AI in Retail: Transforming Consumer Engagement and Operational Excellence

After Gen AI hit the top charts of technologies in 2022, retailers quickly realised its potential for the industry. AI in retail became a major topic of interest, as evidenced by Google Trends.

With the widespread availability of LLMs, retail executives plan to invest more in data engineering to support AI use across the organisation. In 2023, the global AI in retail market size was $9.97 billion, and it's expected to reach around $54.92 billion by 2033.

Despite the fact that almost every large retailer has experimented with AI, and 92% of them plan to expand AI investments, the results haven't always been bright. For example, Target developed Help AI, an internal chatbot that assists employees with in-store processes. However, it failed to meet expectations, as the bot struggles to provide useful answers, and employees describe it as a waste of company resources.

In this article, we'll explore whether AI in retail is as impactful as companies believe, identify the areas where it will have the most impact, and examine successful (and not so) implementations of AI in the market.

The Role of AI in Retail

AI in retail has the potential to support employees across every part of the industry. McKinsey’s report about the economic potential of GenAI in retail highlights 9 main areas of operations:

• Marketing and sales
• Customer operations
• Product R&D
• Software engineering
• Supply chain and operations
• Risk and legal
• Strategy and finance
• Corporate IT
• Talent and organisation

Here's how GenAI can impact each of these areas:

On a grander scale, AI allows retailers to interact with data in a new way, proactively spot trends and changes in data, and adjust  their strategies with fast changing environment and customer needs.

Let's dive deeper into the use cases of AI in each of these business functions.

AI in retail marketing and sales

Marketing and sales is where AI shines the most. It can significantly enhance customer experience through personalised recommendations, 24/7 support, dynamic loyalty programs and more.

For example, Bain & Company research claims that GenAI shopping assistants can boost a retailer's revenue by 5% to 10%. McKinsey also supports this statement, saying that personalization can lower acquisition costs by 18% and increase customer spend by up to 20%.

In one of their surveys, around 67% of customers were excited to try smartphone or in-store assistants that can give them suggestions on outfits for specific occasions (e.g. beach vacation in Goa) and maintain a conversation throughout the purchase journey.

Besides that, AI can improve overall marketing productivity with dynamic content creation. It can generate product descriptions, social posts, and new product announcements, saving marketers' time and ensuring product market fit.

AI in retail customer service operations

AI in retail customer service aims at improving the interaction through efficient, personalised support. AI-powered tools such as chatbots and virtual assistants are available 24/7 and handle multiple queries at once to minimise wait times and increase customer satisfaction. Such solutions ensure seamless customer experience and lead to long-term loyalty.

• Automate 80% of customer interactions. AI support agents can understand and resolve even the most convoluted customer issues. Most requests do not require direct involvement of a human support agent, because AI Agent has access to the support database. If there's a request that an AI agent can't handle, it will automatically transfer it to a human support manager.
• Service quality analytics. With access to all customer conversations and calls, AI can review them and provide insights into support performance. This can help train both your AI Agents and human support agents to handle requests faster and more professionally. For example, Zendesk helped their client establish an internal feedback loop to train agents and achieve a 96% CSAT score.

Software engineering

AI-augmented software development can significantly reduce engineering workload, automate routine tasks, and allow more time for complex, high-value tasks.

AI tools like GitHub Copilot help developers by autocompleting code and providing initial drafts, making it easier to get started and avoid blank page syndrome.

• Code refactoring: AI helps restructure and modernise legacy code to simplify functions and remove code redundancies. Here, AI can save about 20% - 30% of development time.
• Code Documentation: AI can understand what each line of code does, helping developers document their code more efficiently, and cutting the time spent on this task by half.

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Note: By implementing AI in retail software development companies can save up to 30% of time spent on engineering tasks. Learn how to maximise AI efficiency  for your software development team.

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AI in retail supply chain

AI optimises supply chains using predictive analytics. By forecasting demand, managing inventory proactively, and handling returns, companies can significantly improve operational efficiency.

