From Inventory to Intelligence: AI Redefining the Retail Sector

Debates about AI's transformative potential in retail are raging. It's vital to critically assess AI's necessity and consider what has changed creating the need for using AI in such an integral manner. Over the past two decades, the retail industry has undergone profound changes and faced immense disruptions. At a macro level, consumers now have greater spending power and far greater access to information and influence than ever before thanks to the internet. Several startups have cropped up addressing many under-served consumer needs via the thriving eCommerce landscape and infrastructure in India. This has fundamentally shifted underlying business building blocks – demand trends, channels of service, customer service expectations and value perceptions. 

With so many micro-demands, variables driving consumer choices and the need for retailers to be omni-present and accessible 24X7, retail operations have shifted away from human only operations to AI enabled one. In fact, use of AI in business no longer needs a business case to be made, it is almost a hygiene. 

Probably the area where the acknowledgement of AI use has been strongest is supply chain. Leading fashion retailers have invested billions of dollars in building robust supply chain capabilities driven by AI at the core. Trend forecasting, demand forecasting, dynamic allocation and logistics management are some of the key use cases that have been driving down the days of inventory for retailers (leading retailers have reported 20%-25% reduction in days of inventory due to better demand forecasting and intelligent operations). Many of them are now shifting towards a demand sensing-led inventory replenishment while demand forecasting is used for long term inventory projections and planning, e.g. leading global retailer uses demand sensing to analyze real-time data from its point-of-sale systems, social media, and weather patterns. This helps them quickly adjust inventory levels and respond to changes in customer demand, ensuring products are available when needed.

On the consumer side of the value chain, AI has had a significant impact in how consumers shop today. Starting from beauty retailers using AI to create virtual / augmented looks to seamless omni experience enabling a truly delightful journey for shoppers of sports and equipment retailers, AI has been a cornerstone of the next generation of consumer experience. Armed with data from POS, preferences from DTC/app, historical purchases and trends, today retailers know a lot more about their loyal consumers. This ability is critical in creating repeat purchases and monetizing the initial investment made in acquiring those consumers. 

In this competitive landscape, customer acquisition costs are continuously rising. The critical difference between a sustainable vs an unsustainable business is their ability to increase the LTV (Lifetime Value) through personalized experience, tailored offers and an agile set of services keeping the consumer in the center. However, there are also several promises which didn’t pan out in the way we thought they would e.g. magic mirror usage in fashion or use of AI in-store clienteling. However, with Gen AI, we are seeing a renewed interest in the virtual assistants which are much smarter and almost akin to humans assisting in shopping in the virtual world. 

The side of business which often doesn’t find as much mention but is getting transformed rapidly specially on the back of Agentic AI are the all-pervasive support functions such as Finance and HR. Army of agentic bots are enabling corporations to get rid of manual processes. They help reconcile financial entries, support regulatory and compliance requirements and much more. Agentic AI is expected to address the automation needs of 60%-70% of processes which are under-served by current RPA (Robotic Process Automation) capabilities. 

However, while AI seems like a magical solution for many challenges, several hurdles remain. The lack of AI-trained talent and resistance to change in daily workflows are significant barriers. Only 36% of enterprises have allocated resources for Gen AI for their employees in India. This is in stark contrast to some of the more developed nations. With so much data coming in, retailers’ liability towards consumer data privacy, being responsible with the data is also increasing manifold. Not having adequate processes and infrastructure makes them an easy target for ransomware and cyber-attacks.

AI in retail is past being a “fad” and is now core to business growth. However as with any major transformation, it brings challenges. Finding the right journey will be critical for each organization and their leadership.