Key AI Use Cases in Retail

Operational excellence is a backbone of retail success. Agentic AI bolsters operational efficiency by bringing adaptive automation to everything from supply chains to store operations. Traditional automation follows predefined rules, but agentic AI is different- it adapts on the fly, learning from each interaction and outcome. This adaptability is vital in retail, where conditions change rapidly (think sudden supply disruptions or viral social media trends). We’re already seeing efficiency gains in AI-enabled operations. A recent industry study found that AI-driven “connected retail” solutions dramatically increase operational efficiency, in turn boosting profits while even reducing carbon footprint. For example, AI-driven route optimization in delivery can save fuel and ensure faster deliveries, while AI-based inventory management cuts down overstock and waste. Grocery retailers using AI to fine-tune ordering of fresh products have significantly reduced costly food waste even as they increase profit margins, a double win for business and sustainability. The power of agentic AI is that it doesn’t stop at insights- it sees tasks through to execution. In operations, this means an AI agent might detect an incoming snowstorm (perceive), infer that store foot traffic will drop and online orders will surge (reason), automatically reallocate inventory to the online warehouse and reroute delivery trucks (act), then observe the outcomes to update its storm-response playbook (learn). Each of these steps happens with minimal manual input. In fact, Boston Consulting Group reports that automation with AI can increase revenues by up to 5% in less than a year by finding these kinds of efficiency tweaks across the operation. When every percentage point of margin counts, AI’s ability to continuously fine-tune operations is revolutionary. #artificialintelligence #retailAI #agenticAI

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation

AI is BS. Not the technology. The talk track. I attended the NRF Foundation Big Show this week, and everything was “AI-something.” AI for inventory. AI for pricing. AI for customer service. AI for world peace (okay, maybe not yet). With all that noise, it’s easy to feel overwhelmed—and a bit cynical. The possibilities are incredible, but slapping “AI” on everything doesn’t make it useful. Understanding how these tools work to solve actual business problems is critical. I’ve found it’s helpful to kind of simplify it into the two categories that really matter: ✍️ Generative AI is like an extremely knowledgeable friend who can produce new things—written content, images, & beyond—if asked in just the right way. A chatbot interface makes that generative AI friend more accessible: you give it a prompt (for example, “Write a short product description for a new running shoe”), and it instantly creates a response from all the information it has internalized. 🕵️♀️ Agentic AI goes further. It's more like a proactive personal assistant with the same deep knowledge. Instead of waiting on precise prompts, it can infer tasks and even carry them out automatically. For example, it can figure out when stock is running low & reorder items without being explicitly told every step to take. How retailers might use each: 1️⃣ Generative AI: Product Descriptions: Automatically create rich, engaging product descriptions for online catalogs that match the brand’s voice. Marketing Content: Draft email campaigns, social media copy, & blog posts. Store Layouts & Visuals: Suggest store display ideas or mockups, using AI-generated images to spark new merchandising concepts. 2️⃣ Agentic AI: Inventory Management: Monitor incoming sales data & reorder items proactively before inventory runs out. Customer Service Automation: Act on customer requests (like returns or shipping updates) without a staff member walking it through each step. Dynamic Pricing: Continuously check market trends, competitor prices, and demand patterns, then adjust product prices accordingly—without needing a person to oversee it all. I think Agentic AI will provide the biggest benefits and the biggest disruptions because consumers love convenience and businesses love efficiency – and it delivers both. AI is evolving faster than Moore’s Law—doubling every 3 months instead of 18. Do the math—it’s mind-blowing. Moore’s Law gets you 10X improvement in 5 years. At this pace, AI could be 1,000,000X in 5 years! (h/t Kasey Lobaugh) In just a few years, we could see retail transformed by super-powered sales associates, hyper-personalized shopping journeys, and supply chains optimized to unimaginable levels. But first we have to cut through the noise to make sure we’re making the right choices. Are you experimenting with any tools successfully—or are you overwhelmed by the hype (or both!)? #AI #agenticAI #agents #retail #NRF

