By its nature, the retail sphere is one of the first to implement novelty technologies, so when speaking about AI in retail, we refer to ongoing developments rather than describing pioneering innovations. Online stores have long been using smart suggestions and AI in planning, demand forecasting, customer services, and other instances.
At the same time, advancements in AI offer new possibilities for offline retail, such as stuffing physical stores with novelty devices to enhance customer journeys. Yet, implementing AI in the retail industry is a complex endeavor requiring 360-degree literacy in the topic.
In this article, we will share the must-know practical insights about building AI applications for retail businesses and try to illuminate the opportunities of AI in retail.
Let’s get started!
Application of AI in The Retail Industry: Benefits and Challenges
The application of artificial intelligence in retail offers numerous benefits that naturally come with automation and better insights in the inner and outer processes. Specifically for retail, the significant role goes to customer satisfaction, as AI broadens customers’ options and freedom of choice.
The 3 most important advantages of artificial intelligence in the retail industry to consider are as follows:
- Captivating customers: retailers compete in crafting exciting and innovative solutions for their clients to interact with them on all the touchpoints of their shopping journey. The implementation of AI in retail provides them with the necessary tools.
- Getting insights from disparate data: AI helps transform disparate data into a valuable source of insights that further help improve services and operations.
- Building flexible supply chains: with better visibility and the ability to drive insights from various data sources, retailers get the option to reorganize supply chains to maximize benefits and better cater to customer demand.
At the same time, the implementation of artificial intelligence in retail software development comes with challenges. One of the most pressing issues is the necessity to integrate novelty AI solutions into legacy systems, which are quite often outdated. Also, some businesses already use separate AI solutions and require building bridges between the existing applications to avoid data silos.
Among other things, that create some level of friction to overcome are:
- Responsible AI: the use of AI in the retail industry poses security and privacy risks, which are well-known to consumers as well. As people are growing more concerned about how their personal data is used, AI applications in the retail industry should consider transparency, accountability, and governance principles.
- Customer tolerance limits: digital tracking is the fuel for training AI models, yet customer tolerance limits may be crossed at some point, so that customers feel spied on, rather than pampered with attention. Therefore, tracking customer sentiment about AI initiatives remains ever important.
- Organizational investment: to run AI initiatives successfully, preparing company staff is as important as implementing cutting-edge AI software.
TOP 7 AI Use Cases in Retail: from Operations to Customer Experience
The examples of artificial intelligence in retail grow in place with the advancements of technology itself. This way, achievements in natural language processing, text-to-speech, speech-to-text technologies, and computer vision, provide the basis for building online and offline instruments that cater to frictionless customer journeys, personalization, and further data collection for smart business decisions. Alongside handy chatbots and virtual try-on solutions accompanying the online shopping journey, offline stores implement AI-based self-checkout stations and magic mirrors or glasses, allowing customers to virtually test tons of products.
As well, advancements in analytical AI provide retailers with better and more accurate instruments for demand and sales forecasting, which provides a basis for smarter planning, cost cutting and higher revenues.
Let’s review some of the use cases of AI in retail in greater detail.
1. AI Automated Self-checkout
Automated checkout technology eliminates crowds at checkout stations and enhances the overall customer experience. The introduction of AI makes these systems even smarter, allowing them to reduce fraud and introduce some robotic features, like voice chatting with customers.
Most often, a customer’s self-checkout journey is possibly enhanced thanks to computer vision, speech recognition, and analytical AI.
- Image recognition allows for better product identification and reduces scanning mistakes. It also helps spot theft attempts.
- Voice recognition provides for question-and-answer sessions with customers, reducing the need for human personnel to step in.
- Analytic AI allows for tracking inventory in real time, gathering information on product use, customer loads, seasonal trends etc. In other words, it supplies store owners with valuable insights into customer behavior and product popularity.
This way, AI automated checkout serves multiple purposes, yet it also poses some development and financial challenges.
To reduce them, it is recommended to start with simple self-checkout solutions, like vending machines. They can be placed in different locations inside or outside the store and take over a bunch of operations from conventional checkout stations, such as selling drinks or snacks. Powered with a QR code scanner, and an AI camera, they operate purchase processes at the spot.
2. AI Analytics & Demand Forecasting
Demand forecasting has always been a challenge for SMBs, as it depends on too many factors to make predictions that are correct. The implementation of artificial intelligence in the retail industry changes it all.
Previously, demand forecasting used statistical and analytical computing of historical data to predict future trends. AI brings current trends, customer behavior, and external influences into the equation. Also, earlier models were based on correlations between data, while the rise of causal models allows for establishing real causes of demand drivers.
