CIOInsider India Magazine

Artificial intelligence is rapidly moving from an emerging technology to a strategic force reshaping the retail industry. In India, where consumers increasingly move fluidly between physical stores, websites, mobile apps and social platforms, AI is creating new possibilities for retailers to understand customers, anticipate their needs and build deeper, more meaningful relationships.

But the opportunity extends far beyond personalized recommendations and automated engagement. AI is challenging traditional notions of customer loyalty, enabling retailers to build a persistent understanding of individual customers across channels, reduce friction in the buying journey and, increasingly, act on customer intent in real time. At the same time, fragmented data, legacy technology, cybersecurity and the responsible use of customer information remain significant barriers to realizing that potential.

In conversation with CIOInsider, Abhishek Rungta explore how Indian retailers can use AI to rethink loyalty — moving from transaction-based programs towards intelligent, connected and trust-driven customer relationships. He also examines the technology and organizational foundations required to make AI work at scale, the metrics leaders should use to measure its business impact, and how the rise of agentic commerce could redefine the next generation of retail experiences.

1. How is AI fundamentally changing the way Indian retailers understand and build customer loyalty?
The biggest change is that AI is making traditional definitions of loyalty increasingly inadequate. A customer who purchases frequently is not necessarily loyal. They may simply be buying because the price is right or the store is convenient. AI allows retailers to look beyond transactions and understand signals such as browsing behavior, category affinity, frequency, responsiveness, channel preference and changing purchase patterns. For CEOs, the important shift is from asking, "How many members does our loyalty program have?" to "How much do we actually understand about why our customers return?" I believe the next competitive advantage in retail will come from what I call the customer memory layer, where every meaningful interaction improves the organization's understanding of the customer. A consumer should not have to reintroduce themselves every time they move from an app to a website, a support interaction or a physical store. Loyalty begins when the organization remembers, learns and responds consistently.

Also Read: Decoding India's E-Commerce Shift: AI, Speed & Real-Time Retail

2. How can retailers use AI to unify customer data across physical stores, websites, mobile apps, and social platforms to create a 360-degree view of the customer?
I would challenge the phrase "360-degree customer view" because many organizations have spent years building dashboards without actually creating a connected customer experience. The CEO should ask a much simpler question: if a customer does something on one channel today, can another channel intelligently respond to it tomorrow? If the answer is no, the organization does not really have a unified customer view.

AI can help resolve identities, recognize behavioral patterns and generate intelligence across point-of-sale systems, CRM, e-commerce, apps and service interactions, but only when the underlying architecture allows these systems to communicate. This is why AI strategy cannot sit separately from data and integration strategy. Before investing heavily in another personalization engine, CEOs should examine how many versions of the same customer exist inside their organization. Eliminating those disconnected identities can sometimes create more value than adding another sophisticated AI tool.

3. What role can generative AI and predictive analytics play in anticipating customer needs and delivering personalized recommendations in real time?
Retail has spent years trying to answer, "What should we recommend to this customer?" AI allows us to ask a more valuable question: "What is this customer trying to accomplish right now?" That distinction changes personalization considerably. Predictive analytics can estimate propensity, next-best actions and changing preferences, while generative AI can turn those signals into contextual conversations and experiences. The next stage is likely to go further, from recommendation engines towards intent engines. Instead of presenting ten products that an algorithm thinks someone might like, an intelligent interface could understand the requirement, narrow the choices, explain the trade-offs and help the customer complete the journey. For CEOs, that means measuring personalization not by how many recommendations AI generates, but by how much customer effort it removes. The best AI experience may actually show the customer fewer options, not more.

4. As retailers collect increasing volumes of customer data, how can organizations balance AI-driven personalization with data privacy, consent, and cybersecurity?
There is a line between personalization that feels helpful and personalization that feels intrusive, and AI makes crossing that line much easier. CEOs therefore need to treat trust as an economic asset, not simply a compliance requirement. My view is that retailers should adopt a value-for-data principle: whenever an organization asks for or uses customer information, there should be a defensible customer benefit attached to it.

Just because an AI model can infer something does not automatically mean the business should act on that inference. Boards should be asking what data their AI systems can access, which decisions they are permitted to influence, how consent travels when data moves between systems, and what happens when the model gets something wrong. In AI-driven retail, cybersecurity, privacy and personalization are no longer separate conversations. They are different dimensions of the same customer trust architecture.

Also Read: From Silos to Synergy: Building the Future With Agentic AI

5. What technology challenges such as legacy systems, fragmented data, and integration issues can hinder retailers from implementing AI-powered loyalty strategies?
The uncomfortable reality is that AI often exposes technology debt rather than solving it. A retailer may build an impressive conversational interface, but if that system cannot reliably access inventory, order history, product information, CRM and fulfilment systems, the experience collapses as soon as the customer asks it to do something meaningful. I would encourage CEOs to distinguish between AI that talks and AI that acts. The first can sit relatively easily on top of existing systems. The second requires integration depth.

This is where legacy architecture becomes a board-level issue rather than an IT issue. Retailers do not necessarily need to replace every legacy platform, but they do need an integration architecture that allows AI to securely interact with the systems where the business actually runs. Otherwise, companies risk creating what I call an intelligence layer sitting on top of an execution gap, where the AI understands the customer but cannot fulfil what the customer needs.

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6. What metrics should retail and technology leaders track to determine whether AI-powered loyalty initiatives are actually improving customer retention, engagement, and lifetime value?
One metric I would actively discourage CEOs from celebrating in isolation is engagement. AI can make customers click, open and interact more without necessarily making them more loyal or valuable. Every AI loyalty initiative should begin with an economic baseline before the technology is deployed. Depending on the use case, that might include repeat purchase rate, retention, churn, purchase frequency, customer lifetime value, conversion or cost to serve.

I would add another metric that receives far less attention: customer effort. If AI reduces the number of searches, screens, calls or steps required to complete an objective, it is creating tangible experience value. CEOs should ultimately be able to answer three questions about an AI initiative: Did customers stay longer? Did they accomplish more with less effort? Did the economics of serving them improve? If none of those measures moves, an impressive AI deployment is still an unsuccessful business investment.

7. Looking ahead, what AI capabilities will define the next generation of customer loyalty in Indian retail, and how should leaders prepare their organizations for this shift?
The next major shift will be from personalized retail to agentic commerce. Today, retailers use AI primarily to predict, recommend and communicate. The next generation of systems will increasingly be able to execute parts of the journey, such as finding a suitable product, checking availability, applying eligible benefits, coordinating fulfilment or resolving routine service requirements. That fundamentally changes the role of the digital interface from a place where customers navigate menus to an intelligent layer that helps them accomplish an objective. For CEOs, however, the priority should not be to deploy as many agents as possible.

It should be to decide what they are comfortable allowing an AI system to do on behalf of the customer and the organization. That requires connected data, APIs, identity management, cybersecurity, governance and clearly defined limits on autonomy. The retailers that lead the next phase will not simply know their customers better. They will build technology architectures capable of acting on that understanding responsibly, consistently and at scale.

ABOUT THE AUTHOR: Abhishek Rungta, Founder & CEO, Indus Net Technologies, is an Entrepreneur, Digital Strategy Consultant, Offshore Outsourcing Expert, Marketing & Technology Geek, Angel Investor, and Venture Builder. Over the last twenty-seven years, he has built digital innovation venture, Indus Net Technologies, which has a team of 750+ full-time professionals supporting 200+ active clients across Banking, Financial Services, Insurance, Retail, Publishing, Media, Government, Healthcare, and Entertainment sectors in five continents.