AI is not hype. Let's talk about AI productivity gains. Walmart CEO on using AI in their latest earnings: "We've used multiple LLMs to accurately create or improve over 850,000,000 pieces of data in the catalog. Without the use of generative AI, this work would have required nearly 100X the current headcount to complete in the same amount of time" These are some of the use cases he mentioned: 1. Improvement of Product Catalog: Using generative AI to accurately create or improve over 850 million product catalog data pieces. 2. Order Picking: AI assists associates in picking online orders by showing high-quality product packaging images to help them quickly find what they're looking for. 3. AI-Powered Search: Customers and members benefit from AI-powered search on Walmart's app and site. 4. Shopping Assistant: A new AI shopping assistant provides advice and ideas, answering customer questions like "Which TV is best for watching sports?" 5. Follow-up Questions: The AI assistant is being developed to respond to more specific follow-up questions, such as "How's the lighting in the room where you'll place the TV?" 6. Supporting Sellers on Marketplace: AI helps sellers on Walmart’s marketplace by improving their experience and helping them grow their businesses. 7. Testing New Experience for Sellers: A new experience is being tested for U.S.-based sellers that allows them to ask AI anything, focusing on making the selling experience seamless. 8. Summarizing and Answering Queries: The AI assistant provides concise answers to sellers without requiring them to sort through long articles or other materials. The sooner you begin moving quickly, learning, and iterating, the sooner you'll start transforming your business and integrating AI across all operations. Companies that fail to do this will inevitably face disruption.

𝗔𝗜 𝗶𝗻 𝗥𝗲𝘁𝗮𝗶𝗹: 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗮 𝗧𝗿𝗲𝗻𝗱—𝗔 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 AI is reshaping retail from the shelves to the supply chain. It’s not just about automation—it’s about anticipation. 📦 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗶𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 that aligns with real-time demand 🛍️ 𝗛𝘆𝗽𝗲𝗿-𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 powered by behavior analytics 🤖 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗲𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 across chat, voice, and in-store kiosks 💡 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗰𝗿𝗮𝗳𝘁𝗶𝗻𝗴 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 in seconds, not days 📊 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 adapting to market shifts instantly The retailers thriving in 2025 will be those that treat AI not as a tool, but as a strategic co-pilot. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝘀𝗵𝗶𝗳𝘁: 𝗙𝗿𝗼𝗺 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 → 𝘁𝗼 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲, 𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗙𝗿𝗼𝗺 𝗺𝗮𝘀𝘀 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 → 𝘁𝗼 𝟭:𝟭 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 𝗙𝗿𝗼𝗺 𝗴𝘂𝗲𝘀𝘀𝘄𝗼𝗿𝗸 → 𝘁𝗼 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗽𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗶𝗻 𝗲𝘃𝗲𝗿𝘆 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 But success isn’t just about plugging in models. It’s about embedding AI in the retail DNA across people, processes, and platforms.  • Frictionless shopping through cashier less stores powered by AI  • Real-time customer service with AI chatbots available 24/7  • Personalized product recommendations that drive conversion and loyalty  • Smart pricing strategies that adapt dynamically to market demand  • Accurate demand forecasting using predictive analytics, social media insights, and search trends  • Efficient fulfillment systems that adapt to stock changes and optimize order routing  • Smart shelves and carts for a smoother in-store experience  • AI-powered security systems to prevent theft and manage risk  • Sustainable practices using AI to reduce packaging waste and minimize excess inventory Retailers: The question isn’t “should we adopt AI?” It’s “how fast can we scale it responsibly?” Curious to explore how AI can unlock new growth for your brand? Let’s connect. I’d love to share what’s working—and what’s next. Follow Dr. Rishi Kumar for similar insights! ------- 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 - https://lnkd.in/dFtDWPi5 𝗫 - https://x.com/contactrishi 𝗠𝗲𝗱𝗶𝘂𝗺 - https://lnkd.in/d8_f25tH #AI #RetailInnovation #GenerativeAI #CustomerExperience #AIinRetail #DigitalTransformation #RetailTech

AI in Retail: Bridging the Gap Between Ambition & Readiness for Generative AI I recently spoke with Michelle Pacynski, Ulta Beauty & Mike Dupuis, SPARC Group LLC about this topic. Before diving in, I find it helpful to ground the conversation in recent data: 1. According to a recent Forbes estimate, the AI market size is expected to reach $407 billion by 2027. 2. A global Accenture study (July) highlights that: - Companies with strong digital capabilities (data, cloud, security, etc.) are reinventing 2x as many functions with AI & are expected to drive 2x the value creation. - 50% of executives believe they can scale generative AI across their enterprise within 6-12 months, but only 13% are confident they have the right data strategies & digital capabilities to fully leverage GenAI. Why the gap? These insights set the stage for a deeper conversation. Here are some key themes we explored: 1. AI integration frameworks they've adopted: - Assist, Augment, Automate – This framework has helped categorize the 15-20 AI pilots running at any given time. It has especially reduced the pressure to automate everything right away, allowing teams to focus on assistive & augmentative use cases first. 2. Real-world AI implementation examples that required process changes & KPI definition: - Leverage GenAI from your SMS partner to create new audiences & test custom messaging. Process changes included: new workflows for both internal & external teams. KPIs: # of new customers acquired & conversion rates. - Launch a GenAI conversational experience during peak sales to manage case loads, while ensuring real humans are available for more complex queries. Process changes included: customer query routing & escalation procedures, with KPIs focused on # of cases resolved to satisfaction, incremental revenue from case resolutions etc. 3. Keeping up with the evolving world of AI: a) Follow key professors like Ethan Mollick & Stefano Puntoni b) Stay close to the VC community to learn about emerging applications & how people are thinking about the future c) Access to product engineers & innovation through involvement on Boards (e.g., Cordial) d) Attend key tech events like CES and SXSW. 4. How AI can transform retail, from customer experience to back-end operations: - Use weather data to inform inventory buying & planning, ensuring customers get the products they want in the channel they prefer. - The retailer that best uses GenAI applications to develop personalized & on-brand communication with customer segments will create the most value. c) Empower consumers to virtually try on products & learn more about them, both online & in-store. Finally, some questions to consider: - I wish consumers knew AI… - I wish regulations did… - I hope someone develops… There’s no doubt that AI is omnipresent, but achieving meaningful impact requires strong digital strategies, partnerships, and a sharp focus on data governance. #AI #Innovation #Transformation #Leadership

