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Retail is as easy, convenient, and enjoyable as ever, with the advancements of artificial intelligence that have reached the satisfaction of several customers and companies alike. With the ability to organize inventory for smoother operations, connect with customers at a faster rate, inform customers on stock availabilities, etc., there is a lot to be said about how AI is being used in efficient ways. Some features of the technology enhancing retail experience includes automation, generative AI, and natural language. These tools assist in the management and strategies for transforming AI with the highest form of data insights collected by generative AI and other analytical tools. Personalization, management, and storage can all be used for increasing the satisfaction of buyers and workers. Customer service has evolved with chatbots offering 24/7 assistance, and inventory management systems can use predictive analytics for optimizing stock levels. Visual search and visual try-on have made shopping more intuitive and engaging.

There is a lot of good to be said about the applications of AI in retail, but there still exists the concern for ethics and bias, especially when AI is being used by companies to get customers to spend their money. On the other side of things, for companies it costs a lot of money to implement, budget, and apply a technology that will need constant updates, maintenance, and an ability to be tailored for the needs of that company.

I chose to discuss the application of AI in retail, because this is an area of technology I interact the most with. Firsthand, I have experienced being a consumer and using websites to purchase products that have AI assistance programmed into their website. While I have had great experiences with quick customer service, I have also had bad ones with websites not working properly, and sometimes the programming being ineffective. This makes me curious about how these technologies will continue evolving to make the retail experience and industry even better.

In my Padlet wall I will include examples of AI usage with specific companies, examine future trends like autonomous stores, address ethical considerations with privacy concerns and algorithmic bias, analyze societal impact such as in the workplace and consumer behavior, and reflect on what I have learned from my extensive research on the topic of retail. AI in retail is changing how we shop, how businesses operate, and how consumer relationships are built.

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Lucy in the Sky is a store that I buy my dresses from! I took this screenshot from their website, because they provide a personal assistant just for you, to assist in what you are looking for!

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Interactive chatbots have made it easier for customers to get all of their shopping need questions answered. There are so many companies and businesses that lose customers due to disappointing experiences on websites and the lack of support. Interactive chatbots are assistants through a message box, allowing for customers to exchange common questions and seek direct help for their specific needs. These chatbots in turn come up with answers or outcomes for the customer to use for their convenience, but the chatbot also collects valuable data from customers that can be used for future business/company decisions. The technology used for these chatbots include machine learning, natural language processing, and generative AI. One of the biggest benefits of interactive chatbots is the quick response time, leading to customer satisfaction. The more information the chatbot has about you, the better they can assist. At the same time, data collection can be seen as a challenge with data privacy. How much information is too much information to collect from a customer that wants a simple question answered? There’s a give and take. AI chatbots have helped both customers and companies alike, and for that I believe it is great! There’s no better feeling than getting an answer to your question in real time, not having to wait for an email back in 3-5 business days. By that point, the odds of revisiting the website are slim, and the chances of seeking the same answers somewhere else are high.

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https://www.amzadvisers.com/the-magic-of-amazon-personalize/ - This image shows the homepage of someone's Amazon account. The arrows point to the specific items recommended to the customer, due to purchase history, and the users demographics.

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AI in Personalization and Customer Insights

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AI in Personalization and Customer Insights

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AI in Personalization and Customer Insights is all about using collected data from both new and loyal customers to optimize their shopping experience with products displayed specifically for them. Stores will use browsing habits, purchase history, demographic data, social media behavior, and responses to previous campaigns. With all of this data, websites are able to transform storefronts for tailored recommendations, including but not limited to, quick search and content generation. The technology used to achieve this includes retrieval augmented generation (RAG) and advanced algorithms. Personalized marketing helps both customers and companies/businesses because stores have reported increased market shares, and consumers do not want to make purchases without this personalization. Placing the right products in the sight of the consumer drives the most engagement and conversion for stores. With algorithms driving what products to display to customers, there’s the limitation of how accurate these recommendations are with the bias that comes with algorithmic configuration. There might also be the misconception of products being “for you” but are really displayed in order to get more purchases off of new or expensive products. I find personalization to benefit stores more than customers, because oftentimes I’ll go on a website with the intention to buy something and end up adding 2-3 more things to my cart that “I also need”. Amazon does a great job of this, where it shows items you may have already bought, and it will ask if you would like to re-purchase. The convenience makes you, let’s say, buy shampoo and conditioner, which you can easily get at any other store in person if you really needed to.

