5 Dec 2022


How AI Can Help Retailers Solve Business Problems

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The global marketplace is currently more integrated than ever before. This situation presents a never-before experienced opportunity for retailers. Multinational organizations whose sole basis is the internet have emerged and flourished, overtaking the traditional global leaders. Even as the internet links more people, algorithmic technology, particularly artificial intelligence (AI), has taken over the processes of data processing that would normally involve complex, near unmanageable humanistic processes. AI is a technology that allows knowledge-fed machines to imitate human actions based predetermined outcome criteria (Bird et al., 2020). AI is the perfect technology for major global retailers to adopt in order to collect and process relevant information regarding their sales needs. For instance, with AI, organizations can collect and process customers’ data in record speeds, and process transactions through AI guided processes.Through AI, organizations can determine the needs of a customer, their purchasing power, use frequency, individual tastes, likes and preferences, and assess time-based shifts in all these factors (Bird et al., 2020). This way, important managerial decisions can be made with regard to product supply, preferred points of collection/ favorite outlets, and possible complementary services or products that can be stocked alongside the product of interest.Despite these tremendous advantages of AI, it remains largely underutilized at the corporate level. For example, Bird et al. (2020) note that the technology is attractive but not readily accessible to small and medium enterprises (SMEs) due to the relative complexity and cost of the technology. Where the cost issue has been partially resolved through alternative systems of access such as Dell’s cloud solutions, a sticking question remains entrepreneurs’ appreciation of the benefits of this technology. These challenges are relevant to this study because regional retailers are showing their willingness to challenge the dominance exerted by larger global retail outfits such as Amazon.com and EBay, yet the regional retailers have lesser resources in terms of AI technology to enhance their competitiveness.

The Problem  

Against the determination of regional retail firms that are determined to grab a sizeable chunk of the market, big retailers are faced with intense competition amongst themselves. Unlike the smaller regional firms, they are competing with some of the best establishedplayers in their specialty. This is the situation that Amazon.com, the world’s most successful ecommerce firm by revenue faces. Top competition is exerted by EBay and JD.com in the ecommerce category and major physical retailers such as Walmart, Tesco, and Kroger. According to U.S. based data firm Statista, the global retail sales for 2020 are estimated to hit $23.36 trillion by the end of the year, with ecommerce sales totaling $4.13 trillion. The ecommerce sales are nearly 20% of the total global retail sales. Against this background, Amazon.comis leading other ecommerce operators to not only advance its sales but to also seek possibilities for taking over some of the market value of the physical retail sector. Data from Statista further shows that ecommerce has gained tremendous popularity among U.S. consumers. As shown in Figure 1 (appendix), online sales are only likely to increase as more consumers take advantage of the non-contact shopping alternative offered by firms like Amazon.com in the midst of the COVID-19 pandemic.In order to retain its competitiveness in the ecommerce sector, Amazon.com needs to seek suitable ways of enhancing its operations to manage even higher operational demands. For instance, it is conceivable that expanding its reach to more customers will require more resources to cater for increased costs of operation. This would compare to physical expansion of a hypermarket’s store space to accommodate projected increase in buyers. How Amazon.com deals with this demand will influence its ability to remain competitive, accessible, and a considered favorite sales point. In respect to these plans, this paper explores ways in which Amazon.com can utilize AI to enhance its capacity to reach a broader customer base, increase its market share in the ecommerce subsector –extending into the entire retail sector, and maintain healthy profit margins amidst ambitious expansionary undertakings.

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Analysis: Using AI to Address Business Problems  

