Abhilash Kulkarni

Abhilash Kulkarni

Senior Analyst - Insights & Analytics, Data Products

Dentsu Global Services

Abhilash Kulkarni is a Senior Analyst at Dentsu, where he builds and executes impactful solutions at the intersection of Generative AI, Machine Learning, and customer experience. With a five year track record of taking ideas from concept to completion, he is an expert at delivering measurable and sustainable business transformations.

He is driven by a fascination with blending technical expertise with a keen end-user lens, solving complex problems by making technology feel intuitive and effective. Outside of his work, Abhilash is an aspiring novelist, using a different kind of problem solving to build worlds and narratives, a passion that fuels his creative approach to solving real world business challenges.

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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