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Artificial intelligence (AI) was always a concept imagined by people in my generation as something to come after us, because of how elaborately it was portrayed in popular culture. My generation, the last generation to go through a period without the internet, even if it was only for a little while, have seen AI in movies and tv as a means to the end of us. They would start as robot butlers and maids and become weapons turned against us. This concept also existed back in 1967 with the absolutely terrifying short story “I Have No Mouth, and I Must Scream” by Harlan Ellison. Who would have thought AI would just be a smarter computer (a lot of people actually, I just don’t want to admit it.) One of the most surprising industries I’ve seen AI revolutionize is retail! Forbes writes: “80% of retail executives expect their companies will use intelligent automation technologies…” and about 40% already do. My immediate idea was a personal shopping robot, again the idea of the AI butler came to mind, something like the closet in the 1995 film “Clueless”. But AI has so many other uses in retail that will revolutionize the industry almost entirely, with projects ranging from dynamic pricing and fraud protection and my favorite “shopping personalization”! The reason I chose retail as a focus is because, it affects everyone, I would like to research something other than my just main interests so that I can broaden my view. I also find it interesting to imagine a personalized shopping robot following me around Bloomingdale’s.

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Demand Forecasting (Apple, ExxonMobil, BP, Ford, etc.)

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Demand Forecasting (Apple, ExxonMobil, BP, Ford, etc.)

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AI-powered demand forecasting, as seen in SAP’s system, leverages machine learning to analyze past sales data, market conditions, and trends to predict future demand. This technology enhances accuracy, reducing overproduction, waste, and environmental impact, making supply chains more sustainable and cost-efficient. However, challenges include data quality issues, unpredictable external factors (e.g., economic shifts), and reliance on historical patterns that may not capture sudden market changes. I find this fascinating because AI’s ability to anticipate demand could revolutionize industries, but companies must refine models to adapt to unexpected disruptions like pandemics or geopolitical events for long-term reliability.

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Using AI To Help With Demand Forecasting and Product Descriptions

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H&M leverages AI-powered supply chain optimization to enhance efficiency by tracking inventory, analyzing trends, and forecasting demand. The company uses machine learning and natural language processing (NLP) to gather data from search engines and blogs, helping leaders decide what to buy, when to restock, and where to place items. This reduces waste, minimizes lead times, and improves sustainability. Challenges include data accuracy, changing fashion trends, and reliance on AI predictions. I find this fascinating because AI enables real-time adaptability in fast fashion, but ethical concerns arise regarding overproduction and labor conditions, requiring brands to balance efficiency with sustainability.

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Using AI and data for predictive planning and supply chain

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Walmart’s Wallaby is a retail-specific Large Language Model (LLM) trained on decades of company data to enhance customer service and personalization. Using Generative AI (GenAI) and natural language processing (NLP), Walmart’s AI-powered Customer Support Assistant can now recognize customers, take actions like managing returns, and provide contextual responses, improving efficiency and satisfaction. Challenges include maintaining data privacy, potential biases, and AI errors in understanding complex queries. I find this fascinating because AI-driven hyper-personalization can redefine retail experiences, but ensuring ethical AI use and avoiding over-reliance on automation will be crucial for long-term success.

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How Walmart Is Revolutionizing Retail with Generative AI

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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The future of AI in retail is poised for even greater personalization, automation, and efficiency. As machine learning and generative AI continue to evolve, retailers will leverage real-time demand forecasting, hyper-personalized customer experiences, and autonomous supply chain management. AI-powered chatbots and virtual assistants will become more sophisticated, handling complex customer interactions and providing instant, tailored solutions. Computer vision could further enhance in-store shopping by enabling cashier-less checkouts and real-time inventory tracking, a strange adjustment to be made for sure. Additionally, AI-driven analytics will allow companies to predict emerging trends with greater precision, helping retailers stay ahead of consumer preferences. However, these advancements raise ethical concerns. Data privacy is a major issue, as AI systems require vast amounts of customer data to function effectively. Ensuring transparent and responsible AI usage will be essential to maintaining consumer trust. Additionally, there is the risk of biased algorithms, which could lead to unfair pricing or discriminatory decision-making. Over-reliance on AI might also impact jobs, particularly in customer service and logistics, necessitating a balance between automation and human employment. Personally, I find AI’s potential in retail exciting yet slightly concerning. While it offers efficiency and sustainability, ensuring ethical AI practices is crucial. I believe companies must prioritize fair AI development, human oversight, and clear data policies to prevent misuse. The key challenge will be harnessing AI’s power without compromising employment opportunities.