Now, a typical store owner uses a tool that collects weather forecasts, product use trends, historical information of customer interactions with a brand, as well as social media information of events in progress and gets accurate demand predictions at the outcome. That is all possible thanks to AI demand forecasting.
By implementing AI analysis and demand forecasting, businesses have better visibility into real demand drivers and can plan accordingly. As a result, the likelihood of overstocking or stockouts is minimized.
Arriving at the best demand forecasting use case is challenging yet manageable with the right implementation approach. To maximize the quality of forecasts, it’s important to prepare the data before. Often, data collected and stored by companies is not perfect and requires cleaning, analysis for gaps and anomalies, relevance check and restoration. That’s why, to build a powerful AI demand forecasting tool, businesses start with the help of data science consultants.
3. AI Price Optimization
Price optimization uses mathematical analysis to calculate how customers will react to different product prices. AI brings more data into this equation, thus making price suggestions more accurate and timely.
This helps business owners build pricing strategies based on seasonal trends, competitor behavior, processes automation, consumer behavior and state of economy. AI helps strike a balance between setting consumer-friendly pricing and maximizing profits.
AI price optimization can be developed as a standalone product, yet often, it is an element of AI POS systems, which also include other AI perks, such as product recommendations, advanced analytics, demand and sales forecasting, and inventory management.
Building such a complex system is also challenging, yet, as usual, it starts with collecting high-quality data. To build an AI price optimization tool successfully, it is recommended to start compiling historical sales figures, and customer shopping behavior. Incorporating competitor pricing strategies into this database will be the next step. At the next step, the compiled data needs to be refined, organized, and structured with the help of such techniques as cleaning, transformation, aggregation, sampling, filtering, and deduplication.
4. Automated Inventory Management
The implementation of AI in retail offers business strategic control over inventory in the condition of complex supply chains and a growing adoption of warehouse automation.
Amazon and FedEx already use warehouse robots and AI to fine-tune inventory levels. This sets high standards urging other industry players to start their own AI app development.
A typical AI solution for automated inventory management uses real-time information from warehouses equipped with scanners and cameras, historical inventory trends, and supply chain data to craft accurate recommendations. Results come as the reduction of understocking, smarter planning, faster orders, and revenue increases.
On the warehouse floor, the use of AI in the retail industry substitutes reliance on slow-to-manage spreadsheets with handy dashboards offering real-time insights. That guarantees additional benefits in terms of faster operations.
Yet, at the point of implementation of automated inventory management, businesses suffer numerous challenges. Most often, SMBs already have some point AI solutions, but don’t have them running as a connected and fully automated system.
So, to build an automated inventory management system for your business it is traditionally recommended to start with data. Historical and third-party data should be of good quality and free from duplicates or incorrect information.
Usually, AI-based inventory management is integrated into existing systems like ERP or CRM. So, it is important to ensure that a new AI tool and an in-house system talk well to each other through APIs at the stage of development, to avoid issues in the future.
To cater to your specific needs, it is important to select a platform that supports custom model development – general solutions are not always enough, though they are good.
To make sure the model gives correct recommendations, it is necessary to test it on small product ranges or some pilot stores before going on a large scale.
5. AI Product Recommendations
Smart product recommendation is where AI in retail started. Offering customers exactly what they want has long been a dream for retailers, so, no wonder product recommendations systems pioneered as a playground for AI models training.
In the result, we have systems that provide more personalized and accurate suggestions to users, and it’s worth noting that this feature is not more a perk for retail businesses, rather a necessity.
To build an AI product recommendation system, a business needs to collect data on customer behavior. Things like purchase history, browsing history, ratings and reviews, and search queries. The tool may also need data from other retailers, seasonal or weather specifics, or even some web scraping. For example, the successful release of “Barbie” sent millions of teenagers to buy pink stuff, and in case such a situation repeats (it always does), it is necessary that your AI system is ready to respond to trends.
The collected data will run through various algorithms, such as statistics analysis, supervised and unsupervised machine learning, and also natural language processing. The latter will be helpful to understand descriptive customer queries and respond with the right suggestions.
It is also worth mentioning that it is not possible to build a one-size-fits-all recommendation system, especially if a retail network has multiple stores, each catering to different customer demographics and shopping behavior. In this regard, it may be necessary to utilize some store-specific data, like local customer behavior, inventory, and promotions, to build an effective recommendation system on the store level.
6. Virtual Try-on Solutions
Virtual try-on solutions help customers make more confident shopping decisions by providing realistic visualizations of how a product will look on them when tried on. Starting as Augmented Reality technology, virtual try-on solutions benefited greatly from the implementation of AI in retail.
