Generative artificial intelligence (GenAI) tools such as Azure OpenAI have been drawing attention in recent months, and there is widespread consensus that these technologies can significantly transform the retail industry. The most well-known GenAI application is ChatGPT, an AI agent that can generate a human-like conversational response to a query. Other well-known GenAI applications can generate narrative text to summarize or query large volumes of data, generate images and video in response to descriptive phrases, or even generate complex code based on natural language questions.
GenAI technologies offer significant potential benefits for retail organizations, including speedy price adjustments, customized behavior-based incentives, and personalized recommendations in response to searches and customer preferences. These technologies can create new written, visual, and auditory content based on natural language prompts or existing data. Their advanced analytic capabilities can help determine better locations for new stores or where to target new investments. Generative AI chatbots can provide faster, more relevant customer assistance leading to increased customer satisfaction and in some cases, reduced costs and customer churn. To gain a deeper understanding of how retail organizations can benefit from Generative AI applications, we spoke with James Caton, Practice Leader, Data and Artificial Intelligence, at Microsoft, and Girish Phadke, Technology Head, Microsoft and Cloud Platforms, at Tata Consultancy Services (TCS). James and Girish discussed three ways Generative AI is transforming retail: speeding innovation, creating a better customer experience, and driving growth.
How can Generative AI speed innovation in retail?
James Caton: We’re already seeing a lot of data-driven innovation in the industry. Microsoft Azure OpenAI Service, which provides access to OpenAI’s large language models, allows more probing and deep questioning of data. A frontline worker could have the ability to “chat with their data,” to conversationally query inventory or shipping options for example, see the response in a chart, and ask for trend analysis and deeper insights.
It essentially gives you an assistant or a Copilot to help do your job. Imagine having several assistants that are parsing the data, querying the data, and bringing data reports and visual graphs back to you. And you can send the copilot back and say, “please look here,” and “I want more information there.” As a retail sales manager, OpenAI will allow you to develop more innovative solutions, more tailored strategies, and more personalized experiences.
How does Generative AI’s conversational flow enable a more compelling customer experience?
Girish Phadke: Existing call center tools can be conversational, and they do have access to 360-degree customer views, but there is a limit in terms of how far back they can go and what kind of data they can process to answer the customer’s query.
The new Generative AI models can go deeper into historical information, summarize it, and then present it in a human-like conversation. These models can pull data from multiple interactions and sources, from a huge amount of information, and create a response that is the best fit to answer a particular customer’s question. Essentially, tailoring the answer not only based on a massive knowledge base of data, but also on the individual customer’s preferences.
Can you share an example of how one of your customers has benefited from using OpenAI to process and analyze vast amounts of information?
Caton: CarMax reviews millions of vehicles. The challenge for new buyers was there were too many reviews, and they could not get a good sense for why people liked or disliked a certain vehicle. CarMax used the Azure OpenAI Service to analyze millions of reviews and present a summary. If a customer was looking at a certain make and model, the Azure OpenAI service summarized the reviews and presented the top three reasons people liked it and the top three reasons they disliked it. The technology summarized millions of comments, so that customers didn’t have to, thus improving the customer experience and satisfaction.
Are there steps that retailers can take to get ready for OpenAI and similar tools?
Caton: If a retailer wants to take advantage of these capabilities, the first thing they need to do is move their data to the Microsoft Cloud. Then, partners like TCS can help them develop their preferred use case, such as applying Generative AI to inventory or sales data or helping develop more tailored marketing campaigns. TCS knows the industry as well as most retailers. They understand the technology, how to manage and migrate data, and how to optimize to make best use of the new capabilities.
Phadke: We understand this is a new technology; retailers are likely to be cautious. They can start by augmenting existing capabilities, such as with more comprehensive Azure ChatGPT, and adjust the governance models as they learn more about their data and processes. As confidence grows, they can begin to automate the larger deployment mechanism.
How long does it typically take for an organization to see a return on investment from Generative AI?
Phadke: With the right strategy and right set of use cases, a system can start generating a positive ROI very quickly. TCS offers a six-week discovery assessment to help with ideation and strategy development. Within 12 to 16 weeks of adopting Azure OpenAI Service, an organization can have a more scaled-out implementation.
Do retail organizations have to embrace Generative AI technologies right now if they want to be able to compete?
Phadke: I think if some retailers choose to ignore this technology, they risk falling behind. Earlier adopters might get a competitive advantage. This technology is disruptive in nature and will have a significant impact on many industries, including retail.
Caton: OpenAI is the fastest application to hit 100 million users —faster than Facebook, Instagram, or WhatsApp. The risk for slow adopters is that their competitors are adopting it and might gain a competitive advantage. It is being adopted very widely, very quickly.
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Girish Phadke, Technology Head, Microsoft and Cloud Platforms, TCS
Girish Phadke leads Edge to Cloud Solutions, AI, and Innovation focus areas within the TCS Microsoft Business Unit. He provides advisory to customers on next generation architectures and business solutions. He tracks and incubates new technologies through TCS Microsoft Business Unit Innovation hubs across the globe. Girish is based out of Mumbai, India, and in his free time loves watching science fiction movies.
James Caton, Practice Leader, Data & Artificial Intelligence, Microsoft James Caton serves as an AI Practice Leader at Microsoft, helping global system integrators build sustainable Azure Artificial Intelligence businesses. He has held technical and commercial leadership positions at software companies SAS and IBM, as well as with Larsen & Toubro Construction where he led their India Smart Cities business. James lives in Ave Maria, Florida with his wife and three daughters. https://www.linkedin.com/in/jmcaton/
Artificial Intelligence, Retail Industry