AI Industry Innovations , AI Technologies , E-commerce & Retail

Target Introduces Gen AI Chatbot for Store Workers

Retailer Expects Chatbot to Streamline Work, Enhance Shopping Experience
Target Introduces Gen AI Chatbot for Store Workers
Image: Shutterstock

Target Corp., a Minneapolis-based retailer with nearly 2,000 stores in the U.S., is set to introduce a generative-AI-based chatbot in August. The Store Companion chatbot is designed to make jobs easier for "hundreds of thousands" of store workers and enhance the shopping experience for customers, according to Target in a statement.

See Also: Business Rewards vs. Security Risks of Generative AI: Executive Panel

The chatbot can answer on-the-job process questions, coach new team members and support store operations management. With this initiative, the retail giant aims to boost productivity and efficiency of store workers, offering faster service, enhanced guest engagement and better shopping experiences.

This move is part of Target's broader strategy to use gen AI across its business operations.

Brett Craig, executive vice president and chief information officer at Target, acknowledged the role of technology in the future of retail for team members, guests and the business. "We're continually experimenting with new tools to make it even easier for our team to do their jobs," he said. "The transformative nature of gen AI is helping us accelerate the rate of innovation across our operations. These new tools and applications will play [a big role] in driving growth."

How the Store Companion Works

Team members can get immediate answers to their process-related questions from the Store Companion chatbot, which is available as an app on handheld devices. Team members can input prompts such as "How do I sign a guest up for a Target Circle Card?" and "How do I restart the cash register in the event of a power outage?" and receive instructions and resources in seconds. The tool also serves as a store process expert and coach, helping new and seasonal team members learn on the job.

Many organizations exercise caution when using their data to train publicly available large language models - such as ChatGPT, Claude, Llama and Gemini - and then adapt them for specific business functions. To address these concerns, some firms use synthetic data sets for training.

Some companies use the principle of retrieval augmented generation, or RAG, to train their LLMs or chatbots. RAG is an AI framework that retrieves information from internal documents such as user guides, operation manuals, FAQs and policies.

Target's in-house technology team, however, used frequently asked questions and process documents from its store teams across the U.S. The team worked quickly, taking the project from its initial testing phase to the planned rollout in only six months. Target is currently piloting the tool at approximately 400 stores, using the teams' feedback to improve the experience ahead of the chainwide rollout. Experienced team members have contributed their expertise to help shape the tool.

The Store Companion will make daily tasks easier and enable teams to respond to guests' requests with confidence and efficiency, said Mark Schindele, executive vice president and chief stores officer at Target. "The tool frees up time and attention for our team to serve guests with care," he said.

Target's Gen AI Road Map

Target plans to roll out another internal gen AI tool in the coming months, starting with the team members at its headquarters.

The retailer is also using gen AI to elevate its digital experience for consumers, which includes gen-AI-powered product pages and search capabilities that curate the most relevant results. Target said this will make it even easier for consumers to find everything they need when shopping on Target.com.

Target did not respond to a request by Information Security Media Group for additional comments about the chatbot in time for publication.


About the Author

Brian Pereira

Brian Pereira

Sr. Director - Editorial, ISMG

Pereira has nearly three decades of journalism experience. He is the former editor of CHIP, InformationWeek and CISO MAG. He has also written for The Times of India and The Indian Express.




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