Amazon.com is involved in the entire chain of activities involved in the supply chain, right from manufacturing some of the products they display for sale on their platforms to warehousing, marketing, and selling. These processes are captured in Figure 2 (in the appendix).Scheideler (2019) notes the complexity of the process of assigning suppliers enough slots that are proportional to the AI determined optimal supply levels. To this effect, Amazon.com uses what it calls “Probability Level Demand Forecast”, simply denoted by the letter ‘P’ with a subscript of the probability that the product will be accommodated in the online marketplace.For example, P90 shows that the platform will accommodate the indicated 90% level of demand and 10% chance that it can accommodate higher demand.This helps sellers to determine whether to seek alternative selling avenues for their products. The whole essence of this probabilistic assigned demand and supply protocol is the automated AI guided processes that automatically predict the instantaneous demand levels for individual products. For Amazon.com’s suppliers, their alternative selling avenues include selling to physical stores and other lesser prominent ecommerce channels. For them, this is an undesirable option since the amounts of sales they could make through Amazon.com could be higher than through other alternatives based on the platform’s popularity, but they must also conform to the model of operation at Amazon.com. Throughout this demand and supply level determination, Amazon.com uses AI-fed methods of data fetching and analysis to determine the possible levels of demand as determined by the optimal balance between the two main market forces.

Amazon.com uses AI in a wide range of applications to gain competitive advantage or assert itself in the industry. Amazon.com has been combining artificial intelligence and machine learning to ensure that it identifies the specific needs presented by the huge section of customers using its platforms. Therefore, Amazon.com uses AI to power e-commerce. To fully understand how the elements of AI are applied, it is important to explore the nature of Amazon.com’s industry and the strengths it has. A SWOT analysis of Amazon.com reveals that the company has several benefits over its competitors. Focusing in internet strategic factors, Amazon.com has a very strong brand. It has created trust among buyers and sellers by creating a platform where they can transact for years. The company has an expanding business model. With investment in an expanding world population that has internet access, Amazon.com continues to gain new customers with each new day. Amazon.com has the ability to use more advanced technology due to availability of capital as compared to its rivals. The concept of AI falls in this category. As it will be seen later in the project, Amazon.com has managed to use AI to investigate customer preferences and provide recommendations that fit their interests. The company also works closely with other major players in the tech industry like Google to get data about customer preferences to accurately predict the type of products and services they may buy. Amazon.com Inc. has the strongest brand among online retail market players. It can rely on the brand to invest more in AI and increase its market share. Increased market share translates into increased sales. Amazon.com is involved in four major businesses: Amazon.com web services, the market place, Amazon.com Prime and subscription services. Synergies between the four businesses create a great environment that can be used to predict customer preferences and provide recommendations with high levels of accuracy. However, despite the strengths, Amazon.com has faced a major challenge penetrating emerging markets. It can use AI as a solution to penetrate them. There are several opportunities that can be explored in the current setting. Use of AI to expand in developing markets should be one of the considerations. The company should also consider entering into partnerships with other players using high level AI to target customer preferences and market their products. The company is threatened by aggressive competition from non-online and online companies. Its business model has also been heavily copied by companies targeting small online populations. It is important to use AI to diversify or come up with new business ideas that can help the company stand out from the rest of players in the industry.

Amazon.com uses AI and machine-learning in different business segments. According to Helmold (2018) “over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time both internally and across organizational services offered and used by participants of the value chain.” The description perfectly describes the application of AI at Amazon.com. There are different applications of AI starting with customer engagement to automation of payments and lastly providing customers with an interactive platform where they can give their views or rate the service that has been offered. Most customers using the Amazon.com platform do not interact with humans. They simply interact with automated systems through AI that can prompt the user to continue making their selection and giving more preferences based on their search history or internet activity. Using Cookies, Amazon.com’s AI can also identify the user’s online activity and offer additional preferences. While exploring how companies moved from analytics to AI, Davenport (2018) argues that many elements of AI are still based on data and just like analytics. However, with AI, there are no limitations to the kind of services that can be offered. The business environment continues to interact closely with the customer to easily predict their tastes and factors that determine their purchasing behavior. Although there are several cases where AI fails, it works with high levels of accuracy to identify the specific needs as presented by a given player in a given industry (Karppi&Granata, 2019). On the limits of AI, Karppi & Granata (2019) highlighted a case where Amazon.com’s cloud-based AI assistant Alexa ordered a dollhouse for a six-year old girl by mistake. The rare case was used to explore the limits of AI and also identify gaps that need to be sealed. AI is still work in progress. As more companies roll out their machine learning and AI processes, new gaps are identified and sealed to optimize operations. Algorithmic auditing is used in cases like the one highlighted above to identify gaps in AI systems and propose solutions before subjecting the company into serious problems (Raji & Buolamwini, 2019). Considering that AI is still developing as a field, Amazon.com has made significant steps and continues to be one of the model companies using it to gain competitive advantage in the online retail industry.

