- AI use cases in retail
- Automated inventory management
- How AI is being used in retail today
- The impact of artificial intelligence in retail
The application of artificial intelligence in retail is reshaping various facets of the industry, including demand forecasting, optimal pricing, customer experience enhancement, and inventory and supply chain management enhancement. Juniper Research predicts that retailers will spend $7.3 billion on AI by 2022, a significant increase from the $2 billion spent in 2018. This growing trend illustrates the mainstream adoption of AI solutions for retail, previously a unique capability exclusive to market giants.
Let's analyze AI use cases in retail to understand how AI-driven technology is already changing retail and will continue to transform it in the future.
When we discuss AI in retail, the conversation often centers on complex technologies powered by intricate algorithms. However, organizations like Competera have made significant strides toward making artificial intelligence retail solutions understandable and more accessible.
But let's return to AI use in retail. Looking at already implemented use cases is a good way to start a conversation on AI solutions in retail as these use cases show that cutting-edge technologies are tangible and more real than they might appear first. Here are some of the most popular AI use cases in retail:
- Cashier-free stores. You've probably heard of the Amazon Go stores enabling shoppers to make purchases in real-time by simply taking products from shelves and going out of the stores without using cashiers. The unique AI-powered technology automatically takes money for your purchase as you leave the store. It not only means a more convenient shopping experience but also enables the business to optimize and increase the efficacy of the store management process.
- Voice assistants. Even though Alexa is not the only example of an AI-powered voice assistant, it is surely the most vivid one. Now you can just ask Alexa to buy the product you need and specify the order details without typing anything. So far, voice assistant technology is already used by consumers to buy at Tesco, Amazon, Walmart, Kohl’s, Costco, and other stores.
- Chatbots. If voice assistants are still a technology that only a part of consumers regularly uses, chatbots are an example of AI-powered tech that probably each of us has tried. Both on the stores' websites or integrated into retailers' mobile apps, chatbots of different complexity help users to make purchases quickly and easily with no need for managers to get involved every time.
- Pricing. The use cases outlined above were ones helping to enhance the shopping experience of each consumer. But there are also AI use cases that help retailers to optimize processes and improve business performance. Pricing intelligence for retailers is one of the most illustrative examples. AI-powered algorithms can help to predict demand patterns, find product own and cross-elasticities, reveal true competitors stealing a retailer's sales, etc.
IHL Group found that retailers could be losing up to US$1 trillion in sales due to out-of-stocks annually. These numbers show why retailers are eager to use machine-learning algorithms to optimize inventory and fulfillment across online and offline channels. Here's where AI retail solutions shine. They optimize inventory management and improve supply chain efficiency. Comprehensive AI-powered inventory management systems, like those offered by Competera, integrate and analyze data from various sources like cameras, ERP systems, sensors, ensuring the optimal inventory level is consistently maintained. Moreover, AI-driven 'smart shelves' promptly notify users when products are about to run out of stock, leading to improved control over inventory levels.
Another example of AI use in inventory management is the so-called 'smart shelves'. This technology helps businesses to quickly identify the products which are about to run out of stock. Once the critical number of items remains, the AI-powered system immediately notifies the users. The control over the inventory level is also enhanced with automated checkout systems which eliminate the risk of an even when a product turns to be out of stock unexpectedly.
Earlier on we've outlined some of the most vivid use cases of artificial intelligence in retail. Now, let's try to systemize our findings and figure out general trends uncovering the way AI transforms the industry.
- AI for Customer Service. Speaking of the biggest retail trends of the last decade, the personalized shopping experience is worth special mentioning. Powered with AI technology, businesses became able to enhance customer experience tremendously. As already mentioned smart chatbots and voice assistants powered with natural language processing algorithms are the trend's real-life embodiment.
- Predicting demand and understanding consumer behavior. For years, retailers tried to predict the customer's next move and find out how would the demand curve change in response to a particular event. Now, with AI algorithms capable of analyzing large volumes of data, retailers can not only accurately identify in which products would a particular customer be interested the most but also predict the demand even for new market entries.
- AI for loss prevention. Loss prevention is another area where AI use cases in retail are evident. Through predictive analytics and facial recognition, retailers can mitigate losses due to theft, fraud, and other issues. These advanced AI solutions for retail can identify potential vulnerabilities and help in the proactive prevention of potential losses.
- Stores fulfillment and inventory management. As shown above, AI solutions are already being massively adopted by businesses to prevent out-of-stock issues, optimize the supply chain, and reduce the risk of human involvement (which means also fewer chances of mistakes). Transparent real-time inventory management is achieved through combining AI tools with comprehensive machine-to-machine IoT analytics.
- Safety and security. A growing share of e-commerce sales and the overall trend of digitalization made the issue of digital security one of the most important in retail. And this is where AI is also used by businesses to make sure sensitive information is protected. Neural networks can signify potential vulnerabilities in a retailer's ecosystem, prevent money leakages via unsecured payments, identify procurement frauds, etc.
- AI for sustainability. Artificial intelligence in retail also plays a crucial role in promoting sustainability. AI can manage energy-efficient lighting and smart building systems, reducing the environmental impact of retail operations.
The use cases and trends outlined above give a good idea of how AI is already impacting the retail industry. But even though the positive impact of AI technology can hardly be overstated, many fears and stereotypes are still alive among various retail stakeholders. The idea that AI will substitute human workers is a good example showing how the fear of technology prevents retailers from gaining the maximum possible value out of innovations.
Of course, the fear of AI technology can be explained rationally. Indeed, some operations that were always managed by humans are now automated and fully processed by AI-powered solutions. It doesn't mean, however, that AI is about to replace humans. The truth is that AI transforms conventional roles taking away the burden of numerous routine tasks.
The job of a category manager is a perfect example to illustrate the point. Let's look at some essential processes and operations carried out by category managers. These include negotiations with supplies and internal communications with analysts and other managers, as well as market and competitive intelligence, competitor analysis, procurement management, demand planning, and pricing.
Now, let's return to the question: can all of these processes be fully operated by AI? The answer is no. We cannot even imagine an AI-powered solution capable to define the category goals, analyze the market performance, and then negotiate with suppliers to get the required products on the best terms possible.
At the same time, every category manager knows that the strategic processes and tasks mentioned above are just one part of the job. The other one implies gathering, processing, and analyzing large volumes of data which required CatMans to get involved in numerous routine tasks.
And that's where AI can help. When it comes to data analysis, operations automation, and analytics-based decision-making, AI can do the job better than human beings. And this is good news because AI can finally allow category managers (and not only them) to focus on genuinely important processes and strategic goals.
Let's look at the example of pricing as one of the key domains of category management. As the price remains the core factor underlying consumer behavior, many category managers dedicate the major part of their time to price management. In past, it took days to collect the relevant data, analyze it, and adjust category prices based on findings. Now, with AI-driven pricing solutions, CatMans need only to set the repricing parameters, e.g. limitations and business goals (increasing sales volume, gaining extra margin, etc) and the pricing engine will do the rest of the job delivering optimal prices for all products in the portfolio.
The screen above shows the Competera dashboard with an explanation of factors impacting price recommendation and a demand elasticity curve showing how the new price point impacts own product sales and what halo effect it has on other products in the category. And this is a perfect illustration of how AI solutions can save time and help managers make better decisions.
AI in retail helps to improve demand forecasting, generate optimal prices, improve customer experience, and enhance inventory and supply chain management. AI transforms the conventional roles in retail taking away the burden of numerous routine tasks.
AI technology improves the operational effectiveness and performance of both businesses and decision-makers by carrying out comprehensive data analysis, automating operations, and powering data-based decision-making.