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Artificial Intelligence in Retail

Artificial Intelligence (AI) is a hot topic these days and we are starting to see many real-world examples of it in action. College students are using AI to write papers for them, it is changing the way we search for things online, and AI-powered devices like smartphones and home devices are providing assistance and convenience in our everyday lives.

Along with these mainstream examples, we have also seen retailers begin to use AI to enhance the customer experience and to gain efficiencies in their day-to-day operations. From one perspective, this technology is exciting as it enables a lot of opportunity with its virtually endless use cases; however, it can also raise some areas of concern. Let’s take a closer look at what these developments in AI are and what they could mean for individuals and businesses today.

What Is AI?

AI refers to technology that mimics human intelligence and behavior. It includes understanding natural language, recognizing objects or images, making decisions, and learning from experience. AI research aims to create machines that can perform tasks associated with humans, such as understanding speech, recognizing images or objects, making decisions, and learning from experience.

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Often you read or hear about AI concerns and the negative implications of this new technology. With innovations that are expected to be big, impactful, and far reaching—it is okay to be cautious. Some concerns related to AI include the following:

Job Loss. As AI technology improves, it may lead to job loss as AI-powered machines and systems begin to perform tasks previously done by humans.

Bias. AI systems can perpetuate and amplify existing societal biases if trained on biased data.

Transparency. AI systems may be challenging to understand, making it difficult to explain how the AI arrived at a particular decision.

Safety. AI systems may be unpredictable and may cause unintended harm if not properly designed and tested.

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Privacy. AI systems collect and analyze large amounts of data, raising concerns about privacy and security.

Control. AI systems may be used for malicious purposes, such as creating autonomous weapons and malware, and there may be difficulty in controlling their actions.

Dependence. As AI systems become more integrated into society, we risk becoming too dependent on them and losing our ability to function without them.

Singularity. Some experts believe AI could eventually become so advanced that it surpasses human intelligence and capabilities, which could lead to existential risks for humanity.

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These are a few examples of concerns related to AI. It’s essential to consider this technology’s ethical and societal implications as it advances.

Although we are beginning to see the mass adoption of AI, it has already been a part of many devices we use every day. Some examples of consumer devices using AI include:

Smart speakers, like Amazon Echo and Google Home, use AI to understand and respond to voice commands.

Smartphones, such as Apple’s iPhone and Google’s Pixel, use AI to improve camera performance, assist with tasks, and answer questions.

Smart home devices, like Nest thermostats and Amazon’s Ring doorbells, among many others, use AI to learn and adapt to user preferences.

Personal assistants, such as Apple’s Siri, Google Assistant, and Amazon’s Alexa, use AI to understand natural language and perform tasks.

Smart TVs from many brands, use AI to suggest viewing recommendations and connect with other personal devices.

AI in Retail

The potential of AI in retail is massive. With AI, retailers can analyze data and make predictions about customer behavior both in-store and online. It can provide actionable insights into customer shopping habits, enabling retailers to better target their customers with the right products, offers, or campaigns. Examples of this are Facebook and Google digital ads, which use AI and machine learning to deliver the most relevant ads possible. Through their AI-powered algorithms, they automatically analyze data from countless data points to show ads to people who are, for example, making a purchase or visiting a website. AI tools also allow retailers to identify supply chain inefficiencies, reduce costs, and match the inventory with demand. The next logical step is to bring this type of targeting in-store. Retailers already have access to large amount of data points through security cameras, RFID, and smartphone apps. With the right AI-powered system, retailers will be able to leverage that data to provide a more catered shopping experience. AI has many retail use cases, including:

Personalization. Customizing the shopping experience, such as by recommending products based on shoppers’ browsing history and purchase history, can be achieved through AI.

Inventory Management. AI helps to optimize inventory levels and predict future product demand.

Pricing Optimization. AI assists with data analysis and dynamic pricing decisions, such as adjusting prices based on demand and competition.

Fraud Detection. AI can detect fraudulent activity, such as identifying unusual patterns in customer behavior or detecting suspicious transactions.

Chatbots. AI-powered chatbots are providing customer service, such as answering questions and helping customers find products.

Image Recognition. AI-powered image recognition is used in stores to help customers find products or assist with store navigation.

Predictive Maintenance. AI could assist in predicting when equipment or machines need maintenance, reducing downtime and improving store operations.

Autonomous Robots. Another useful deployment of AI, autonomous robots, can be used for stocking, cleaning, and even delivering product.

Sentiment Analysis. AI assists with analyzing customer feedback and sentiment to identify issues, trends, and areas for improvement. On top of providing a better shopping experience, AI will be a valuable tool for LP professionals, as it can help to identify and prevent theft, fraud, and other losses. Some use cases for AI in LP include:

Surveillance. AI-powered surveillance systems automatically identify and track individuals, vehicles, and other objects in real time, helping to identify potential threats and suspicious behavior.

Fraud Detection. AI analyzes transaction data and detect patterns of fraud, such as suspicious transactions or unusual spending patterns.

Employee Monitoring. AI can monitor employee behavior, such as by analyzing security camera footage, to identify potential thefts or other losses.

Inventory Management. AI has been implemented to analyze inventory data and identify patterns of shrinkage, such as missing products or discrepancies in stock levels.

Predictive Analytics. AI can analyze data and make predictions about potential losses, such as identifying areas of the store at high risk of theft.

Exception-Based Reporting. AI can be used to analyze point of sale and self-checkout data and make predictions about potential losses, such as refund fraud, cash theft, and gift card fraud.

Facial Recognition. AI-powered facial recognition technology can identify and track known shoplifters or individuals with a history of stealing.

Automated Alarm Management. AI automates alarm management in stores by identifying and prioritizing alarms and reducing false alarms

Over the next months, we will continue to see more LP use cases, and we will need to weigh the pros and cons. In general, AI can be a powerful tool to assist retailers and LP professionals in making better and faster decisions to reduce the risk of losses.

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