When people look at Amazon.com’s fulfillment center, they imagine how data has been used to achieve such major milestones. It is important to explore how Amazon.com uses AI-driven technology across its warehouse footprint to achieve the goals already achieved in its current standing. The team at Amazon.com has leveraged computer vision systems to ensure that they analyze images and provide the most accurate predictions whenever a user is trying to find a product from their website. Product pods at Amazon.com’s warehouses are managed using AI. That Is the only way that the company can address its speed challenge. With the amount of data it manages, it needs swift systems that can respond to queries and guide buyers throughout their purchase journey. Amazon.com ranks ahead of the other retail players due to its flexible and interactive site and a most importantly, the amount of data one can get from the site. Using the four businesses highlighted above, Amazon.com has ensured that it has almost all solutions for a user looking for online products. It has used AI to classify its products and provide them when needed. From warehousing, Amazon.com uses AI to execute shipping options. With millions of products and a huge array of destinations across the world, Amazon.com had no choice but to explore ways to use AI to optimize service delivery. According to Corbato et al. (2016) Amazon.com conducted the “Amazon.com’s Robotics Challenge 2016” with the aim of providing creative teams with a platform and chance to explore new ideas in AI. The winner, Team Delft’s robot involved automating pick and place activities in semi-structured environments (Corbato et al., 2016). Focusing on the challenge, it shows that Amazon.com is constantly exploring new ideas in AI. Pick and place is the major activity in Amazon.com’s operations. By identifying ways in which the process can be optimized, the company continues asserting itself even strongly in the industry. The online retail market has a very promising future. As technology continues connecting the world’s population, the geographical gap that hindered businesses from reaching out to billions of customers in different locations has been addressed. Amazon.com is using AI to reach out to these new populations and provide unparalleled quality of services that offline and online competitors cannot match.

Operational Costs  

Operational costs are a common worry for many organizational managers. One of the visible triggers for operational costs is the human resource element. For instance, using the flywheel, Amazon.com has sustained its AI activities and cut costs for years while edging out its competitors in the industry. The flywheel strategy was employed in all levels of Amazon.com’s supply chain to ensure that machine learning formed the basis of AI. Machine learning while integrated with analytics provides a reliable approach to create an environment where humans can naturally interact with machines. The Flywheel is among the AI technologies and strategies used at Amazon.com to optimize its services. According to Levy (2018) Srikanth Thirumalai, a computer scientist met with Jeff Bezos, Amazon.com CEO in 2014 to explore chances of Thirumalai heading Amazon.com’s recommendations team. Thirumalai had worked at IBM as a computer scientist until 2005. His hiring at Amazon.com was supposed to provide a sweeping approach to technology and mainly AI. Therefore, in the 2014 meeting between Bezos and Thirumalai, the latter explained how the flywheel would be launched. Simply, the flywheel involved a new approach to machine learning that was more engaging and interactive. Alexa, Amazon.com’s online assistant was among the leading projects rolled out under the flywheel. The project was successful and so the company engaged in more vigorous AI activities. Thirumalai also set a clear roadmap that could be followed to achieve the company’s AI goals. AI innovation under the flywheel ensures that every strategy is based on machine learning and new insights based on technological advancement. Amazon.com is therefore almost always ahead of its competitors in AI. AI is also not located at a single department at Amazon.com. It has been applied in all levels of production. It is impossible to think about any unit of production at Amazon.com without exploring how it has used AI to optimize its operations. Kepuska&Bohouta (2018) argues that one of the major goals of AI is realization of natural dialogue between machines and humans. Amazon.com’s Alexa has achieved that goal by creating a platform where humans can engage naturally with machine and have their problems addressed. Presence of such technology ensures that there is no wastage in production. It can be explained as an application of lean and six-sigma production models in the online market. Wastage is reduced by ensuring that every customer seeking services at Amazon.com is served accurately and without wasting time. The chances of the customer buying are higher when their interests are addressed in the shortest time possible. Another problem addressed by AI is the long waiting time that customers had to wait to have their concerns addressed. On top of the FAQ (Frequently asked questions), Amazon.com has developed supportive AI strategies to prompt users state their challenges and have them addressed by their AI system. With each new day, customers present with closely similar challenges that the system can address with ease. Smith et al. (2017) argues that recommender systems have been in place at Amazon.com to help customers identify services and products they have not seen in the past.

