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Generative AI’s true opportunity in Retail

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The retail industry is rapidly adopting Machine Learning, Computer Vision AI, and smarter AI-led solutions to enhance customer experiences, drive supply chain optimization, smarter in-store operations, and more. Generative AI, however, can help them achieve more. It can significantly improve the team’s productivity, create personalized customer interactions, and accelerate code creation, testing, and debugging for the IT engineering teams.

Gartner’s recent emerging tech roundup report reveals that a significant shift in generative AI usage is anticipated in various sectors. By 2025, generative AI is projected to play a pivotal role, generating 30% of marketing content, substantially augmented by human input, a substantial leap from the meager 2% in 2022. Advanced virtual assistants (VAs) are also undergoing a transformation. Over 50% of VAs are predicted to become specialized for specific industries, a considerable rise from the earlier 25% in 2022. Generative AI and foundation models will transform software products in two years, driving up productivity, augmenting customer support, and decision intelligence across diverse domains.

Generative AI assistants are already being utilized to transform how customers search for products. For example, with a chat/voice-based interface, customers can request a recipe and receive product recommendations with a list of ingredients used in the recipe. This simplifies searching for ingredients while improving sales and customer satisfaction.

The latest advancements in ChatGPT/Azure OpenAI, like plugins and code interpreters, open yet more opportunities for retailers to sharpen their targeting and complement the productivity of retail associates.

A key impact area of Generative AI has been the evolution of retail media networks. This new and alternate revenue channel for retailers is already creating a positive impact on the bottom line for leading retailers and will remain the focal point of innovation for online, offline, and in-store targeting. The result? Targeted and personalized ads get better, costs drop, and shopping gets smoother.

While retail media networks seize the spotlight, other use cases further underline AI’s importance in retail:

  • Driving autonomous store operations: AI-driven automation reimagines store operations, allowing employees to amplify customer satisfaction. Automated restocking, predictive maintenance, and theft prevention guarantee secure and efficient store functions.
  • Powering fulfillment AI: Swift deliveries materialize through AI-powered adaptable supply chains. AI monitors demand, stock availability, and location, facilitating optimized fulfillment for unparalleled customer delight.
  • Enabling demand forecasting: AI-generated demand forecasts empower retailers to outpace competition. These insights recalibrate prices and fortify supply chain resilience, empowering businesses with a competitive edge.
  • Improving customer engagement – Chatbots and copilot assistants can power natural language conversations in customer support and eCommerce, simplifying shopping for customers.
  • Refining product merchandising – Generative AI can help create persuasive descriptions and utilize unstructured data from customer interactions to generate novel product improvement ideas, thereby improving product development and refinement.
  • Enhancing commerce – Generative AI can elevate the personalization game with specific situations and need-based product recommendations. It can generate SEO-centric product copies for websites and social media posts supplemented with generated imagery.
  • Improving associate’s experience – Generative AI can act as a trainer and copilot for the store associates providing micro training, enabling help with routine tasks, and empowering them with natural language queries for specific information. This can lead to improved associate morale and enhanced overall productivity.
  • Powering IT teams – Generative AI can help IT teams generate code and resolve issues faster, perform tests using LLM-generated test cases, and fix technical debt with code analysis.
  • Empowering data teams – The retail data teams can leverage Generative AI analytics features allowing users to query for insights using natural language. This capability can empower the teams with improved insights and accurate decision-support tools.
  • Driving supply chain resilience – Generative AI can ensure the amalgamation of data from various sources to interpret and visualize the multi-source data inputs into charts and graphs. Real-time visibility can fast-track decision-making, improve supply chain robustness, and enhance operational resilience.

Real-world application of GenAI:

Industry titans like Nestle and Unilever are at the forefront of this transformation through Generative AI, reshaping ad campaigns and product marketing. Collaborations among these companies are yielding innovation and cost-effectiveness, bridging creativity and efficiency in campaigns that resonate globally. Through the adoption of ChatGPT 4.0 and DALL-E 2, Nestle is harmonizing AI-generated ideas with human creativity, rewriting the norms of marketing excellence.

Retailers, however, must recognize that there are shortcomings in Generative AI models. They need to ensure a proper governance model to prevent any spread of misinformation due to hallucinations, inadvertent sharing of confidential information, or privacy breaches.

With the technological advancements continuing, retailers can expect more novel use cases for Generative AI. It is crucial for them to start preparing and building the foundations for an AI-first enterprise. Generative AI will undoubtedly play a leading role in revolutionizing retail experiences, and the time to create such experiences starts now.

Click here to learn how HTCNXT can help you leverage Gen AI in your retail operations with our ready-to-use AI platform MAGE.


Adnan Saulat

Senior Vice President, Consumer Services








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