Future-proofing sales in complex environments requires not just scale, but intelligent adaptability—and that’s where AI plays a defining role
Words by Karan Karayi
As India’s consumer landscape becomes more dynamic, companies must adapt faster, smarter, and with greater precision. For Rachit Vohra, Senior Director – Sales, South Asia at Kellanova, AI is no longer optional—it’s foundational. From demand forecasting to route optimisation and SKU-level precision, he shares how Kellanova is embedding AI into every layer of its go-to-market strategy.
How is Kellanova using AI to drive sharper sales strategies and make go-to-market plans more agile and insight-led?
At Kellanova India, AI has become a critical enabler in shaping agile, insight-led sales strategies. We are embedding AI at multiple points in our go-to-market engine—from predictive planning to field execution. For example, our Suggestive Order Module (SOM) uses AI to recommend SKUs tailored to each outlet’s buying history and seasonal trends. This improves assortment accuracy, reduces sales loss, and enhances order relevance at the last mile.
Moreover, we’re piloting AI-powered budgeting tools that dynamically allocate trade spends based on past performance and forecasted ROI. This allows our teams to plan smarter and deploy resources with greater precision. In a dynamic market like India, agility is everything—and AI is helping us respond faster, plan better, and execute sharper.
In what ways has AI helped you understand shopper behaviour better across modern trade, general trade, and digital channels?
AI is unlocking a 360-degree view of shopper behavior by connecting data across formats and touchpoints. In modern trade, for instance, AI models track store-level sales trends and help tailor promotions based on category velocity and footfall patterns. In general trade, our SOM tool learns from outlet-level ordering habits and refines assortment recommendations over time, improving on-shelf availability and repeat purchases.
On digital platforms, AI helps us analyse consumer interactions—from keyword searches to content engagement—giving us sharper insights into preferences and price sensitivities. For instance, a search like “best breakfast cereal for kids” not only signals category interest but also helps us understand the consumer’s priorities, such as nutrition and child appeal. When layered together, these insights allow us to build more responsive, data-informed campaigns and drive greater personalization across retail environments.
With India’s retail landscape becoming more complex and omnichannel, how do you see AI helping future-proof sales operations in such a dynamic environment?
India’s retail landscape is vast, diverse, and increasingly omnichannel. Future-proofing sales in such an environment requires not just scale, but intelligent adaptability—and that’s where AI plays a defining role. We’re using AI to bring structure to this complexity, from geo-tagging millions of outlets to optimizing visit frequencies based on billing behavior.
As channel boundaries blur, AI will become the bridge—helping us manage channel-specific nuances while maintaining cohesive execution. Whether it’s evaluating the ROI of a digital promotion or deciding beat plans in a rural town, AI ensures that decisions are backed by data, not guesswork. It empowers us to scale with confidence and stay ahead of disruption.
What are some tangible ways AI is improving frontline sales execution—be it through route optimization, demand forecasting, or real-time decision-making?
AI is directly impacting how our frontline teams operate. Our Smart Beat Optimization model uses machine learning to redesign outlet coverage patterns based on performance and billing frequency. Through a strategic route planning with F2/F4 model—where outlets are visited fortnightly or weekly depending on their potential—we’ve increased customer-facing time on high-performing stores without expanding our field force. This has helped us unlock access to over 50,000 additional outlets in 2024 alone.
AI also enhances real-time decision-making. For instance, the integration of suggestive selling tools into our DMS enables sales reps to receive intelligent SKU prompts during their outlet visits. Combined with dashboards and predictive analytics, this ensures that our teams are not just capturing orders—but doing so in a smarter, more effective way.