• AI for product returns: AI can predict how many products will be returned by analysing historical data and consumer sentiment towards a specific product. Return forecasting helps manage inventories, reduce excess stock, and avoid shortages.
• Demand Predictions: AI in retail uses a vast array of data to predict demand from various sources such as sales history, social media, and external economic factors. It helps businesses understand future market demand correctly so they can make decisions faster and allocate resources quickly.

Risk and Legal

AI transforms retail legal operations through the automation of routine tasks, enhancing legal workflows. With its help, legal teams can manage high volumes of contracts, compliance checks, and customer disputes. This reduces turnaround times and operational costs.

With GenAI retail legal departments will be able to assure much higher accuracy and consistency while tackling complex legal challenges.

AI in retail legal processes helps predict case outcomes, automate insurance claims, and automate tax processes.

Strategy and finance

AI is transforming retail finance operations by improving the accuracy of forecasts and enhancing the efficiency of day-to-day processes. By automating repetitive tasks, analyzing vast datasets, and generating predictive insights, AI equips financial teams with practical ways of deploying resources and timely responses toward changes in market trends. This leads to better financial planning, reduced operational costs, and precise risk management.

Also, AI solutions enhance fraud detection and compliance monitoring to protect the retail business from financial threats. With advanced algorithms driving real-time analytics and proactive insights, retail finance teams can focus on strategic priorities while ensuring profitability and long-term stability.

Key Benefits of AI in Retail Finance Operations

• Precise Financial Forecasting: It reduces forecasting errors by up to 50% and enhances predictive capabilities, thus enabling informed decisions.
• Improved Productivity: Automates up to 40% of regular financial tasks for the saving of time and thereby allows the processing of data at an instance.
• Proactive risk management: It identifies potential risks and fraud early, thus reducing financial losses by up to 30% due to timely intervention.

AI in Retail: Success Stories and Case Studies

Now, let's explore how retail and e-commerce companies use AI today to simplify the buyer's journey and increase customer engagement.

OLX: Product recommendation chatbot

OLX Group, an online marketplace operating in over 30 countries, recently introduced OLX Magic, an AI-powered chatbot that helps users find products based on specific requirements.

Instead of filtering by category, city, and price, users can enter a single prompt to see multiple product options that match their description.

Since OLX operates internationally, the chatbot allows users to search in any language, making it easy for travellers or temporary residents to find products without knowing the local language.

AI Magic speeds up the search process by 50-80% compared to regular searches, improving user satisfaction and optimising the buyer's journey.

Walmart: AI-driven storage management

Walmart reinvents holiday shopping with an AI-powered inventory management system that can deliver 'what's needed at just the right time.' By using historical data and predictive analytics, Walmart places the festive items in stores and fulfillment centers in strategic ways to make sure availability is seamless for customers.

Walmart's AI algorithms integrate macroweather patterns with economic trends and local demographics to forecast demand and minimise disruptions. With features such as anomaly filtering, Walmart prevents one-off events from distorting future predictions, allowing for seamless inventory flow and consistency.

Integrated into 4,700 stores and digital platforms, the AI of Walmart now empowers real-time adjustments by region so that warm sweaters go to cold states and sunny essentials to warmer climates. This approach improves customer satisfaction while streamlining the supply chain.

Instacart: AI grocery assistant

Instacart is implementing AI on the grocery front. Ask Instacart is an AI-powered search tool that will not only answer food-related questions from customers but also provide personalised recommendations. That will help with meal planning, ingredient swap suggestions, and mastering a cooking technique–all thanks to AI instigated by both OpenAI's ChatGPT and Instacart's proprietary AI models.

The Ask Instacart feature, integrated into the Instacart app, uses machine learning to automatically predict what customers need or may want, based on their shopping history and dietary preferences. It provides intuitive product recommendations, meal ideas, and helpful tips to make grocery shopping quicker, easier, and more fun.

By matching customer questions with personalised answers and brand-sponsored campaigns, Instacart creates a seamless shopping experience-allowing customers to discover new products while delivering ingredients as fast as an hour.

