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How AI Impacts the Retail Industry

by DataFlareUp
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Most retailers know customer data can unlock powerful insights that lead to higher profits, more frequent purchases, increased engagement and other advantages. That’s why many companies in the retail industry are applying artificial intelligence (AI) to their efforts to learn more about consumers. Here’s a look at how that technology could change the industry.

Accelerating the time-to-value metric

Leaders typically want to see high-value outcomes as soon as possible after investing in AI or ML. Otherwise, they may become discouraged and decide against using these technologies to the extent they initially planned. Fortunately, some people see retail as the ultimate test case for AI adoption.

That could be because the opportunities to apply it extend to the physical and online realms. Retail stores where people walk in, buy what they want and leave without seeing a cashier have cropped up in the United States over the last few years. However, they’ve arrived in the European market more recently. Those shops feature AI, advanced sensors, cameras and other high-tech features.

Even when retailers recognize AI’s potential, many balk at deploying it. They understand using the technology well often requires hiring a dedicated team of tech professionals. That could mean it takes longer than companies would like to see results.

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However, tech company Tredence’s ATOM.AI product claims to enable a 50% decrease in the time-to-value metric. It has advanced AI and ML capabilities and a store full of ready-to-use features. People can explore them and add the appropriate options to their applications, freeing them from hiring specialists. ATOM.AI also boasts deep data sets, third-party integrations and technical notebooks, further increasing how soon retailers see results from AI and ML.

Some retailers may feel that AI and ML are separate from their primary business models. Perhaps that’s true, but given customer data’s immense value, retail professionals often realize they can’t ignore the potential in that information. Otherwise, they may fall behind industry peers already using AI and becoming more mature with their overall adoption.

Solving known pain points

Retailers are more likely to use new technologies if doing so addresses identified challenges. AI can tackle several of them. Consider how one of the most common questions shoppers are asked is whether they found everything they were looking for during their visits. Consumers that face empty shelves will likely feel disappointed and may shop elsewhere.

However, AI can reduce that likelihood. Walmart tested an AI solution in several Canadian stores that detected real-time product availability and alerted employees when stock ran low. That could boost profits while elevating customer satisfaction levels.

Retailers can also use AI to enhance training efforts within the workforce. Many deskless workers, including sales floor employees, use iPads with specialized software to improve their work.

They can then complete training faster or collaborate with colleagues more effectively. It was previously often tricky to find the time and place for them to do those things, particularly since the workers didn’t have dedicated offices to use.

Including AI in the learning experience caters to people with different levels of experience in the retail industry and learning styles. They might receive personalized content that focuses on material they don’t know as well while offering less coverage of familiar concepts. That approach keeps employees interested and allows employers to maximize their ongoing efforts.

Numerous pioneering AI applications also exist for retailers to explore. In one example, researchers from Queensland University of Technology (QUT) suggested stores use AI to inform layout decisions. They clarified that retail professionals could capture data from security cameras and use AI to process it.

That method could show retailers how and where to place products to raise customer satisfaction rates and encourage people to buy more. It’s also convenient that most retailers already use security cameras, making it easier for them to deploy AI through them.

Addressing the last-mile delivery problem

Perhaps you’ve heard logistics professionals discussing the many challenges associated with the final phase of parcel delivery. Even though the packages have already come so far, traffic slowdowns, inefficient routing, rising costs and road construction are all factors that can make it more challenging to get items to their destinations on time. AI can fix it.

Many fleet managers use AI-powered routing software to help drivers steer around potential obstacles and deliver goods faster. The associated improvements keep retail store shelves stocked and significantly boost e-commerce brands.

Kroger announced a partnership with NVIDIA in 2022 concerning building an AI laboratory. Improving last-mile delivery so consumers can access the freshest foods was one of the key things people intended to study in the facility.

Scientists at Amazon have even developed a machine-learning solution that accounts for driver expertise during intelligent routing. Optimizations allow drivers to change a prescribed route if they know a better way.

That’s a great example of a realistic use of AI. Retailers who want to use the technology must understand that artificial intelligence is not perfect and won’t always give people the best options.

Making AI and ML more accessible to retailers

Retailers are unlikely to show interest in technologies if they believe they’ll be prohibitively expensive or difficult to use.

This overview shows how there are virtually endless possibilities to explore. Plus, retailers can start small and scale up from there. Once retail professionals realize AI is within their reach and easier than expected to deploy, they’ll feel more confident about using it in all aspects of their business for the foreseeable future.

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