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#RetailTech: AI use case landscape in retail – Guide to start your AI journey

9 out of 10 retail enterprises who implemented AI are still not entirely satisfied with their AI implementations

Globally, retail organisations are at varying levels of AI adoption across the value chain segments. Very often, enterprises that are starting their AI journey, quickly jump to an assumed end state and start working on a use case without completely understanding feasibility and/or resultant benefits.

This results in enterprises facing one or more of the following scenarios:

  • Not fully being able to realise the ROI of their initial AI investments
  • Understanding the transformation potential of the technology
  • Losing the initial momentum when their initiative hits a roadblock

To avoid this common pitfall, our recently launched report on Indian retail: AI imperative to data-led disruptive growth proposes a unique way of highlighting AI uses cases in retail through a periodic table. It provides spectrum of use cases in retail industry solving specific value chain challenges that will help enterprises understand the landscape, application areas and come up with an action plan for AI.

The research also highlighted that globally, popular enterprise focus areas for AI implementation in retail comprise Customer experience, In-store and on-line operations and Distribution and logistics (specifically as a fallout of COVID-19)

Download our report here for accessing the full periodic table and instructions to interpret and use it

Here are some illustrative use cases that are amongst the most popular AI use cases implemented by retailers globally across each of the value chain segments

  • New Product Planning: AI system can enable product customisation, as an example IBM Tommy Hilfiger and FIT collaborated on a project called Reimagine Retail and used machine learning to derive insights about patterns, silhouettes, colours to create wholly new design concepts, helping them to plan for future products
  • Dynamic Pricing Management: AI-powered system for dynamic pricing that uses information about products, promotional activities, and sales figures to make price recommendations to seller. It optimises pricing and selling, as ML model can help sellers identify the best prices, when they should list a specific product and when to market themselves to better attract buyers. eBay is a good example in this use category.
  • Freight Routing Scheduler: Retailers can deploy AI algorithm for van routing and scheduling for drivers to ensure a faster and efficient scheduling and delivery. As an example, Tesco utilises this use case.
  • Shopping Assistant: Personalized product category/brand recommendations based on style preferences, browsing history, individual shopper journeys. As an example Tata Cliq collaborated with Vue.ai’s to utilise their personalization suite and dynamic personalization engine for assisting their shoppers with the right recommendations.
  • Customer Service Chatbot: AI-powered chat bot can understand customer’s queries and respond. It can understand a customer’s emotion and can prioritize and alert human customer service agents to intervene. As an example Alibaba’s AI-powered chat bot – AliMe can proactively analyse, predict customer service needs, and reach out to customers.

Retailers have been successfully deploying these popular AI use cases globally, as highlighted in the above examples. Increasingly, new areas in customer services and in-store and online operations are emerging as AI hotspots apart from operations focused solutions. The basic premise of these new areas of AI solutions is to improve customer experience, reduce customer churn and increase the top line of the enterprise. Enterprises are required to invest in AI solutions that specifically target their weakest performing areas across value chain to benefit from the efficiency gains and financial impact.

Feel free to reach out to me for any queries/suggestions. Watch out for our next article on how to prioritise the right use cases with our unique use case prioritisation matrix.

To access the full periodic table and detailed use cases download our full report: https://tinyurl.com/y9johts2

References

[1] https://www.forbes.com/sites/rachelarthur/2018/01/15/ai-ibm-tommy-hilfiger/#15df2a1978ac

[2] https://www.forbes.com/sites/bernardmarr/2019/04/26/the-amazing-ways-ebay-is-using-artificial-intelligence-to-boost-business-success/#736b9d682c2e

[3] https://community.nasscom.in/communities/emerging-tech/ai/hows-ai-making-business-more-profitable-dynamic-pricing.html

[4] https://www.infosys.com/insights/ai-automation/documents/moving-goalposts.pdf

[5] https://go.vue.ai/hubfs/resources/tata-cliq-report-2020.pdf

[6] https://digital.hbs.edu/platform-rctom/submission/ai-chatbot-behind-alibabas-31-billion-singles-day-sales-miracle/

[7] https://www.nasscom.in/knowledge-center/publications/indian-retail-ai-imperative-data-led-disruptive-growth

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