A 12-person data science team driving AI transformation across a $50B+ global conglomerate. Shashank Kadetotad, Global Sr. Director of Enterprise Data Science and AI at Mars, reveals how his lean team accelerates outcomes across hundreds of brands by building composable “accelerator” modules, defining criteria for failure before they start, and staying vendor-agnostic in a rapidly shifting AI landscape.
Shashank shares how Mars went from weeks-long deployments to days using standardized forecasting and optimization accelerators, why governance is the biggest enabler of scale rather than a bottleneck, and how the AI Lab they built provides a safe space for experimentation with built-in failure criteria. From pizza-team principles borrowed from Amazon to the discipline of killing pilots early, this episode is packed with practical lessons for any team trying to punch above its weight.
What
you'll learn
• How a 12-person AI team serves an organization of 100,000+ across supply, demand, finance, and HR
• Why governance is your biggest enabler of scale, not a bottleneck
• The “accelerator” approach: 15 reusable Lego blocks that cut deployment time from weeks to days
• How defining failure criteria upfront prevents pilots from dragging on for years
• Why staying vendor-agnostic and systematizing processes is the key to long-term AI adaptability
Links:
• Shashank Kadetotad on LinkedIn
• Mars
• Small Team Big Scale Podcast
Supported by
This episode is sponsored by qBotica — helping enterprises move beyond AI pilots and into real execution. Through qubi, its Agentic AI platform, qBotica brings together intelligent agents, automation, enterprise systems, and human oversight to transform complex workflows across healthcare, banking, insurance, real estate, and more. Learn more at qBotica.com →
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