This morning at Amazon Accelerate the focus was firmly on AI. One thing I like about Amazon’s approach is that the use of AI is extremely practical and is squarely linked to improving the customer and seller experience. All too often AI is talked about in very nebulous ways, without proper consideration of the applications. Also, Amazon has been using some form of AI, especially machine learning, for over 25 years. This isn't new for them; but they're embracing the advances. Here are some things I learned… 🤖 Amazon is launching an AI-powered seller assistant called Amelia. It is designed to help sellers across all aspects of their selling journey. Amelia will also help sellers assess performance and recommend improvements. 📋 Producing listings and content is very time consuming for sellers. AI tools are helping to speed and simplify the process; and its making listing more effective by helping sellers identify keywords and search terms that customers use. 👀 Product titles will no longer be static but will be personalized based on individual users and what they’ve searched for. The example was used that if you search for pink aviator sunglasses, AI will ensure that for relevant listings the word pink is included in the title. 🎥 AI tools are already in place to help sellers create images. This is now being extended to video. With one click, sellers can provide a static image of their product, and it will be turned into a relevant video. Video has better conversion rates for selling. ⭐️ Last year, customers left 125 million reviews on Amazon. That’s too much information to sift through manually. AI is helping buyers and sellers by quickly summarizing and pulling out key trends and points from all reviews. 📉 There are a stack of AI tools helping sellers make more sense of various analytics within their businesses. This helps them make more informed decisions and to better forecast the impacts of decisions like advertising more. 👩🏽⚖️ AI is helping sellers to ensure they are compliant with rules and regulations. 📺 Outside of AI, Amazon’s ads now reach 275 million people per month in the US across all channels. #retail #retailnews #AmazonAccelerate #Amazon #AI #ecommerce

The holidays are like the Super Bowl for retailers. And the competition is fierce. Changing customer expectations and shifts in data collection have made it harder than ever for marketers to succeed in a crowded retail space. That’s why I collaborated with Jason Downie, U.S. CEO of Making Science, on these data-driven strategies retailers can use to maximize their earnings this season (and future-proof their marketing). Check out our tips, or catch the link to the full Total Retail article in the comments below.  1️⃣ Put first-party data first With third-party cookies on the decline, prioritizing first-party data is more important than ever. Loyalty programs and mobile apps are great tools for building comprehensive customer profiles (with consent!) and enabling deeper personalization. 2️⃣ Leverage #GenerativeAI Retailers like Walmart are finding tangible ways to leverage generative #AI to improve customer and employee experiences. For example, their “adaptive retail” program harnesses data and large language models to improve their product catalog. This improves customers’ ability to find what they’re looking for. 3️⃣ Predict inventory and pricing needs AI-powered predictive analytics can help forecast demand, align inventory and delivery, and fine-tune pricing strategies in real time. This approach avoids stockouts and rushed pricing changes while improving margins. 4️⃣ Adjust campaigns in real time Holiday campaigns need to adapt quickly. Real-time data helps refine ad spend and messaging on the fly, ensuring campaigns align with customer trends. A centralized #DataCloud paired with AI makes this easier than ever. 5️⃣ Unify cross-channel data Siloed data hurts customer experience. By integrating data across in-store, online, and mobile channels, retailers can deliver a seamless journey and consistent messaging that keeps customers coming back. The future of #retail is data-driven and AI-powered. Those who embrace it now will be better positioned to meet rising expectations and build lasting loyalty—not just this season but all year long.