Here's how a home improvement player is rearranging the furniture of customer impact with the right alignment of technology and business. 'We would not jump on the bandwagon just for its sake', stresses Mayur Purandar, VP Enterprise Architecture, Lowe's India as he shows a blueprint of how this business space is being made alive and sharp with Al, AR, Metaverse and in-built platforms. He also tells why home-grown tech is working better here than packaged tech.
Your approach to AI/ML and digital forces is quite distinct-it's holistic and product-embedded, and not isolated. Can you elaborate on the 'why' and 'how' here? Any examples you can share? Also, how easy is it to achieve this deep and end-to-end approach?
For both AI/ML and transactional systems, the customer is the same that's why we have taken a holistic and product-embedded approach. For example, if we are doing pricing analysis, the consumer of that analysis and the consumer of the transactional system, who is authoring prices and promotions, are the same; therefore, if it's not product embedded, there is going to be a lot of context-switching for the end-user. For this reason, we don't build separate products for running transactions and running Al/ML algorithms. Another classic AI/ML probe people work on is demand forecasting. When forecasting demand at a specific location, it needs to be entered into the demand plans. However, if you have your demand forecasting outside of the planning inventory system, it becomes two different experiences and it's bound to be full of friction and buy-in errors. Therefore, it needs to be put in one product i.e., both demand planning and forecasting should happen in one go. The idea is to put the customer first; hence, a holistic approach is necessary.
This story is from the September 2022 edition of DataQuest.
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This story is from the September 2022 edition of DataQuest.
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