Retail Technology Adoption

Now that we're halfway through 2025, where do we stand with AI for Retail? AI = New Competitive Moat for Retailers - Incumbents like Amazon and Walmart are using AI to reinvent customer experience, logistics, pricing, and personalization. - Open-source models and global competition (notably from China) lower the barrier for smaller players to adopt cutting-edge tech. AI-Powered Consumer Discovery is Mainstream - ChatGPT’s growth and capabilities are disrupting how customers discover products—search behavior is changing fast. - Generative AI is being integrated into search (Google), recommendation engines, and digital shopping assistants. Personalization at Scale Is Now Table Stakes - AI enables ultra-personalized messaging, product curation, and promotions—critical in email, SMS, and ecommerce experiences. - Segment-of-one marketing is becoming real-time and predictive, not reactive. AI Reshaping Ops & Labor - Brands like Yum! (Byte by Yum!) are using AI to optimize inventory, kitchen ops, scheduling, and staffing decisions. - Retailers can now reduce SG&A and improve store-level profitability by automating repeatable decisions. AI Voice & Translation → Global Expansion Leverage - ElevenLabs and Spotify show that real-time AI voice translation is viable—enabling retailers to localize at scale without human translators. - Big unlock for global DTC growth with minimal operational overhead. AI Infrastructure Is the New Storefront - Companies are rethinking their digital stack: AI copilots, LLMs, and autonomous workflows are becoming embedded in ecommerce platforms. - NVIDIA and others frame AI infrastructure as the next era of retail IT—akin to cloud adoption in the 2010s. CMO + CTO Alignment More Critical Than Ever - 75% of global CMOs are already using or testing generative AI tools. - Success requires alignment across data, content, and experience design to fully activate these capabilities. (Source: BOND)

During my decades in retail, I’ve seen how yesterday’s innovations become today’s customer expectations. Which means I’m always on the lookout for the latest breakthroughs that are poised to become tomorrow’s “table stakes.” Things like omnichannel retailing, price matching, curbside pickup and self-checkout have quickly gone from unique selling points for retailers to baseline expectations for consumers. Here are some emerging technologies I’m watching: 𝗛𝘆𝗽𝗲𝗿-𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 According to Sender.net, 80% of self-identified frequent shoppers say they only buy from businesses that tailor their experiences. Hyper-personalization, driven by advanced data analytics and AI, allows us to offer customized recommendations, promotions, and services. This level of personalization will soon be a fundamental aspect of customer engagement, ensuring each interaction feels unique and relevant. 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗔𝗜) AI is revolutionizing retail, from predictive analytics and inventory management to customer service and personalized marketing. AI-driven chatbots and virtual assistants enhance the shopping experience by providing instant, accurate responses and recommendations. As AI continues to evolve, it will become integral to retail operations as a way of optimizing operations and boosting customer satisfaction. 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗮𝗻𝗱 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 (𝗩𝗥/𝗔𝗥) VR and AR technologies are opening new dimensions in customer engagement. Virtual try-ons, immersive product displays, and interactive store experiences are reshaping how customers interact with brands. These technologies provide a rich, engaging shopping experience that transcends traditional boundaries. Soon, they will be critical elements of the retail landscape. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 I recently checked out at my local Whole Foods using just the palm of my hand. Contactless biometric payments like this are becoming more prevalent. So are digital wallets including cryptocurrencies. These advanced payment methods offer greater convenience, security, and speed, aligning with the expectations of today’s tech-savvy consumers. Adopting these technologies will soon be essential for our customers. 𝗘𝗺𝗯𝗿𝗮𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 The future of retail is unfolding before our eyes, with rapid innovation and ever-evolving customer expectations. As a retail leader, my role has always been to ensure my teams not only adapt to these changes but anticipate them. By integrating today’s breakthroughs and preparing for tomorrow’s advancements, we can continue to deliver exceptional value and experiences to our customers. The key to success lies in our ability to remain agile, innovative, and customer-centric. The future of retail is now, and it’s an exciting journey we’re all embarking on together.

