Strategies to Maximize Generative AI in Retail

💯 No fluff, no exaggeration - just cold, hard facts from Walmart's latest earnings call on tangible use of GenAI. The message is clear: GenAI isn't just a fancy toy. It is a business transformer. To me, he dropped a bombshell against all the critics that undermine the enterprise value from GenAI. He drops this mind-blowing stats 🤯 : "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" Let that sink in - 100X productivity boost. Not 10%, not 2X…. but 100X 🤯 Here is how they are integrating GenAI across their entire operation: - Supercharged Product Catalog: 850 million data points improved. - Smarter Order Picking: AI shows associates high-quality images for faster item location. Time is money, especially in logistics. - AI-Powered Search: Because who has time to scroll through pages of results? - Personal Shopping Assistant: "Which TV is best for watching sports?" AI's got your back. - Contextual Follow-ups: "How's the lighting in your TV room?" AI that thinks ahead. - Empowering Marketplace Sellers: AI helping small businesses thrive on Walmart's platform. - Ask Me Anything for Sellers: A frictionless selling experience powered by AI. - Instant, Accurate Answers: No more digging through FAQs. AI summarizes and delivers. My key takeaway/ considerations: - focus on the right areas and - embrace the power of iteration - go for holistic ai strategy with ecosystem approach - master human-ai collaboration - value in customer-centric innovation - quantifiable performance measurement Well, the clock is ticking ⏰ - there is a huge opportunity for individuals and enterprises to create exponential value from continually learning and iterating using GenAI. Because here is the harsh truth from the crumbs of evidence that is starting to mount: in the ai-driven world, you’re either the disrupter or disrupted! #artificialintelligence

McKinsey & Company: "𝗧𝗵𝗮𝘁'𝘀 𝗛𝗼𝘄 𝗖𝗜𝗢𝘀 𝗮𝗻𝗱 𝗖𝗧𝗢𝘀 𝗖𝗮𝗻 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗜𝗺𝗽𝗮𝗰𝘁" This McKinsey & Co report highlights how #GenAI, when deeply integrated, can revolutionize business operations. I took a stab at CPG eCommerce use case below, and thriving with generative #AI isn’t about just deploying a model; it demands a deep integration into your enterprise stack. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗖𝗣𝗚⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, browses personalized product recommendations, and either finalizes a purchase or escalates to a support agent—all seamlessly without grasping the backend processes. This layer prioritizes trust, rapid responses, and tailored suggestions like skincare routines based on user preferences. 📍Business Impact: Boosts customer satisfaction and loyalty, increasing conversion rates by up to 40% through hyper-personalized interactions that drive repeat purchases. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Oversees user engagement: - Chatbot launches and steers the dialogue, suggesting complementary products - Escalation to a human agent activates if AI can't fully address complex queries, like ingredient allergies 📍Business Impact: Enhances efficiency in consumer support, reducing resolution times and operational costs while minimizing cart abandonment in #eCommerce flows. 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Performs smart actions using context: - Retrieves user profile data - Validates promotions and inventory - Creates customized options, such as virtual try-ons - Advances the process, like adding to the cart 📍Business Impact: Accelerates innovation in product discovery, lifting marketing productivity by 10-40% and enabling dynamic pricing that optimizes revenue in competitive #FMCG markets. 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Links AI to essential enterprise platforms: - User verification and access management - Promotion rules and order processing - Support agent routing algorithms 📍Business Impact: Streamlines supply chain and sales workflows, cutting technical debt by 20-40% and improving inventory accuracy to reduce stockouts and overstock costs. 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Delivers instant contextual details: - Consumer profiles - Purchase records - Promotion guidelines - Support team directories 📍Business Impact: Powers precise AI insights, enhancing demand forecasting and personalization to minimize waste in perishable goods while boosting overall data-driven decision-making. 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Supports scalability, efficiency, and oversight: - Cloud or hybrid setups - AI model coordination - High-speed response handling - Privacy and compliance controls 📍Business Impact: Ensures robust, secure operations at scale, unlocking value by optimizing resource use, slashing IT ops costs.

