Shashank Kadetotad
2026 Speaker

Shashank Kadetotad

Global Senior Director, Enterprise Data Science and AI

Mars

About

Shashank Kadetotad is the Global Senior Director of Enterprise Data Science and AI at Mars, where he leads the deployment of AI and advanced analytics across global consumer goods operations. With a background that spans Amazon operations, retail and CPG forecasting, and AI leadership across multiple Fortune 500 companies, he has built and scaled enterprise AI platforms that power decisions in supply chain, marketing, and finance. Shashank is recognized for his work on generative AI and multi-agent architectures, with multiple provisional patents focused on applying AI to product innovation, analytics, and consumer engagement. A frequent industry speaker, he is known for making complex AI topics accessible to business leaders while keeping the focus on responsible, high-impact AI in retail and CPG.

2026 Sessions

Oct 144:20 PM5:05 PM
Main StagePanel

(Panel) Beyond Immersive: How AI is Reshaping Enterprise Today and Tomorrow

Atrium Ballroom A

AI adoption has accelerated across nearly every enterprise function, but moving from experimentation to production-grade systems requires disciplined execution. In this panel, industry innovators share how they’re deploying AI today, measuring value, and managing risk, cost, and complexity over time. Rather than surveying trends, the discussion focuses on practical approaches to cost-effective experimentation, safe implementation, and robust governance. Hear how organizations are integrating AI into existing systems and workflows, building internal capabilities, and balancing automation with human oversight. Topics include: Use case selection : High-impact opportunities aligned with data, talent, and business goals Types of AI : Choosing the right approach (predictive models, computer vision, NLP, generative, agentic, physical AI)  Data readiness : Preparing, labeling, and governing data to ensure quality, accessibility, and security Model development & integration : Build, buy, or hybrid; training models and embedding into workflows Lifecycle management : Versioning, monitoring, and continuous improvement Risk & safety : Implementing human-in-the-loop and managing bias, drift, errors, hallucinations, and harmful outputs through validation, monitoring, and guardrails Infrastructure & cost : Scaling inference across cloud, on-prem, edge; optimizing performance, energy use, and cost Governance & responsible AI : Establishing policies for traceability, accountability, and regulatory compliance; aligning with organizational values Workforce readiness : Upskilling employees, building trust, and embedding AI into daily operations Measuring impact : Linking model performance to business outcomes like productivity, cost savings, and quality Scaling adoption : Standardizing tools and processes for reliable, repeatable deployment Use cases : Automation, workflow optimization, customer support, predictive maintenance, quality inspection, personalization, software development, cybersecurity, and more What’s next : Convergence of AI with XR, robotics, and digital twins; early agentic systems and autonomous workflows