AI for Retail: Turning Omnichannel Chaos into Intelligent Commerce Over the last two years, I’ve helped retail enterprises navigate one of the biggest shifts the industry has ever seen — the move from channel-driven to intelligence-driven commerce. And one thing is clear: AI is no longer a pilot. It’s a performance engine. When done right, AI doesn’t just automate — it orchestrates. It connects marketing, sales, service, logistics, and customer support into one intelligent ecosystem that learns from every interaction. Here’s what we’re seeing across leading retailers 👇 🛒 Virtual Shopping Assistants Provide 24/7 omnichannel support across web, voice, chat, and social. → 35–40% reduction in call-center volume → +28% improvement in CSAT → Response time cut from hours to seconds 📦 Intelligent Order Management Predicts demand, optimizes fulfillment, and prevents stockouts in real time. → 25% improvement in forecast accuracy → 15% reduction in delivery delays → 100% order visibility across channels 💳 Automated Returns & Refunds Streamlines post-purchase experience with AI-led workflows. → 3x faster processing → 67% higher repeat purchase intent → Fraud reduced through anomaly detection 🎯 AI-Driven Marketing Uses real-time data to personalize engagement and automate content at scale. → 10–15% conversion rate increase → 20% lift in average order value → Campaigns optimized automatically based on behavior signals These results don’t come from technology alone. They come from adoption strategy — from helping organizations trust AI enough to use it daily. And that happens when enterprises focus on three fundamentals: 1️⃣ Customer-Centric Design – Make AI invisible but indispensable. Let it enhance journeys, not interrupt them. 2️⃣ Employee Enablement – Train and empower store associates, service reps, and marketing teams to leverage AI insights. 3️⃣ Scalable Frameworks – Start with one use case, prove ROI within weeks, and expand with measurable impact. The real transformation happens when retailers stop asking “What can AI automate?” …and start asking “What can AI help us reimagine?” Because when every interaction — from discovery to delivery — is powered by intelligence, retail doesn’t just grow. It learns. That’s how the future-ready retailers are already outperforming the market. Not through hype. Through measurable value. 💭 In my experience, the retailers that win with AI are the ones who treat it as an enterprise capability — not an experiment. #AIforRetail #OmnichannelAI #RetailTransformation #CustomerExperience #GenerativeAI #DigitalCommerce #KoreAI

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱, 𝗖𝗿𝗮𝗳𝘁 𝗮𝗻𝗱 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗙𝗼𝗿𝗲𝗴𝗿𝗼𝘂𝗻𝗱 I still remember those endless nights in SEPHORA, manually counting thousands of items. That’s why Starbucks’ announcement today resonated so strongly with me. They are rolling out AI-powered automated counting across all their North America coffeehouses: 11k stores. What’s remarkable is the technology mix: 👀 Computer vision to instantly recognize products on shelves. 🔢 3D spatial intelligence to capture placement and quantities. 🪩 Augmented reality overlays guiding partners through the process. 📈 AI analytics that flag low-stock items and will soon automate replenishment orders. The results are striking: ✅ Inventory now counted 8x more frequently. ✅ A process that used to take one hour, now takes minutes. They are reporting a saving of 16,500 hours per week. ✅ Sales people spend less time in the backroom and more time crafting and connecting with customers. Starbucks calls it “technology in the background, craft and connection in the foreground”. And that’s exactly why it matters: technology here is the enabler of efficiency, consistency, and focus on consumer experience. Starbucks is not alone. Walmart with robots scanning shelves, Inditex embedding RFID across its stores, and Amazon Go pioneering frictionless checkout all point to the same truth: the future of retail advantage lies in mastering the invisible backbone of operations. 👉 We’ve moved beyond pilots and “experiments.” AI, AR and computer vision are becoming part of operational infrastructure. Having lived both sides, the manual counts and the promise of automation, I guess this will become the standard for every retailer. #RetailInnovation #AI #AugmentedReality #Operations #CustomerExperience

