Artificial intelligence in retail: 6 use cases and examples

Artificial intelligence in retail is injecting a fresh dose of energy into the industry, helping retailers optimize their operations, explore new ways to engage with customers, and take CX to the next level.

We all know that the new frontier for retail success is personalization, but we face digitally savvy shoppers with constantly changing preferences who expect shopping experiences that are tailored, instant, and effortless. AI is the ultimate tool for delivering on these expectations, with its ability to intuitively understand customer desires and craft personalized services.

AI in retail: A strategic partner amid a tumultuous time

But staying profitable is about more than creating experiences that grow loyalty. Retailers face tremendous challenges — geopolitical unrest, economic volatility, and the climate crisis, to name a few. While traditional tactics might be losing steam, AI lends a strategic lens, offering cutting-edge analytics and forecasting to help retailers adapt swiftly to market twists and turns.

In fact, by 2025, 80% of retail executives expect their companies will use intelligent automation technologies and 40% already use some form of it, according to Analytics Insight.

Yet retailers can’t just plug in artificial intelligence and expect it to magically fix things. They need to take a practical approach that focuses on areas of their business where AI can have the greatest impact.

Merchandising survey: The good, bad, and ugly stats of retail

Find out how merchandisers aim to boost margins, profits, and customer loyalty in the digital retail era.

A retail playbook: 6 AI examples and use cases

There many areas of business where retailers can use artificial intelligence to improve efficiency, drive down costs, and improve customer experience. Getting the best results, however, requires a combination of the right investments in both technology and people.

A new playbook from Incisiv, Transforming Retail with AI, provides a guidebook for using artificial intelligence in the retail industry. Incisiv, a peer-to-peer executive network and industry insights firm, teamed with SAP to provide a practical framework for retailers.

Six use cases and AI examples in retail:

1. Inventory management: Maintaining sufficient stock is a constant challenge. By combining customer purchase data with supply chain analytics, AI predicts future buying trends, aligns stock, and helps spot and eliminate inefficiencies that are a drain on profits. This reduces waste, optimizes space, improves customer satisfaction, and bolsters profitability.
2. Demand forecasting: Beating the competition to the punch requires knowing what demand will look like before it happens, but forecasting is incredibly complex with multiple variables. AI systems examine past sales data, current market conditions, and emerging trends to generate accurate demand predictions. This kind of precision limits overproduction, minimizes waste, and boosts sustainability efforts.
3. Route planning: Delivery logistics play a huge role in a retailer’s bottom line. Using complex algorithms and real-time data, AI can overhaul delivery routes to limit transit times, reduce fuel consumption, and improve customer satisfaction. AI-based route planning helps companies manage changing conditions and avoid service disruption.
4. Price optimization: Retailers have to constantly adapt their pricing strategies to succeed. AI systems analyze broad market trends, buyer behavior, competitor pricing, demand flows, and internal costs to quickly adapt prices, manage promotions, and maintain profitability.
5. Assortment planning: Traditional retail assortment strategies and planning methods struggle to keep up with dynamic customer behaviors. AI digs into customer data, identifying patterns and relevant variables that are generally impossible to spot otherwise. This creates a more personalized, regional, or individual-centric product mix. According to Gartner, all global multichannel fashion retailers will use AI and automation by 2025 for targeted assortments.
6. Personalization: Providing a memorable shopping experience comes from a deep understanding of customer behaviors and preferences. AI analyzes data points such as buyer browsing habits and purchase history to help retailers craft personalized shopping experiences that drive loyalty. Optimized product placement and promotions ensure the best engagement and conversion.

Retail supply chain management: 4 ways to keep shelves stocked

The pandemic may be over, but retailers still face a slew of problems that wreak havoc on their supply chains and bottom lines. Here's what they can do to manage the risks.

Using artificial intelligence in retail

For retailers aiming for the epitome of AI sophistication — where the technology shifts from predictions to making decisions autonomously— investing in infrastructures like RFID and IoT, and fostering a unified data ecosystem are vital. Strengthening your organization’s AI capability with the requisite skills and change management practices will help drive AI’s effectiveness.

There are indications that AI is already helping retailers boost sales and profits: A Statista analysis found that retailers using AI and machine learning outperform ones that don’t.

By using artificial intelligence to refine their operations and engagement models, retailers can position themselves to thrive in a digital-centric commerce environment.