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AI in Retail: How Artificial Intelligence is Personalising Shopping Experience

AI in Retail: How Artificial Intelligence is Personalising Shopping Experience

Discover how AI in Retail is revolutionizing shopping with personalized experiences, improved customer engagement, and efficient inventory management for optimal operations.

Nethra Ramani Author
Sharjeel Ahmed
CEO - Pazo

Artificial intelligence has revolutionised every conceivable industry on earth, and the retail industry is no exception. Support for AI in retail seems to be growing as it helps optimise various operations, easing the burden on every stakeholder. A whopping 80% of retail executives expect their brands to implement AI-powered automation by 2027. The foremost outcome of using AI in retail is room for increased personalisation of shopping experiences.

From using AI analytics to better serve customer touchpoints and AI software in inventory and supply chain management to deploying interactive virtual systems, the future of AI in retail holds immense potential. In this connection, every retailer must note the ways AI is shaping the upcoming shopping experience. Let's discuss it in this blog post.

Higher Customer Engagement

One of AI's most significant achievements in retail is its capability to improve customer engagement. Combining AI and ML systems helps gain insights into customer personas, behaviour and preferences. Businesses have a better view of what their customers want and what makes them buy twice. Using the data, retailers can design targeted marketing campaigns and curated content like product recommendations, offers, advertisements and more. According to Accenture, 91% of customers purchase from brands that offer meaningful and relevant offers.

Customised Shopping Experience

Online shopping requires higher customer engagement, which can be enhanced by providing stunning consumer experiences. For example, a seamless product catalogue based on customer behaviour, suggesting curated offers, interactive ecommerce page design, etc.

AI in retail statistics suggests a strong correlation between personalised customer experience and brand loyalty.

Analysing purchasing patterns, customer behaviour, and retail transactional data is useful in customising the customer experience further. One example of customer-oriented AI in retail is Sephora's Colour IQ. It analyses customers' skin tones to offer a range of suitable cosmetic products.

Enhanced In-Store Experiences

AI is also improving the online shopping experience. But the future of AI in retail is to enrich in-store experiences also. Take mirror technology, for example. There are intelligent mirrors that use AI to suggest complementary products based on customer interactions. Similarly, AI-powered virtual assistants are helpful in suggesting tailored stylisation for individual customers. In fact, more than half of the customers agree that AI-powered virtual assistants make life easier. Adopting them one or the other way is promising.

AI in retail operations can also replace sales personnel to assist customers in store, help with billing to improve speed and accuracy and reduce jamming at peak hours. Lately, there are robotic systems to replenish stock levels as they're finished.

Identifying Non-Scans

As surprising as it may sound, retailers lose $45 billion every year due to discrepancies in checkouts. While direct non-scans contribute significantly, checkout scams also have a role to play. To prevent such mishaps at product checkouts, there are systems of AI in retail. They can identify non-scans with the help of video feeds of all the products. By identifying the unscanned products and informing the staff in real time, retailers can minimise checkout errors and count their profits.

MAP Analysis

The Minimum Advertised Price (MAP) is the minimum price retailers can use to advertise their products. It is used to promote healthy competition among retailers in the market. One of the best applications of AI in retail is facilitating MAP analysis. While brands depend on retailers for selling their products, they have to ensure that the retailers uphold the minimum advertised price, as it adversely affects profits. Though retailers can sell products below the MAP, they can not advertise using MAP.

So, brands need to monitor whether retailers are continuously violating MAP policies. AI software can achieve that. Pricer24, Minderest, and Prisync are AI software that offers accurate MAP price tracking among retailers in real-time.

Effective Inventory Management

Stockouts and overstocking are detrimental to seamless customer service. Yet, they can only happen with effective inventory management. There is generative AI in retail to predict demand and optimise inventory levels to prevent unexpected stockouts or stockpiling. Many companies are using AI in retail inventory management to analyse past sales data, market trends, economic factors and even weather factors.