Amazon: Dynamic Pricing and Recommendations

Amazon is great at personalising the shopping experience through its AI-driven dynamic pricing and recommendations. By looking at real-time data on market trends, competitor pricing, and user browsing patterns the platform’s algorithms adjust product prices and serve up recommendations that enhance the shopping journey. This data-driven approach has resulted in 30%+ sales growth and 20%+ customer satisfaction, proving the impact of AI in retail.

Barriers to AI Implementation

AI is everywhere (AI even wrote this article), but implementation has been patchy because each industry has its own unique challenges and cultural barriers to overcome; AI in retail is no different. But the benefits of AI far outweigh the challenges. A McKinsey report commissioned by the Harvard Business Review estimates AI will add over $13 trillion to the global economy in the next decade.

Despite these predictions, the same McKinsey report found that 8% of companies surveyed had made the internal cultural shift to get AI to pay off. Beyond culture though, the report outlined 10 common mistakes all companies (not just retailers) make when trying to get AI to work which they listed as:

The future of AI in retail is endless

AI in retail will reshape customer experiences, and optimise operations and supply chains. But retailers can use AI to respond to consumers' growing expectations of efficiency, personalisation, and security across digital and physical shopping environments. Beyond customer experiences and profit margins AI in retail may have unexpected social benefits.

More Inclusive Shopping

Retailers may use AI to increase digital inclusion for underserved populations or those who struggle with accessibility. Voice recognition and text-to-speech to help visually impaired customers. Apps like Seeing AI by Microsoft will change how AI is used in retail. Brick-and-mortar stores could use the same AI shopping apps to help visually challenged customers navigate the store without human assistance, massively improving their accessibility and independence. Here are the trends that will shape AI in retail over the next few years.

Inventory and Supply Chain Management

AI in retail is also driving innovation in inventory management with predictive analytics enabling businesses to forecast demand and manage stock levels better. Walmart for example has used AI to monitor inventory so it can anticipate demand shifts and keep popular items in stock. Companies can reduce overstock and understock issues through machine learning models, save costs, and provide a more reliable customer experience. By 2030 these applications will be even more precise with better data intelligence and predictive algorithms that can talk to each other across retail networks.

AI for Omnichannel and AR Experiences

Augmented reality (AR) and AI is a natural progression of AI in retail. Through this combination, IKEA has offered virtual try-ons and room planning tools so customers can see products in their space before buying. By 2030 these will be mainstream in retail and provide a seamless experience between online and in-store and make omnichannel strategies more cohesive. This will be crucial for businesses to create shopping environments that appeal to digitally native consumers.

AI adoption by SMEs

AI adoption by small and mid-sized enterprises (SMEs) is a big trend in retail. Unlike larger retail giants SMEs have more agility, centralised structures and closer communication channels to implement AI retail solutions quickly and efficiently. This flexibility allows SMEs to adapt AI technologies to meet local customer needs more precisely and engage customers while managing resources better.

With the right AI tools SMEs can build loyalty through personalised promotions and dynamic pricing based on AI analytics that knows the best price points for their unique customer base. This kind of analytics and predictive power was the domain of the big companies. But as AI in retail evolves it’s clear these tools are levelling the playing field and allowing smaller retailers to compete in the digital age and innovate alongside the big retailers with solutions that fit their customers perfectly.

Vodworks: Your Trusted Partner for AI-Powered Retail Solutions

Vodworks is a global technology company providing software development services across Europe, Asia-Pacific, and North America. Headquartered in London, Vodworks has four global R&D centres in Ukraine, Romania, Pakistan, and Cambodia, with an in-house team of over 150 engineers supported by a broad talent network across regions.

The company has 13 years of experience in data engineering, app development, and AI-driven projects across industries such as retail, telecom, healthcare, streaming, fintech, and more.

Vodworks team helped numerous clients future-proof their data infrastructure for the AI era and get the most out of GenAI.

Ready to power your retail business with GenAI? Contact Vodworks today and start your AI journey.