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Target names its first chief AI officer in a bid to remake the shopping experience (exclusive)

Target has appointed Chandhu Nair as its first chief AI officer as the retailer doubles down on the technology and its potential benefits for customers. Nair comes to Target after more than six years with Lowe’s, where he was most recently senior vice president of stores, data, AI, and innovation. He previously served nine years at Staples, and cofounded a data analytics software company and a…

Target names its first chief AI officer in a bid to remake the shopping experience (exclusive)

Target has appointed Chandhu Nair as its first Chief AI Officer (CAIO), signaling the retailer's commitment to integrating artificial intelligence into its operations. Nair brings over six years of experience from Lowe’s, where he served as senior vice president of stores, data, AI, and innovation. He also spent nine years at Staples and co-founded a data analytics software company and a business strategy consulting startup.

Nair expressed his enthusiasm for making AI "real" for Target customers, emphasizing the importance of tangible benefits. In a parallel move, Target promoted Purvi Shah, the current vice president of user experience design, to senior vice president. Shah has been instrumental in developing Target's conversational shopping experience in ChatGPT and believes that AI, paired with human-centered design, can enhance the shopping experience for both customers and employees.

Both Nair and Shah emphasized the importance of listening to their teams and understanding the needs of guests, team members, and partners before implementing AI solutions. Initial applications of AI at Target could include inventory management, employee workflow optimization, and streamlining online operations. The ultimate goal is to improve the overall shopping experience by making it more efficient, inspiring, and valuable for all stakeholders.

Written by urgent.news from Fast Company's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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