For example, augmented reality is enough for simple virtual try on tasks like fitting hats. In cases of cosmetic testing though, AI technology is needed for correct segmentation of skin and hair or natural-looking cosmetics distribution. AI enhances virtual try on applications with body movement tracking, making experiences super-realistic.
Besides the described cases, AI technology is also used to create virtual fitting rooms. The principle of this technology lies in the overlapping of the video feed of a shopper with a 3D model of an item to buy. AI here enhances the quality of video rendering with the benefits granted by computer vision.This way, live video with a virtual object gets more realistic.
It’s worth mentioning that the amalgamation of AR with AI facilitates effortless buyer experimentation both on the offline and online stages. While online stores make use of phone cameras and screens, physical store’s devices, like smart mirrors or big digital screens, allow users to try on a versatile array of products – from cosmetics and accessories to shoes. For example, Walmart allows its users to test over 500 samples of their hair color palette virtually. Such technology solves one of the most pressing challenges of in-store retail – simplifying customer journeys and adding interactive elements to offline shopping.
As for actual virtual try-on development, it is important to remember that various tasks require various resources. Some features can be developed with AR frameworks like ARKit and ARCore, which suffice for the simplest facial recognition architecture. More complex tasks, such as testing different hair color types will need AI implementation, that will provide the project with necessary calculations.
In general, the process of building a virtual try-on solution requires the following 6 stages:
- Planning: specifying project scope and objectives
- Functionality: deciding on the features to be included in the product
- UX/UI design: creating the user-friendly interface of the future product
- Development: actual product creation
- Deployment: going live with the solution
- Support and maintenance: running updates and fixing issues if there appear any.
To conclude this section, it’s worth mentioning that virtual try-on solutions vary in kind and features. An interesting example to learn from is MobiDev’s virtual try-on glasses. More information about this solution is presented in the video below:
7. AI Chatbots
AI chatbots for retail, or retail bots, are digital assistants assisting customers alongside their shopping journey. They can be deployed on websites, mobile apps or social media websites, but they also have a place in offline retail, when applied on self-checkout stations.
AI chatbots, as a brilliant example of AI in retail, give benefits to both retailers and consumers.
Consumers get round-the-clock support, smart product recommendations, and convenient order management. Moreover, they take over the burden of returns and exchanges, by cutting these stressful procedures to several clicks.
Retailers benefit from enhanced customer insights that fuel further analytics, get more prepared for peak shopping seasons when the demand in support shoots up, and reduce costs by automating repetitive tasks, like answering FAQs and increased sales and conversions from up and cross-selling sessions.
AI chatbot development utilizes generative AI, speech recognition and natural language processing, depending on how they interact with customers. Text-based chatbots, for example, utilize natural language processing or generative AI to communicate though texting inside an app. Voice-based chatbots use speech recognition and text-to-speech technology to communicate via spoken commands and generate voice answers.
While chatbot development was confined to big businesses previously, today, with the abundance of tailored platforms for chatbot creation, they are more widely available. Yet, while selecting the best platform to develop a chatbot, it is necessary to consider not only current needs but also scaling possibilities. For businesses making the first steps in the domain of chatbots, simple tools may suffice. Yet, the development of personalized chatbots requires AI platforms that include NLP and machine learning in their offering.
The Future of Artificial Intelligence in The Retail Industry in 2025
The implementation of artificial intelligence in the retail sector will grow alongside the development of new technologies. Since computer vision-based object detection enhances in-store cameras and self-checkout stations, and speech-to-text technology allows conversations with virtual shopping assistants, we may expect even more stunning implementations of this technology. This is also proven by numbers: according to Grand View Research, in the next few years, artificial intelligence in the retail market will grow by 23% yearly, prompting a wider implementation of new AI use cases.
To remain competitive, retail businesses will have to prioritize productivity and automation, as well as the ability to respond fast to changing consumer demands.
Why Build Your AI-driven Retail Product with MobiDev
If you’re looking for guidance on how to build an AI-driven retail software product, MobiDev offers a comprehensive range of services, from expert AI consulting to full-scale development, supporting clients through every stage of the process. Whether integrating AI into existing systems or building a solution from scratch, MobiDev’s team works closely with clients to deliver highly effective AI systems.
Since 2013, MobiDev has been trusted by big POS providers like Comcash (acquired by POS Nation) and SmartTab for retail software development services. We’ve also provided consulting and engineering services for retail businesses and helped them achieve their unique business goals.
To explore how MobiDev can assist with your retail software development, reach out to our expert team.