Advanced AI systems can take multiple variables for more accurate demand forecasts. After all, data-driven decisions are crucial to maximise profits and ensure customer satisfaction.

Increased Operational Efficiency

While AI has apparent applications in providing personalised experiences and increasing customer engagement, the use of AI in the retail industry also streamlines backend operations. While it optimises the crucial inventory management part, AI systems help with supply chain management, order fulfilment, fraud detection and prevention.

In every retail operation, there are a ton of repetitive tasks that AI tools can take care of, including 24*7 customer support with chatbots. By reducing various costs and mitigating possible business risks, AI in retail has a significant role to play in helping businesses focus on strategic growth and innovation.

Challenges of using AI in Retail

Artificial intelligence systems have revolutionised the way retailers conduct their business within and without. But they have also brought a set of challenges to be wary of such as data privacy, algorithmic limitations, and ethical usage of AI systems. Onboarding advanced AI systems and staff training also add to the difficulties of employing AI in the retail industry.

Further, hiring AI systems is subject to local regulations of a country or region. As retailers plan to deploy AI-powered tools in shopping, they must ensure the implementation of data governance rules and practice transparency and accountability. They must uphold the ethical use of AI systems.

Case Studies of Using AI in Retail Industry

Walmart Uses AI Robots for Restocking

Walmart uses robots to scan the shelves in retail stores to identify restocking needs and missing items. They are capable of taking 20 million photos of everything on the shelves. Walmart uses AI for price adjustments, letting its staff focus on delivering a customer experience.

Amazon Go for Cahier-free Shipping

A popular use case of AI in retail is Amazon Go, which uses AI-powered sensors and cameras to enable cashier-free shopping. It also automatically charges customers' accounts for selected products.

My Starbucks Barista

Another prominent one among artificial intelligence in retail examples regards Starbucks. It is an AI-powered app that facilitates voice and text-based orders, eliminating the burden of standing in lines and getting one's orders immediately upon arrival.


The role of AI in retail industry is spreading into every area. From offering valuable customer experiences and providing interactive shopping to automating backend operations and optimising inventory levels. The aim for all retailers is the same: to personalise shopping experiences as much as possible. We have also noted that adopting AI in retail is also fraught with challenges like ethical usage and ensuring relevant regulations.

Effective use of AI in retail operations surpassing the hurdles is only holding optimal times for proprietors to maximise profits and instil brand identity like never before. How much of it retailers can leverage remains to be seen.


How does Zara use AI in retail shopping?

Zara has started using AI and robotics in its stores to speed up the pickup of online orders. This is part of its effort to enhance the shopping experience by allowing customers to buy online and pick up their purchases in-store.

What are the problems with using AI in retail industry?

While AI systems can enhance retail shopping experiences, some things could be improved, such as the need to train staff, implement pricey technological systems, ensure ethical usage, and regularly check and follow AI-related regulations.

What are the 5 use cases of AI in retail?

  • Personalised shopping experiences.
  • Inventory management.
  • Customer service and support.
  • Fraud detection and prevention.
  • Operational Efficiency.

Can AI help with retail sales?

Yes, AI can boost retail sales by analysing customer behaviour. Then, it uses the analysis to personalise marketing, set prices, and manage inventory. It also enhances customer experience with tailored recommendations and seamless support. These improvements lead to increased customer satisfaction and loyalty, impacting sales positively.

Nethra Ramani Author
Sharjeel Ahmed

As someone who has built highly scalable products from the ground up, I've always been drawn to solving challenging problems. But it's the quest for operational excellence that truly lights my fire. The thrill of streamlining processes, optimizing efficiency, and bringing out the best in a business – that's what gets me out of bed in the morning. Whether I'm knee-deep in programming or strategizing solutions, my focus is on creating a ripple effect of excellence that transforms not just businesses, but the industry at large. Ready to join forces and raise the bar for operational excellence? Let's connect and make retail operations and Facilities Management better, together.

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