The numbers don't lie, and they're getting worse. $162.7 BILLION. That's how much retailers are losing annually to in-store inefficiencies—up 27% from last year. The new "State of In-Store Retailing 2025" report from Coresight Research and Simbe just dropped, and the findings should be a wake-up call for every retail leader focused on operational excellence. Here's what the data reveals: → Retailers are losing 5.5% of gross sales to operational inefficiencies (up from 4.5% last year) → Only 20% of retailers have fully scaled store intelligence tech (leaving significant opportunity for the rest) → Autonomous robots are cutting out-of-stocks by 50% at some retailers! → Early adopters of store intelligence technologies are seeing 98%+ shelf availability and 90%+ pricing accuracy improvements The challenge? While 66% of retailers have begun implementing store intelligence technologies, most are still in early phases rather than full deployment. The opportunity gap between early adopters and those still catching up continues to widen. While some retailers are leveraging automation for operational excellence, others are still relying on traditional manual processes. The performance difference is becoming increasingly pronounced. The report shows a 151% surge in planned tech investments—indicating the industry recognizes the urgency, though execution speed varies significantly across organizations. This report is worth your time. The insights on store digitization, AI implementation, and operational transformation could help inform your strategic planning. The question isn't whether stores will become more intelligent—it's how quickly retailers can successfully implement and scale these technologies. The "Dawn of New-Age Stores" represents both a significant challenge and an enormous opportunity for forward-thinking retailers. Get the full report here: https://lnkd.in/gBsAG577 #retail #AI #automation #storeoperations #retailtech #omnichannel

Your retail store is dying. And your website isn't saving you. Between 2019 and 2024, retail margins shrank by 2 to 3 percentage points annually. Some verticals lost 5 to 6 points. McKinsey & Company's data is brutal: digital laggards are being crushed. Digital leaders are generating 3.3 times higher revenue growth. Here's what changed: shoppers stopped choosing between online and offline. They want both, simultaneously, perfectly integrated. And most retailers have no idea how to deliver that. I run an IT company. We build solutions for retailers across three continents. Last month, a CEO told me his conversion rates were collapsing. Premium furniture. Established brand. Loyal customers who suddenly stopped buying online because they couldn't visualize the products at home. We deployed AR visualization. His return rates dropped from 7% to under 2% in 60 days. Conversion jumped 94%. Not because the furniture changed. Because uncertainty disappeared. This is the shift. Gartner projects 80% of retailers will deploy AR by end of 2025. Not as an experiment. As survival infrastructure. The AR/VR market just crossed $100 billion and will double to $200 billion by 2030. But here's what the research from Deloitte and McKinsey & Company reveals: technology adoption means nothing without transformation. Retailers spending millions on AR while running legacy systems from 2010 are burning money. The winners are rewiring their entire tech stack around immersive experiences. Forty percent of shoppers will pay premium prices for products they can test through AR. Over 90% of American consumers are open to AR shopping. Gen Z expects it. They're not impressed by virtual try-ons anymore. They're confused when you don't offer them. The brutal truth: if your customer can't see your product in their space, in their context, in real time, they're buying from someone who makes that possible. The gap between leaders and laggards isn't closing. It's accelerating. The revolution isn't coming. You're already late.

