Arun Rasika Karunakaran is a Retail AI Product Manager at Tata Consultancy Services Ltd based out of Atlanta, USA.
In this enlightening interview, we delve with Arun Rasika Karunakaran into the transformative power of AI in retail merchandising. With over 11 years of experience in AI-led retail product transformations, Arun Rasika Karunakaran shares insights into her pioneering work with Fortune 500 retailers and her vision for the future of retail driven by AI technologies.
Your work has been instrumental in leveraging AI to revolutionize retail merchandising. Could you share how AI-driven insights have reshaped merchandising strategies and enhanced operational efficiencies?
AI has fundamentally transformed how retail merchandising operates by enabling deeper insights and automation at scale. My product initiatives have focused on integrating advanced AI tools to analyze shopper behavior, optimize merchandising, and drive sustainability in operations. For instance, by implementing AI-based space and assortment optimization solutions, we’ve seen 5-7% business benefits across various retail segments. This has reduced overstock scenarios, localized assortment, and improved product availability, leading to increase in customer satisfaction. Furthermore, leveraging AI for real-time shopper insights allows retailers to offer personalized experiences, boosting customer engagement and loyalty.
Retailers often face challenges when adopting advanced AI technologies into existing systems. What key obstacles did you encounter in your projects, and how did you address them?
Adopting AI technologies in retail often involves challenges like data silos, system incompatibilities, and change management. One key obstacle I encountered was mitigating the data quality issues of diverse data sources to ensure AI models had reliable inputs. To overcome this, I used data quality assessment and normalization framework to ensure data quality and create scalable ecosystems. Aligning AI with business operations was another challenge; I addressed this through cross-functional collaboration and iterative testing to refine models for actionable insights. By combining robust integration tools with stakeholder engagement, I successfully embedded AI solutions into workflows, enabling retailers to enhance decision-making and improve operational efficiency.
Sustainability is a growing focus in retail. How have you integrated AI to address sustainability challenges in merchandising and supply chains?
Sustainability is a key area where AI has immense potential. I’ve worked on projects that use machine learning algorithms to optimize retail store space, assortment there by reducing wastages and carbon footprints. For example, by analyzing assortment and inventory patterns, we reduced the wastages and improved the shelf space utilization by 15-20%. AI also plays a pivotal role in waste management—predictive analytics helps retailers minimize unsold inventory, cutting down on landfill contributions. In merchandising, AI-powered tools evaluate the sustainability of sourced materials, ensuring alignment with ESG goals. These solutions not only benefit the environment but also enhance brand reputation and operational cost-efficiency.
One of your focus areas has been shopper insights. How has AI enhanced the way retailers understand and engage with their customers?
AI has revolutionized shopper insights by enabling real-time data analysis and hyper-personalization. By integrating AI-driven analytics platforms, retailers can now capture granular data on shopper preferences and behaviors. This has led to the creation of micro-segmented merchandise strategies that resonate with individual shoppers, increasing conversion rates. Tools like computer vision in stores provide insights into foot traffic patterns, helping optimize layouts and product placements. These innovations empower retailers to anticipate shopper needs, creating more engaging and fulfilling customer experiences.
Looking ahead, what do you see as the next frontier in AI-driven retail merchandising? How will these innovations shape the future of the industry?
The future of AI-driven retail merchandising lies in predictive and prescriptive analytics, coupled with advancements in machine learning. Imagine systems that not only predict what shoppers will buy but also recommend sustainable alternatives or design personalized offers based on real-time sentiment analysis. Retailers will also leverage AI to create immersive, hybrid shopping experiences, blending the physical and digital. Autonomous supply chains powered by AI will further streamline operations, reducing costs and environmental impact. These innovations will redefine retail, making it more adaptive, efficient, and customer-centric.
From the editor…
Arun Rasika Karunakaran’s visionary approach to retail and AI underscores her belief in technology as a catalyst for transformation. With a strong focus on merchandising, sustainability and operational excellence, her work continues to set benchmarks in the retail industry.