In this persuasive HBR article, Ian G. and Lisa Earle McLeod, make the case for changing the mode in which the sales professionals reach out to prospective buyers and to existing customers. They argue that Gen AI can help - 1) understand the client’s major challenges and goals 2) identify the key metrics associated with those challenges 3) find the client stakeholder who cares about those metrics 4) assess how your product can improve those client metrics 5) open your interactions with clear, concise, and relevant messaging that ties all together While each B2B complex sales scenario is unique, there are some commonalities that can be learnt from past experiences (successful or unsuccessful) of such pursuits within a company. Many times the pursuit information is codified in interactions between the deal team members, in meeting minutes, and in the eventual proposal, presentation, response etc. that was made to the customer. And yet, a much more richer perspective is stored in the "tribal knowledge" of the organization. Among the many recommendations to benefit from this tribal knowledge would be: 1. Build an internal community or group of sales / pre-sales / business analysts etc. who have worked on such deals in the past. Whenever there is a new pursuit, the current deal team members post their questions to this group and the eventual interactions/knowledge is captured and used by the Gen AI models for future pursuits 2. A conscious effort is made to document new "tribal knowledge" being generated - this might take the form of transcribing meetings, daily post-its / reflections from deal team members, auto-capturing different sources of data used to understand the client context, and tagging all communications 3. Leveraging data in CRM systems like Salesforce 4. Using CoPilot etc. to understand how the tone/message to and from the customer is changing over the deal lifecycle In all, Gen AI as a tool is a great enabler to human ingenuity! https://lnkd.in/gvr3W6kM

𝗖𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗖𝗼𝗱𝗲 𝘁𝗼 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 & 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 The journey from AI idea to impact isn’t a straight line. It's a structured process that combines vision, business alignment, and smart execution. Too often, AI initiatives fail—not due to poor technology, but because they lack strategic grounding and cross-functional alignment. That’s why having a clear two-phase framework can be a game-changer. Here’s how leading teams build scalable and high-impact AI products: 𝟭) 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗣𝗵𝗮𝘀𝗲  • 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗡𝗲𝗲𝗱 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 – Start by identifying real pain points and opportunities. AI should solve a specific problem.  • 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – Not all use cases are created equal. Rank them based on feasibility, impact, and ROI potential.  • 𝗥𝗢𝗜 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – Set clear, quantifiable success metrics. Know what "success" looks like before development begins. 𝟮) 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗣𝗵𝗮𝘀𝗲  • 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 – Pick the right platforms and tools that integrate well with your existing stack.  • 𝗠𝗩𝗣 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 – Move fast, test often. Build lean prototypes, gather feedback, and iterate rapidly.  • 𝗦𝗰𝗮𝗹𝗲𝗱 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 – Once validated, roll out your solution across all relevant channels and touchpoints. 𝗥𝗲𝘁𝗮𝗶𝗹 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗔𝗿𝗲 𝗔𝗹𝗿𝗲𝗮𝗱𝘆 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝘀𝘂𝗹𝘁𝘀  • 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗔𝗴𝗲𝗻𝘁𝘀 – Provide 24/7 personalized support to reduce wait times and improve satisfaction.  • 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – Minimize stockouts and overstocks, reducing waste and saving money.  • 𝗣𝗿𝗶𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – Adjust pricing dynamically based on demand, competition, and market signals.  • 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝗲𝗮𝗿𝗰𝗵 & 𝗔𝗜 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 – Enable intuitive, image-based product search experiences.  • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 – Use smart recommendations to increase engagement and conversions.  • 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 – Implement real-time transaction monitoring to flag potential threats early. What’s the outcome of doing this right?  • Increased Revenue  • Reduced Operational Costs  • Higher Customer Satisfaction  • Greater Efficiency at Scale AI isn’t just about automation—it’s about creating smarter, faster, and more personalized experiences that deliver measurable business value. If you're planning to integrate AI into your roadmap, this strategic model is a great starting point. Follow Dr. Rishi Kumar for similar insights! ------- LinkedIn - https://lnkd.in/dFtDWPi5 X - https://x.com/contactrishi Medium - https://lnkd.in/d8_f25tH