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https://www.prismetric.com/ai-in-retail-industry/ - This image outlines the steps for reaching operational efficiency in retail with AI. It involves a lot of data analytics and consumer insights.

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Operational efficiency in AI refers to real time insights on how the store is performing, in order to make adjustments and improvements in order to gain more customers and shares. AI can play a huge and convenient role in logistic management systems. This includes monitoring inventory, management, staffing, delivery, and more to make a store run efficiently and successfully. Amazon uses a program called Quicksight robust visualization to ask its program questions that give back answers in real time. Machine learning is a key technology in helping with inventory management practices. AI has the ability to track what products will observe higher customer purchases using collected data, and using this same data can eliminate items that are draining profits. Stores are then better able to stock up their shelves, reduce waste, and improve the satisfaction of their customers. Walmart uses another program, Wallaby, that has been trained with years and years of collected data intended to be combined with large language models to tailor the Walmart operating environment. While AI is supposed to be efficient, some detrimental challenges can occur with the potential for over-reliance on automation and how expensive these programs are to implement. Amazon and Walmart are multi-billion-dollar companies; therefore, they are able to implement AI programs that run at the most up to date speed. This is not a luxury all can afford. The maintenance for such programs is costly, and updates are needed so there are no hiccups that can cost stores a lot of money. I find this form of AI highly beneficial for retail, because it makes everyone's lives easier. Awareness on how a store is performing, and what is missing, makes it easier for a company to fill in the missing pieces, never leaving a customer hanging. It’s very rare for both Amazon and Walmart to have an item sold out for more than a week, and that is because they are better informed than other stores to replenish their inventory, staff, etc.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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There’s a lot of room for future development in retail, but there are a lot of things to consider about how ethical these applications truly are. The goal of retail is to showcase to customers the latest most valued products on the market, both online and in stores. The future of AI in retail is sure to include capturing more data and insights of consumer behaviors in order to get customers to stay loyal and favor their stores over others.

Generative AI is key in retail, giving up small tasks to technology, in order for staff to focus on more social-like issues such as relationship building and customer problem-solving. Visibility is another key feature in retail we are sure to see in the future, because customers and stores alike will have more awareness as to what items are available, how smoothly a website or store is running based on web traffic, and even the condition of vehicles that are delivering such items. The personalization and interactiveness of AI will only get better as this technology continues to grow in the retail industry. Operations will become as efficient as ever with advanced data collection for powerful consumer insights, connecting customers with their biggest needs and answers.

The implementation of AI has done wonders in the retail world, but its rapid implementation has raised concerns about privacy and fairness. Some ethical issues that have been raised about AI include algorithmic bias, consumer privacy, data protection and fairness. Many customers that have used AI in retail have felt like AI systems do not treat customers equally, and that there’s a lack of trust with how their data is being used and stored. One of the biggest challenges for retailers to tackle is finding a way to be as transparent as possible; while also maintaining the protection of data they collect from customers. Some studies suggest for retailers to address biases, use the voices of concerned consumers for AI development, and assure customers of the measures taken to uphold data privacy. I myself find data privacy to be a valid area of concern, especially when personalization is so unique and specific to a person. I believe that it’s important to remember not to overshare with chatbots and use them mostly for questions that can be answered with a “yes” or “no”. As for the future of AI in retail, it is proving to be a major positive breakthrough for both consumers and retailers. With AI taking over smaller jobs within retail, there are going to be many more opportunities created to operate and manage these systems, which is both exciting and innovative.

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There are several goals when it comes to the use of AI in retail, and it’s fair to say that it is hard to distinguish who benefits or loses when it is implemented. The shared goals are to captivate customers and for customers to feel engaged, to create an exciting experience and for customers to feel satisfied by it, and to collect as much data from consumers to enhance their wants and needs as time passes. Statistical analysis has shown that retailers implementing AI and machine learning are set to outperform those who do not. A major societal impact that comes from this, is that we may see major gaps in profit from those who have the money to implement such systems and those that cannot afford it.

Employment is affected by the implementation of AI in retail, but it’s not to say that it’s a complete negative impact. While inventory clerks and cashiers may not be needed, there are new roles for data analysis, and customer experience specialists. Workforce retraining and adjustments are needed, and not all workers will be able to acclimate which is a negative, but a positive is that AI is not taking over jobs without giving one in return.

Privacy will continue to be an issue as long as retailers collect the amount of consumer data that they do which goes unprecedented. These privacy concerns consist of facial recognition, tracking the location of customers, and stalking their browsing history. An environment has been created where consumers are constantly being surveilled, and the question becomes, is the convenience of personalization worth the surveillance?

