How Kellanova Is Strengthening Its Internal AI Capabilities to Drive Business Impact

Kellanova is building out its internal AI infrastructure with a focus on scale, usability, and cross-functional adoption — part of a broader strategy to embed machine learning into core business decisions across marketing, supply chain, and innovation.

According to Ramesh Kollepara, Kellanova’s VP of Data Science & AI, the company is prioritizing centralized AI platforms that can be reused across departments, reducing the need for one-off pilots and accelerating time-to-value. The team is also investing in training and tooling to empower non-technical users to engage with AI confidently and ethically.

Key applications include demand forecasting, dynamic pricing, creative optimization, and innovation modeling — all aimed at improving agility and reducing decision latency. The goal, Kollepara says, is to shift AI from experimentation to execution, and from novelty to necessity.

corpbrief insight:

AI’s real power isn’t in the pilot — it’s in the process. Kellanova’s approach shows that internal capability-building is what turns data into direction and speed into strategy.