Insights

How AI is reinventing the world of retail

UST AlphaAI Team

You only need to look around to see how AI (Artificial Intelligence) has emerged as a game-changer across most industries. Yet, the retail sector is an exceptional example of how AI can reinvent industries swiftly and universally. Retail is at the vanguard of a digitally transforming world powered by the rise in AI and IoT. Retail businesses are turning to AI for its ability to create efficiency and proficiency at velocity wherever it goes and derive business value from retail’s big data, followed by real-time decision-making and forecasting.

A recent survey by Nvidia revealed that around 69% of retailers use AI, more than 60% plan to increase their AI infrastructure investment in the next 18 months, and 34% are evaluating or piloting AI. More than 80% of respondents deployed three or more AI use cases, and over 50% cited having six or more deployments. The research found that AI in retail positively impacts revenue and operating costs, with 72% of retailers using AI reporting a decrease in operating expenses.

There are countless AI applications in retail. However, here are a few popular uses of AI in retail that stand out as steadfast trends that can serve as the foundation for successful future AI implementations.

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Creating personalized shopping journeys leads to deep connections

Keeping customers delighted is a primary motivator for AI adoption in retail, and personalization is how AI retailers are making this happen.

A personalized customer experience involves offering relevant and appealing products, services, promotions, etc., based on a particular customer’s preferences and tendencies, predicting their future needs, and making offerings available anytime, anywhere, and when needed.

The attributes of a personalized shopping journey in the retail sector include:

• Customized product recommendations.
• Personalized digital content and promotions.
• Immersive omnichannel experiences.
• In-store shopping and checkout assistance to increase engagement and gratify customers.

A personalized shopping journey helps retailers build deep connections with customers, leading to better conversion rates and sales.

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AI introduces frictionless shopping and checkout experiences

Frictionless shopping enables customers to find the right products and buy them instantly, helping retailers improve customer experience and increase sales. Frictionless shopping features emerging technologies, such as smart shopping carts, self-checkout machines, virtual assistants, and assisting shopping robots.

Frictionless checkout is where IoT sensors and cameras track and identify customers and their chosen products, creating a checkout process that requires no physical interaction.

These technologies work together to form a unified, efficient shopping experience that reduces customer wait time, creates loyalty, and lessens the need for cashier-assisted checkout.

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AI chatbots deliver instant, accurate, and consistent customer service

AI-powered chatbots in retail play a vital role in customer service by providing instant and accurate responses to customer inquiries 24/7. Chatbots in retail answer customer inquiries with automated responses to frequently asked questions, handle and track orders and returns, and offer promotions and recommendations based on a customer’s preferences and inclinations.

AI chatbots also gather insights into customer behavior, helping merchants consistently enhance their product offerings. Consequently, AI chatbots in retail are on the verge of ubiquity. With emerging technologies such as generative AI in retail, chatbots have evolved into virtual shopping assistants, enhancing customer satisfaction and fostering brand loyalty.

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AI in data analytics uncovers customer insights and forecasts future behavior

AI-driven analytics examines historical retail big data to gain insight into customer behavior, patterns, and preferences, allowing merchants to target customers more effectively and paving the path to a better shopping journey that leads to lasting customer loyalty and higher conversion rates.

In addition to analyzing customer interactions, Al data analytics examines sales data, market trends, consumer behavior, social media signs, and other factors to predict future demand precisely and timely, allowing retailers to adjust swiftly to market trends, remain relevant, and compete.

AI holds the key to unlocking value from retail big data, breaking down data silos across different sources, distributing data across various architectures, systems, frameworks, applications, and networks, and opening new ways to improve data integrity.

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AI in process automation amplifies speed, precision, and productivity in retail operations

Retailers tally about 55 million transactions daily, and every transaction relies on numerous processes that cross departments, functions, and channels, making process automation a great fit for retailer operations to keep pace with the growing needs of customer-facing AI activations.

AI can automate simple, repetitive tasks, such as order processing, invoicing, and credit card processing, using pre-defined machine learning algorithms that need no human interaction — an impressive feat.

Consequently, AI deep learning algorithms can automate and connect multiple processes that can create autonomous large-scale retail business functions and operations, including inventory management, order fulfillment, supply chain management, and premises maintenance while optimizing pricing, display placement, inventory levels, delivery routes, and generating accurate demand forecasting to reduce lead times and improve overall operational efficiency.

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AI in fraud prevention safeguards retail businesses and their customers

Trust is a critical bond between retailers and their customers. Yet, merchants forfeit more than $100 billion each year to online scams, credit card theft, return abuse, deceptive bots, and coupon fraud.

AI works in real-time with instant detection and predictive powers, making fraud detection a strong candidate for AI implementation.

AI in fraud detection applies machine learning algorithms to big retail data to identify patterns and anomalies that represent suspicious transactions, flag them for immediate action, and prevent future schemes. AI can safeguard customer data by detecting, classifying, and securing it.

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AI in omnichannel creates seamless, personalized shopping experiences

Omnichannel bridges the gap between physical stores and the online world. Specifically, omnichannel in retail describes a business strategy to connect and synchronize all sales channels, including websites, physical stores, apps, mobile, and social media, to offer customers a consistent and unified personalized shopping experience.

AI in omnichannel collects and examines data from different touchpoints to understand customer needs and behaviors, predict their future needs, and provide timely, tailored recommendations and information to create personalized shopping experiences.

For example, AI can give real-time updates to customers using their phones in stores, offer equally pertinent suggestions and promotions to active online shoppers, and provide timely and relevant real-time insights and recommendations like product availability, delivery speed, and appropriate discounts.

AI also helps omnichannel retailers automate inventory and smart fulfillment functions while synchronizing inventory across all channels.

Seamless experiences across channels elevate customer delight while helping retailers advance the personalized shopping experience. Furthermore, by converging inventory into a single platform to manage all sales channels, retailers save money, time, and resources.

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Retails AI challenges

While retailers continue to activate AI, turning innovations into success stories, they still face challenges. AI requires investing in AI infrastructure, emerging technology, and new methods.

Retailers aiming for AI must first move to modern architectures, systems, databases, frameworks, and applications and embrace emerging technologies. AI requires distributed clouds, big data, the IoT, and modern Agile approaches to coding, development, and project management that value continuous improvement, fast delivery, adaptability, and customer satisfaction.

By investing further in AI infrastructure and emerging technologies, retailers can overcome AI’s greatest challenges of insufficient technology and lack of AI talent.

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Wrapping up

The retail sector is undergoing a technological transformation driven by the rise of AI and IoT. Retailers around the world are past the exploration stage with AI; they’ve seen the benefits, and they are now swiping the isles searching for more ways to capitalize on AI with novel approaches like frictionless shopping and checkout, real-time fraud detection, new product introduction, and generative AI-powered chatbots, among many others.

AI’s powers of efficiency, velocity, and proficiency lured retailers toward AI for its ability to enrich customer satisfaction and personalize shopping journeys while securing efficiency, proficiency, speed, forecasting, and instant decision-making.

Though AI has reached most industries today, the retail sector was an early adopter, making retail a promising role model for how businesses can thrive in this age of AI.

At UST, our AI experts work at the cutting edge of technology and collaborate with top academic institutions like MIT Computer Science and Artificial Intelligence Lab (CSAIL) and Stanford AI Lab (SAIL) to accelerate innovation and the pace of change. Our AI solutions help businesses solve challenges faster, reach their goals, and achieve sustainable growth. To learn more about how Generative AI can meaningfully impact your business, visit UST AlphaAI.