Equity issues exist with algorithmic bias, an area of AI that is prevalent in retail as a lot of it has to do with who the consumer is. Recommendations are many times based on demographics, and this could lead to some consumers being presented with the best items they have, while others are offered budget options based on algorithmic assumptions. I personally have experienced this with buying hair products on Amazon, where I have purchased curl creams and gel before. I have been recommended bonnets and a pick comb after my hair product purchases, intended for the use of Afros, yet my hair is not curly enough for a pick comb to even achieve what it’s used for. I felt like they used my information to classify me into a particular race category, and from there filtered out a universal result for all of those in that same demographic. It didn’t feel as personalized anymore. Due to this, while I think AI can be super helpful and convenient, there are definitely flaws that exist in how the algorithms are built and by who. This type of technology might benefit some over others, and it varies from store to store, person to person.

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From all of the research I have conducted in the use of AI in retail, I have learned so much about the massive impact it has had on both consumers and retailers. A big challenge I faced in my research was finding future trends, and the future direction of AI in customer satisfaction. There’s a fine line between personalization and privacy, which is difficult when considering how the advancement of AI will look like in the future. The best future trend information I could find was the way in which retailers will assure consumers that their data is protected and not being exposed. This is an aspect of AI that is actually super important, and one of the most relevant aspects of future trends to take note of. Many people do not completely trust AI, and for valid reasons. Not all retailers provide the transparency consumers would like in order to trust the systems and programs that are in place. Focusing on how this trust with algorithms could better improve was how I overcame the challenge of finding future trends in this industry. The information I was able to find opened my eyes to be more cautious about the data I share with AI, but I am still super appreciative of the advancements AI has done to the retail industry. There exists a complexity in implementing AI into everyday functions in order to avoid issues such as algorithmic bias, but when done correctly it proves to be very beneficial.

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References

Adanyin, A. (2024, October 27). Ethical AI in Retail: Consumer Privacy and Fairness. European Journal of Computer Science and Information Technology, 12(7), 21-35. https://eajournals.org/ejcsit/wp-content/uploads/sites/21/2024/10/Ethical-AI-in-Retail.pdf [Rationale: This article provided the ethical dilemmas of consumer trust in AI applications. More specifically with data privacy and fairness.]

Fairlie, M. (2024, October 29). The Business of Automation and AI-Powered Retail Predictions. Business.com. Retrieved March 4, 2025, from https://www.business.com/articles/automation-ai-powered-retail-predictions/ [Rationale: Assesses AI’s ability to analyze data for retail and consumer insights.]

Intel. (n.d.). Artificial Intelligence (AI) in Retail. Intel. Retrieved March 13, 2025, from https://www.intel.com/content/www/us/en/learn/ai-in-retail.html#:~:text=drive%20greater%20return.-,AI%20in%20Retail%20Use%20Cases,in%20crowded%20and%20competitive%20spaces. [Rationale: Explains the future trends of AI in retail and its benefits.]

Marotta, D. (n.d.). Artificial Intelligence: How AI is Changing Retail. Hitachi Solutions. Retrieved March 4, 2025, from https://global.hitachi-solutions.com/blog/ai-in-retail/ [Rationale: Explains the way in which AI is socially impacting the lives of many consumers.]

Nagaraja, A. K. (2024, October 22). How Generative AI and Data are Redefining Retail Experiences. AWS Industries. Retrieved March 4, 2025, from https://aws.amazon.com/blogs/industries/how-generative-ai-and-data-are-redefining-retail-experiences/ [Rationale: Amazon quicksight is explained to provide faster issue resolution and an improved customer experience.]

Spencer, A. (2024, April 19). Artificial Intelligence in Retail: 6 Use Cases and Examples. Forbes. Retrieved March 4, 2025, from https://www.forbes.com/sites/sap/2024/04/19/artificial-intelligence-in-retail-6-use-cases-and-examples/ [Rationale: A general idea of how AI is used in the retail industry.]

Walmart Innovation. (2024, October 9). Walmart Reveals Plan for Scaling Artificial Intelligence, Generative AI, Augmented Reality and Immersive Commerce Experiences. Walmart Innovation. Retrieved March 4, 2025, from https://corporate.walmart.com/news/2024/10/09/walmart-reveals-plan-for-scaling-artificial-intelligence-generative-ai-augmented-reality-and-immersive-commerce-experiences [Rationale: Walmart explains their generative AI program Wallaby and other LLMs.]