Pavak Biswal

Pavak Biswal

Senior Manager - Insights & Analytics, Data Products

Dentsu Global Services

Pavak Biswal is a Senior Manager in Insights and Analytics at Dentsu Global Services and a 2025 “40 Under 40” awardee, recognized as one of India’s leading minds in Data Science and AI. With over 13 years of experience across retail, banking, telecom, and tech, Pavak has led high-impact solutions at the intersection of Machine Learning, Generative AI, and business transformation. His work blends deep technical expertise with a sharp business lens, making him a go-to expert for enterprise-scale AI transformation.

Beyond work, he’s passionate about mountaineering, combat sports, and making music—always exploring ways to fuse his personal interests with his leadership skills, and continuously pushing his own boundaries in both walks of life.

What if brands could detect when a loyal, high-value customer was about to walk away — and win them back before they even thought about leaving?
 
In this demo-driven session, you’ll follow the journey of Ben, a long-time brand loyalist whose disrupted purchase experience is transformed by a GenAI-powered CX Playbook. Through five critical AI capabilities: AIVA, Customer Pain Point Discovery, (CPPD), Orders at Risk, Route Intelligence, and Agentic AI Triage — Ben’s potential churn becomes a retention success.
 
From product search to post-purchase resolution, every step is proactive, connected, and intelligent. This is the future of customer experience at scale, where millions of customers like Ben are engaged, protected, and kept loyal through AI-driven precision.
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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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