By Prabhath Nanisetty
AI is set to transform the retail industry, enabling retailers to leverage their data more effectively and craft AI-driven growth strategies. Prabhath Nanisetty, Industry Principal, Retail Data & Technology at Snowflake shares insights on how AI will be a key driver in reshaping the retail sector in the coming year.
Prediction: The generative AI wave will unleash a data science revolution in retail and CPG
While generative AI is currently capturing headlines, the technology adoption curve still exists and what we will see is transformation in retail and consumer goods that will come from the broader adoption of a data strategy that powers better data science and machine learning across organizations. This trend will allow companies to move beyond experimentation to operationalizing AI throughout their businesses. The focus will shift from consumer-facing applications like chatbots to foundational uses, such as faster decision making with fast-moving operational data, harmonizing disparate data sources for a more complete view of a market, and providing operators better tools to drive actions. This adoption will pull the entire industry forward on the technology adoption curve, with even traditionally less tech-savvy parts of the business such as finance or warehouse management embracing more data-driven approaches.
Prediction: Data clean rooms will enable new forms of collaboration and analytics across the ecosystem
Data clean rooms have exploded on the scene largely in the marketing and advertising area to provide privacy-preserving collaboration. As we look forward, the use of data clean rooms will expand beyond those applications to enable broader forms of secure collaboration across the retail and consumer goods value chain. This technology already allows companies to share sensitive information in controlled environments, and will continue to open up new possibilities for analytics and business intelligence in 2025. As an example, profitability data is often highly protected and often creates analysis air-gaps as a strong pricing model that drive revenue growth needs to be matched with profitability from another system – a data clean room could remove that gap which may allow for faster, more predictive outcomes. As it stands, the retail ecosystem is incredibly complex with various manufacturers, distributors, retailers, and more. Data clean rooms will help facilitate even more efficient operations across all parties, enabling novel insights that were previously impossible due to data silos and privacy concerns This trend will be particularly transformative in areas like supply chain optimization and collaborative planning, that have had limited access to data sharing capabilities prior.
Prediction: AI-powered upskilling will transform the retail workforce
There will be a significant shift towards AI-enabled tools that upskill and empower non-technical retail employees, from store associates to business analysts. This trend goes beyond simply providing AI assistants; it involves a continuous cycle of learning and capability enhancement. Technologies will emerge that allow employees to start with simple AI-assisted tasks and gradually progress to more complex data analysis and decision-making processes. This evolution will lead to smarter, more data-driven decision-making at all levels of a retail organization. For in-store workers, this means AI-powered tools for inventory management, customer service, and personalized shopping experiences. At the corporate level, it will enable business users to perform advanced analytics without needing deep technical skills, democratizing AI capabilities across the organization.
(The author is Prabhath Nanisetty, Industry Principal, Retail Data & Technology at Snowflake, and the views expressed in this article are his own)