Solution and Conclusion  

Amazon uses AI to improve customer experience. The reason Amazon has stood out among other players in the online retail industry is its ability to use AI to help customers in their online purchases. It has incorporated AI strategies such as Alexa in its website to ensure that human services are not needed to serve millions of customers at any given time. AI has reduced the costs of operation at Amazon. By replacing human capital with AI, it is easier predicting the quality of services that can be offered and correcting emerging issues as they present. AI is used to leverage customer-centric innovations at Amazon. Over the years, Amazon has redesigned its business strategy and model to focus more on machine learning and AI applications. Since 2016, the company has also been exploring new ideas in robotics to incorporate them in its operations. Although Jeff Bezos has steered the company to its current position globally, there is urgent need to sustain its performance by investing more in AI. Morgan (2017) notes that Amazon.com’s greatest win in its AI journey is by distributing the strategies to all levels of production. In its current application, Amazon uses AI to link up suppliers and retain high levels of trust with all parties involved. Unlike other online retailers whose operations fail due to cases of fraud and mistrust, Amazon has managed to use AI even in its financial activities to provide its users with high-quality services. AI is used to increase security while using Amazon.com’s platforms. Bank fraud is a common problem when using online marketplaces. Many people avoid linking their cards due to the fear of losing their hard-earned money to fraudsters. Amazon has used AI to develop a fool-proof system that is hard for hackers to access so that they can access customer data to facilitate their activities. Lastly, Amazon.com is using AI to understand customer quizzes better. It has a recommender system based on customer quizzes that solely relies on AI to identify quizzing trends among users to inform their purchase trends.


Corbato, C. H., Bharatheesga, M., Van Egmond, J., Ju, J. &Wisse, M. (2018). Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016. IEEE Transactions on Industrial Informatics , 14, (11), 4916-4926, https://doi.org/10.1109/TII.2018.2800744 .

Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1–8. https://doi.org/10.1080/2573234x.2018.1543535   

Helmold, M. (2019). Industry 4.0 and Artificial Intelligence (AI) in PM. Progress in Performance Management, 161–163. https://doi.org/10.1007/978-3-030-20534-8_13   

Karppi, T., Granata, Y. (2019). Non-artificial non-intelligence: Amazon’s Alexa and the frictions of AI. AI & Soc iety, 34, 867–876 https://doi.org/10.1007/s00146-019-00896-w 

Këpuska, K. &Bohouta, G. Next-generation of virtual personal assistants (Microsoft Cortana, Apple Siri, Amazon Alexa and Google Home). 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC) , Las Vegas, NV, 99-103, https://doi.prg/10.1109/CCWC.2018.8301638 .

Levy, S. (2018). Inside Amazon’s artificial intelligence flywheel. WIRED. https://www.wired.com/story/amazon-artificial-intelligence-flywheel/ 

Morgan, B. (2018). How Amazon has reorganized around artificial intelligence and machine learning. Forbes. https://www.forbes.com/sites/blakemorgan/2018/07/16/how-amazon-has-re-organized-around-artificial-intelligence-and-machine-learning/#6fe1dced7361 

Raji, I. D. &Boulamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. AIES '19: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 429-435. https://doi.org/10.1145/3306618.3314244 

Smith, B. & Linden, G. (2017). Two Decades of Recommender Systems at Amazon.com. IEEE Internet Computing , 21,(3), 12-18, https://doi.org/10.1109/MIC.2017.72 

U.S. quarterly e-commerce sales since 2009. Source: https://www.statista.com/statistics/187443/quarterly-e-commerce-sales-in-the-the-us/ .  

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