Adopting AI in retail allows companies to bridge the gap between the virtual and physical sales channels. AI helps in personalizing the CRM as well as creates bigger business-customer- engagement platform.

FREMONT, CA: Retail industry is evolving at an extraordinary speed, and with it becomes significant challenges and opportunities. Information technology spending in the retail sector went underfunded for years, and efforts to refresh the stores through digital transformation will likely be a thorough task. Yet it is one that retail experts believe will lead to higher profitability.

Retailers need to explore use cases about exponential technologies to discuss the disruption that the industry is going through. They need to catch up with how the recommendation engines are defining the customer experience, how retail business value chain transformation is shaping up, and how AI can enhance the supply chain aspects of their business.

Predictive analytics

Predictive analytics in the retail industry is used for several years. However, in the last few years, with advances in technology and artificial intelligence with a high speed, scale, and value what predictive analytics can deliver. The AI-based retail helps the business transition into a world where consumers are always connected, more mobile, more social, and have choices about where they shop.

Deep Learning

The retail industry has grabbed a lot of benefits from leveraging deep learning. Further, a lot of AI techniques enjoying success in other applications across industries powered by deep learning are well-positioned to make a significant impact on retail. Streamlining processes and changing customer experience into something that mostly follows the experience customers get when they visit online portals.


At present, with minimal effort, the retailers can leverage automated AI capabilities and see a steady rise in customer engagement and sales. This can also be accomplished by data that is already available to them and captured in their enterprise systems. Also, the algorithms required for powering these systems, such as collaborative filtering, are relatively simple to deploy and efficient to run.

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