𝐀𝐭 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐬𝐜𝐚𝐥𝐞, 𝐬𝐦𝐚𝐥𝐥 𝐛𝐥𝐢𝐧𝐝 𝐬𝐩𝐨𝐭𝐬 𝐛𝐞𝐜𝐨𝐦𝐞 𝐛𝐢𝐥𝐥𝐢𝐨𝐧-𝐝𝐨𝐥𝐥𝐚𝐫 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬. For Target, January is not a slow start - It’s the launchpad for everything that follows. Now consider this scale: 100K+ SKUs. 2,000 stores. And nearly 𝟓𝟎% 𝐨𝐟 𝐨𝐮𝐭-𝐨𝐟-𝐬𝐭𝐨𝐜𝐤𝐬 not even visible to core systems. When demand, footfall and inventory are forecasted in silos, planning accuracy collapses. Industry-wide, that puts $𝟏𝟎𝟔.𝟔𝐁 𝐢𝐧 𝐚𝐧𝐧𝐮𝐚𝐥 𝐬𝐚𝐥𝐞𝐬 𝐚𝐭 𝐫𝐢𝐬𝐤. This is what changes when AI is applied end-to-end instead of point by point. 𝟓 𝐂𝐨𝐫𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐓𝐚𝐫𝐠𝐞𝐭 𝐟𝐨𝐜𝐮𝐬𝐬𝐞𝐬 𝐨𝐧: ➤ Demand forecasting at SKU level ML models trained on 3+ years of history, weather, and events → 10–20% accuracy improvement vs. traditional methods ➤ Footfall prediction, not guesswork Store-level traffic forecasts tied directly to staffing and inventory → Dynamic workforce allocation and reduced wait times ➤ Real-time inventory ledger Ensemble ML processing 360K transactions per second to detect out-of-stocks as they happen → 4-8% sales lift from immediate inventory correction ➤ Trend intelligence, not lagging reports Generative AI surfaces emerging demand patterns early → Faster buying decisions and fewer markdowns ➤ Personalization at scale AI-driven recommendations and dynamic pricing across app and in-store → 4.3M daily app users and top-8 retail app adoption in the U.S. This only works because planning itself changes. 𝐓𝐡𝐞 𝐟𝐮𝐥𝐥-𝐲𝐞𝐚𝐫 𝐀𝐈 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐜𝐲𝐜𝐥𝐞 → Q1: Strategic targets, market analysis → Q2–Q3: Model training, forecasting, store segmentation → Q4: Deployment across 2,000 stores, inventory and workforce optimization → Ongoing: Real-time corrections, daily retraining, continuous learning 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 ✓ Real-time forecasting vs. annual/quarterly cycles ✓ Integrated system (demand → footfall → inventory → personalization) vs. siloed models ✓ Predictive out-of-stock prevention vs. reactive discovery ✓ Ensemble ML (thousands of models) vs. single-model approaches ✓ Continuous learning (daily retraining) vs. static models 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐨𝐫 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 - Retail AI wins don’t come from better dashboards. They come from architectures that see, decide, and act continuously. When planning becomes anticipatory instead of reactive, AI stops being a cost center and starts compounding value at enterprise scale. The opportunity is no longer theoretical. The question is which part of your planning stack still can’t operate in real time. Where do you see the biggest breakdown today: demand, inventory or execution? ♻️ Repost to help teams understand the different aspects of AI. 🔔 Follow Keith R. Worfolk - MBA, MCIS, CCIO, CISSP, CCISO, CCP for insights on unlocking value with AI & Enterprise Scale #AIinRetail #EnterpriseAI #AgenticAI

It's a relief to me that the conversation around AI is finally transitioning from excitement and hype to more practical applications that make a real difference in retail. And as a huge advocate of store colleagues, I am fascinated to see how AI can work with them, hand in hand to deliver enhanced value for shoppers. That's why it was a pleasure to contribute to the @VoCoVo AI Retail Report recently, where so much focus is given to store colleagues and how tech is enabling them, not replacing them. To me, tech should give store colleagues more information, more solutions and more tools to deliver the best service and experience. For staff, the challenge is how they learn about hundreds of different products in the store, while shoppers can research a product online for days, arriving at the store with detailed knowledge about one item. I often chat with store colleagues and they unanimously agree that relevant, supporting technology makes their lives so much easier. If staff can access information quickly and easily, they can assist shoppers with far more credibility and meaningful help. Likewise, if technology can facilitate service support, such as assessing check out queues and opening more tills or checking product availability, retailers will see financial returns as lost sales or minimised. The VoCoVo report shines a light on the challenges and opportunities within retail and how technology can, and is, offering practical support. This report offers insight and a whole bunch of useful statistics for anyone that is interested or involved in working with store colleagues and technology. You can download the report here: https://lnkd.in/g6u_ZnkC #ISRC #RetailTechnology

