AI for Personalization, Support, and Loss Prevention in Retail

Retailers are among the early AI adopters, with giants like Walmart, Amazon, and Lowes seeing significant returns-and smaller organizations like Uniqlo and West Elm pioneering AI shopping. While many retailers remain hesitant to release AI applications directly to individual customers, the impact of AI on business can no longer be denied. This article examines AI's benefits in the retail industry, along with the unique security risks and best practices retailers should consider when using AI tools.

The Benefits of AI for Retailers

AI promises to provide significant productivity improvements by cutting down on repetitive work, allowing employees to refocus their efforts on higher-value tasks. AI can forecast product demand, analyze consumer behavior, and detect fraud. The customer experience benefits from both AI personalization and improved business operations.

• Personalized customer experience: AI solutions can recommend best-fit products to consumers based on their browsing history and preferences. Today, 78% of consumers are more loyal to brands that offer personalized shopping content like this, and 87% of organizations are using AI to improve their email marketing campaigns.
• Customer support: Retailers today use AI to identify customer issues, route customers to the right support agent, and-in some cases-provide end-to-end support. For instance, passenger rights firm AirHelp improved their response times by 65% by using AI to route customer requests to the right support agent.
• Loss prevention: AI tools can monitor real-time security camera feeds and flag suspicious activity for in-store personnel. They can also search transaction logs for potentially criminal transactions, such as frequent or identical high-value purchases. One leading retail chain realized a 30% reduction in shrinkage by using AI-enabled video surveillance.
• Inventory management: Retailers are using AI to reduce out-of-stocks, analyze sales data, and draw insights about inventory. Walmart applies AI to substitute missing items for curbside pickup and delivery orders. Meanwhile, Amazon's AI tools detect damaged items, pick and pack, and manage inventory more efficiently.
• Supply chain management: AI can forecast demand, plan the most cost-effective routes and suppliers, and optimize communication between all links in the chain. Walmart uses AI to negotiate supplier contracts, extending payment terms and cutting costs.

Data Security Risks Specific to the Retail Industry

Retailers face significant security hazards from the large volumes of customer data managed in the industry. Intensifying these risks are bad actors that can manipulate and confuse AI systems, tricking them into sharing sensitive information.

• Payment card data theft: Retailers are prime targets for hackers aiming to steal payment card data through point of sale (POS) systems or online transactions. Retailers who share payment details with AI tools risk having those tools tricked or compromised, exposing customer data.
• Phishing: Offenders can target employees through phishing emails or social engineering tactics. Cybercriminals are using AI tools to improve their phishing efforts, and AI can be tricked with phishing-like scams.
• Data breaches: Retailers can inadvertently expose customer information-such as names, addresses, and payment details. AI systems present another entry point for data breaches.
• Supply chain vulnerabilities: Third-party vendors and suppliers with weaker security can provide easier access to customer data. Sharing AI systems or outputs with third parties can open organizations to added risk.
• Compliance Issues: Failure to comply with data protection regulations (including GDPR or CCPA) can result in legal action and damage a company's brand. AI can open organizations up to increased liability.

Retailers currently use encryption, secure payment gateways, regular security audits, and cybersecurity training to mitigate these risks. However, AI systems create new pathways for cybercriminals to access sensitive data. For example, criminals can ask an AI tool multiple legitimate questions, then use its replies to reverse-engineer an off-limits answer.

With the onrush of AI, 77% of businesses have already suffered AI-related breaches. In response, firms are developing new AI security strategies and policies to reduce their risks from AI.

AI in Retail FAQ

Here are answers to frequently asked questions about AI in retail for personalization, support, and loss prevention.

How can retailers create an AI customer journey?

Use common, visible customer data to create basic customer profiles. Augment these profiles with input from social media posts and online reviews. Then, ask AI to recommend process changes to improve the customer experience. AI can help retailers:

• Decide which products to carry
• Determine inventory levels
• Simulate promotions and pricing
• Create personalized messaging in emails, offers, and product descriptions
• Chat with customers about your products and offer support
• Monitor surveillance footage to notify employees of suspicious behavior

Where should a retail company start when implementing AI?

It's best to start with a small project that can show early success. Document the results and publish them internally to gain buy-in for future projects.

How can a retail business identify AI opportunities?

Retailers can find use cases for AI in their organization by analyzing their customers' pain points and their own internal challenges. Assess which ones are tied to existing, easily accessed and organized data troves-such as online reviews or net-promoter score (NPS) survey results. Then determine the issues that are currently handled by repetitive, automatable, non-value-added human labor-or aren't addressed at all because the cost of labor would be prohibitive.

How can retailers protect customer data when using AI?

The best way to protect customer data is to share only common, currently visible data. If you do use AI to process private information, only share it with closed-loop AI tools that don't store or share it with online libraries or other users. Be transparent with customers about the data you use and how you use it. And subject the outputs to human review, with the same level of control you apply to all customer information.

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