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AI’s impact on retail and society extends beyond profit maximization, affecting employment, privacy, and human interaction in ways that are both promising and unsettling. Demand forecasting and supply chain automation will likely displace traditional jobs in logistics and inventory management, forcing workers to adapt to a rapidly changing labor market. While AI-driven efficiency may lead to lower costs and reduced waste, it also raises questions about job security and economic displacement. Additionally, AI’s reliance on customer data brings serious privacy concerns, as corporations collect and analyze vast amounts of personal information, often without full transparency.

However, what truly stands out is Walmart’s AI-powered chatbot, designed to handle customer interactions with a sense of “humanity” coded into it. This raises profound questions: Where does its compassion come from? If its empathy is artificial, does it teach us that kindness is transactional—a tool for brand loyalty rather than genuine human connection? Over time, could reliance on AI for conflict resolution weaken our own social skills? If millions of Americans interact primarily with AI-powered assistants for customer service, how will that shape our ability to navigate real human disputes?

Personally, I find this shift deeply unsettling. AI’s role in retail is inevitable, but when it replaces human interaction in emotionally charged spaces, it risks reshaping how we relate to each other. The convenience is undeniable, but I worry that in ten years, we may struggle to handle frustration, negotiation, or even basic conversation without AI as our intermediary.

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Researching AI in retail revealed both its efficiency gains and unsettling implications. While demand forecasting and supply chain automation are clear examples of profit-driven AI, Walmart’s chatbot stood out as something different, an attempt to replicate human empathy through code. This raised deeper concerns about how AI might reshape social skills and human interactions over time. One challenge was moving beyond surface-level discussions of AI’s benefits to critically examine its societal consequences. This research has shifted my perspective, making me realize that AI’s impact isn’t just about efficiency, it’s about how we define human connection in an increasingly automated world.

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Ibrahim, M. (2025, January 29). Companies that use SAP: Leading brands achieving success. TechGenies. https://techgenies.com/companies-that-use-sap/#:~:text=With%20SAP’s%20centralized%20data%20platform,and%20adapt%20to%20market%20changes.

Marotta, D. (2024, April 2). Artificial Intelligence: How ai is changing retail. Hitachi Solutions. https://global.hitachi-solutions.com/blog/ai-in-retail/

Artificial Intelligence in retail and improving efficiency: American public university. APU. (2024, May 22). https://www.apu.apus.edu/area-of-study/business-and-management/resources/artificial-intelligence-in-retail-and-improving-efficiency/

Walmart reveals plan for Scaling Artificial Intelligence, Generative AI, augmented reality and immersive commerce experiences. (n.d.). https://corporate.walmart.com/news/2024/10/09/walmart-reveals-plan-for-scaling-artificial-intelligence-generative-ai-augmented-reality-and-immersive-commerce-experiences

Guest, S. (2024, October 24). Artificial Intelligence in retail: 6 use cases and examples. Forbes. https://www.forbes.com/sites/sap/2024/04/19/artificial-intelligence-in-retail-6-use-cases-and-examples/

Law, M. (2025, January 10). Why sap has unveiled AI-Powered Retail Cloud Platform. Technology Magazine. https://technologymagazine.com/articles/why-sap-has-unveiled-ai-powered-retail-cloud-platform

Ai in retail | transforming operations and customer experience | SAP. (n.d.-a). https://www.sap.com/resources/ai-in-retail