Matt Schumer's "Something big is happening" essay last week went viral (and how). In summary, AI advancements will cause a major disruption to white-collar jobs within 1–5 years, exceeding the impact of Covid. And yet the TCS Global Retail Outlook tells a different story. - 85% of retailers haven't begun deploying multi-agent systems.  - 51% say their main AI initiative for 2026 is ad hoc chatbots. TCS calls out the barrier directly: "Misalignment is now a bigger barrier than technology. Retailers are clear on the what, but misaligned on the how." IT, marketing, and merchandising all rank AI priorities differently. No shared vision = no integrated strategy = no path to agentic AI. Every retailer has access to the same AI capabilities, the same vendors, the same models. At this point, capability has exceeded execution. - Knowing which workflows to redesign.  - Who owns the AI-augmented process.  - How C-suite aligns on priorities before building.  - What guardrails exist before you scale. This is where 85% are stuck - waiting to figure out how their organization actually operates differently. The disruption Matt describes is real. But so is the readiness gap. What separates the 15% isn't the technology they chose.  It's the operating model they built.

𝐌𝐨𝐬𝐭 𝐫𝐞𝐭𝐚𝐢𝐥𝐞𝐫𝐬 𝐚𝐫𝐞 𝐢𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐀𝐈 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. But they're treating it like a series of isolated experiments instead of a complete customer journey transformation. The real transformation isn't about making checkout faster or recommendations smarter. It's about the full "𝐑𝐞𝐭𝐚𝐢𝐥 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈" It's about fundamentally rewiring how retail operations think about data across every touchpoint. Here's what separates AI-mature retailers from the rest: They don't optimize individual touchpoints. They redesign the entire value chain. While others chase 24/7 chatbots and virtual try-ons, leading retailers are using AI to predict demand shifts before they happen. They're automating inventory decisions that used to take weeks of analysis. They're detecting fraud patterns invisible to traditional systems. The data is clear: 𝐑𝐞𝐭𝐚𝐢𝐥𝐞𝐫𝐬 𝐮𝐬𝐢𝐧𝐠 𝐀𝐈 𝐚𝐜𝐫𝐨𝐬𝐬 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 19 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞𝐥𝐲 𝐭𝐨 𝐛𝐞 𝐩𝐫𝐨𝐟𝐢𝐭𝐚𝐛𝐥𝐞. But here's the uncomfortable truth most aren't ready to hear: Your customer experience AI is only as good as your operational AI. You can't deliver personalized promotions if your supply chain can't keep the product in stock. You can't offer dynamic pricing if your demand forecasting is still running on gut instinct. 𝐓𝐡𝐞 𝐜𝐚𝐫𝐨𝐮𝐬𝐞𝐥 𝐚𝐭𝐭𝐚𝐜𝐡𝐞𝐝 𝐛𝐫𝐞𝐚𝐤𝐬 𝐝𝐨𝐰𝐧 𝐰𝐡𝐞𝐫𝐞 𝐀𝐈 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐯𝐚𝐥𝐮𝐞 𝐚𝐜𝐫𝐨𝐬𝐬 𝐭𝐡𝐞 𝐫𝐞𝐭𝐚𝐢𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. The journey spans from 𝐖𝐞𝐥𝐜𝐨𝐦𝐢𝐧𝐠 𝐟𝐞𝐞𝐥𝐢𝐧𝐠 & 𝐨𝐦𝐧𝐢𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐭𝐨 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 & 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 Customer-facing applications only work when backend operations are AI-enabled first. We're not talking about incremental improvements anymore. We're talking about a 30-40% reduction in operational time. We're talking about moving from reactive to predictive at every stage. The question isn't whether to adopt AI in retail. It's whether you're building it from the customer backward or from the operation forward. What's your take? Are we overinvesting in flashy customer-facing AI while neglecting the operational foundation? ♻️ Repost if you think this will be useful for your network. follow Vinod Bijlani for more insights