with this new computational prowess we are continually (seemingly daily now) seeing breathtaking exponential breakthroughs, none more exciting than in the field of Synthetic Biology, where we can truly see the practical application of the positive outcomes of AI. One powerful example of how this is already being used is being pioneered by Andrew Adams at Eli Lilly and Company. In seeking treatment to the debilitating disease for Alzheimer’s, researchers discovered the Christchurch Mutation: just one copy of the Christchurch variant conferred protection against Alzheimer’s disease, even in individuals with genetic predispositions to the disease. Being able to apply Synthetic Biology to enhance and deliver this modification would transform the field of Alzheimer’s from treatment to prevention.

Add to this the recent open-source release of Google’s Alphafold, their free AI Model to predict the structure of all of life’s molecules and we start to see what’s now possible. The fact that Google just gave away access to over 200 million proteins and provided a free AI tool for scientists to experiment with is in itself exponential.

Admittedly, to the non-scientific community, this seems interesting but arcane until we start to see the practical application of the new technology toolsets being built. Enter Colossal founder Ben Lamm, a protégé of George Church. A self-described ‘serial entrepreneur,’ he started Colossal with $15M in 2019, now worth $10.2B, with the sole purpose of advancing the cause of de-extinction, using advanced gene editing technology to rebuild the DNA of lost megafauna and other creatures. With Earth on track to lose between 30 to 50 percent of its biodiversity by 2050, maintaining this integrity is vital to life on Earth; his work is mission-critical. His most recent accomplishment, the birth of three dire wolves (Romulus, Remus, and Khaleesi) is considered “the world’s first successfully de-extincted animal,” the first of their kind to be alive in over 10,000 years. While Woolly Mammoths and Dodo Birds are also on their roadmap, the work at Colossal is not simply to create novel creatures for 21^st-century Jurassic Parks; instead, it is about building the technological capabilities to stop the current waves of extinction in their tracks. Colossal also seeks to use Synthetic Biology in other useful ways, successfully editing the genes of an Amazonian microbe they call ‘X-32’, from a microbe that enjoys eating plastic to a microbe that is voracious for them, digesting polymers in weeks instead of decades or centuries and leaving behind carbon dioxide, water, and biomass. This novel solution could swiftly help us address the problem of plastic pollution on our planet and microplastics in our bodies, all with the enhanced use of data and computational power.

A Synthetic Future

With synthetic data and synthetic biology naturally comes ‘synthetic everything’. Synthetic Humans (Robots), Synthetic Media (Google VEO, Midjourney), Synthetic News and even Synthetic Truth. That there are bad actors and bad intent, we are clear. And the need for us to be diligent and leery, especially in this current era, of dystopias of our own making, is real. The need for the ethical human in the loop couldn’t be more dire and we must all remain diligent to these poor outcomes. We must, and will be, diligent, as these are the human decisions put before us as we build this awesome new AI toolset whose promises far outweigh the perils.

At this year’s World Economic Forum in Davos, Andrew Ng, ever the tech optimist, was asked directly about the perils of AI and Artificial General Intelligence (AGI), and he replied, “Do we think the world is better off with more intelligence? We use, primarily, human intelligence; now we have artificial intelligence. I think that intelligence, net-net, tends to make societies wealthier, make people better off…intelligence can, in some cases, be used for nefarious purposes, but on average, I think it actually makes us all much better off.” But is this a false equivalency? More intelligence may be better intelligence, but is more AI better AI? The answer to this depends wholly on how we use it.

From our daily businesses to our daily feeds, the exponential pace of the practical use cases for the application layers of machine learning, AI, and even Agency is indisputable. It has already become a known-known that AI is the ubiquitous open-source business ingredient for speed, agility, efficiency and profitability. To paraphrase Eli Whitney, ‘From this technology, one can make fortunes.’ However, only by fully understanding the implications beyond the opportunities and using the processes of applied innovation to look at technology breakthroughs outside our industry, we can see more clearly a wider view, ensuring we drive the most desirable outcomes.

As leaders, we must always be deliberate about what we set in motion. In Pandora’s fable, when we are open to every inevitability there can be unintended consequences. But with the right insights, we also know that Hope never left the box.