How Gen AI is proving to be a game-changer for retail industry

In recent years, the retail industry has undergone a significant transformation, largely driven by advancements in artificial intelligence (AI). For quite some time now, retailers have been using AI for tasks like personalisation, demand forecasting, and improving supply chain efficiency. But the emergence of Generative AI is a game-changer. This new wave of AI technology is not just an incremental advancement; it represents a seismic shift in transforming the way modern retailers operate.

​​In this article, we delve into the use cases and far-reaching implications of generative AI in retail and inventory management, exploring how it is set to reshape the future of the retail industry in ways we are only beginning to comprehend.

Generative AI: Under the hood

Generative AI is a subset of AI that focuses on generating data or content, such as images, text, or code. It operates by learning patterns from vast datasets and then creating new, original content based on those patterns. The ability of Generative AI to create content marks a significant leap from earlier AI models, which primarily focused on prediction and probability scores. This advancement is primarily due to two key factors: advanced neural network architectures, and large-scale training data combined with computational power. In the context of retail, businesses can utilise the immense amount of data they have collected over years of digitisation efforts to harness the potential of Generative AI.

Use cases in retail & inventory management

• Enhanced demand forecasting

Accurate demand forecasting is a critical aspect of retail. According to this recent article, Generative AI can analyse historical sales data, external factors like weather, holidays, and even social media trends to predict demand patterns. This is a massive improvement over traditional demand forecasting models that were not capable of analysing complex patterns in vast, diverse datasets to provide more nuanced and accurate demand predictions.

• Personalisation at a new level

Another classic use-case of AI in retail is set to be turbocharged by Generative AI's ability to process vast amounts of unstructured data in real-time. This technology coherently brings together disparate customer insights, offering hyper-relevant retail suggestions to each shopper.

• Visual search

The ability of Generative AI to comprehend images similarly to humans enables online stores to offer their customers the option to search for products using images. This not only makes it easier for consumers to find the products they want but also helps retailers enhance user experience and boost sales.

• Customer engagement and support

Generative AI, exemplified by technologies like ChatGPT, can significantly enhance customer support in retail. It enables natural, relevant conversations, answers queries, explains products, and offers recommendations, thereby reinventing customer service roles. Solutions like Carrefour’s Hopla and Macy’s On Call, which are already operational, help customers find the products they want by chatting with an AI-powered assistant.

• Supply chain management

Generative AI surpasses traditional methods in optimising supply chain operations for retailers, particularly those with large SKU assortments, by leveraging advanced pattern recognition to forecast demand more accurately and manage inventory efficiently. While current solutions perform well in steady states, they often fail to adapt swiftly to market changes, a limitation Generative AI overcomes by ensuring an agile response to supply and demand fluctuations.

• Dynamic pricing

Generative AI can be used to adjust pricing in real-time based on factors like demand, competition, and inventory levels. Retailers can maximise profits by offering the right price at the right time.

• Innovative product design and merchandising

Generative AI stands to revolutionise consumer interactions in physical stores as well by enabling retailers to make data-driven decisions when optimizing store layouts and product placements. It can analyse shopper behaviour and preferences to design more effective store arrangements.

Implications of Generative AI

• Job transformation and creation

The integration of Generative AI in retail and inventory management has implications for the workforce. Traditional roles in inventory management and retail operations may require upskilling to adapt to the changing technological landscape. Retailers need employees who can work with AI-generated data, maintain algorithms, and ensure data accuracy. This creates opportunities for new job roles, such as AI data analysts, AI specialists, and algorithm maintenance experts.

• Renaissance of brick-and-mortar stores

Generative AI holds the potential to significantly enhance efficiency and reduce operational expenses in physical retail stores. Furthermore, it enables these stores to offer personalised suggestions to shoppers, thereby building a superior customer experience–an area where they have often lagged behind online retailers. Through the automation of demand forecasting, inventory optimisation, and personalised customer interactions, retailers can streamline operations, minimise human errors, and optimise resource allocation. Such efficiencies can result in cost savings, increased profitability, and a competitive edge in the market.

• Ethical and data privacy considerations

As generative AI relies on vast amounts of data, including personal customer information, retailers must pay careful attention to ethical and data privacy considerations. Secure data management, anonymization techniques, and compliance with regulatory frameworks are crucial to maintain trust with customers and protect sensitive information.

Conclusion

In conclusion, Generative AI is a game-changer for the retail industry. Its use cases, from demand forecasting to personalised marketing, have the potential to transform the way retailers operate.

By harnessing the power of generative AI, retailers can improve efficiency, enhance the customer experience, and gain a competitive edge in a rapidly evolving industry. This technology not only streamlines operations but also opens new avenues for customer engagement and business growth, especially the data-deprived physical stores. It empowers retailers to anticipate market trends, tailor their offerings, and respond proactively to consumer needs.

Generative AI in retail and inventory management is not just a technological trend, it is a strategic imperative for those looking to thrive in the digital age of commerce. As the field of generative AI continues to evolve, retailers who embrace this technology will be better equipped to adapt and innovate in an ever-changing landscape. The future of retail lies in the integration of these advanced technologies, transforming traditional practices into intelligent, data-driven strategies.

Ankit Narayan Singh is the Co-founder and CTO of ParallelDots, an image recognition solution provider for FMCG companies and retailers.