Day 2 | Main Hall - Thought Leadership and Strategic Insights

As GenAI opens the door to limitless opportunities for businesses, large enterprises need to find ways to bring GenAI solutions into production rapidly, effectively, securely and at scale across diverse teams. In this session, explore how ThinkTank—Target’s GenAI development platform—enables teams to harness the full power of generative AI by promoting agility, accelerating innovation, and streamlining delivery. Learn about the origins of ThinkTank, how it empowers experimentation, and the transformative impact it has on enterprise-level initiatives.

In 1999, Anil Kumble etched his name in cricket history by taking 10 wickets in a single innings. But the story isn’t just about stats — it’s about focus, perseverance, and alignment. In this reflective conversation, Kumble shares what it means to stay consistent across decades, how to reinvent yourself through data, and why long-term thinking trumps short-term wins — be it in sports, leadership, or engineering modern data foundations.

In an era where data is abundant but insight is scarce, the true challenge lies not just in collecting information, but in empowering it to act. “Making Data Think” explores how organizations can move beyond passive dashboards to intelligent systems that reason, adapt, and predict. This session delves into the intersection of data engineering, AI, and decision intelligence — showcasing how contextual awareness, machine learning, and real-time feedback loops are transforming raw data into active, thinking partners in innovation. Whether you’re a data leader or a curious technologist, this talk will leave you reimagining the very role of data in the enterprise.

Addressing privacy, fairness, and bias while designing law bots for legal assistance.

In an era where the pharmaceutical industry is increasingly reliant on complex digital infrastructures and sensitive data, the threat landscape has grown both in scale and sophistication. This session will explore how AI-driven threat detection systems can be engineered to meet the unique challenges of the pharma sector—ranging from protecting intellectual property to ensuring patient data integrity and compliance with stringent regulations. Drawing from real-world implementations, the talk will delve into scalable architectures, real-time anomaly detection, and the fusion of domain knowledge with machine learning to proactively mitigate cyber threats at an enterprise scale.

Aniruddha (Ani) Ray is the Senior Vice President & the global technology lead for Agentic AI. He is also the global lead for Genpact’s Products and Platforms. He has been with Genpact for 8 months.

His primary role is to create business impact and design the agentic architectures for our productized solutions to pivot to the “Services as Agentic Software” paradigm. He is currently leading the charge in rolling out the Genpact AP Suite while creating the Genpact Agentic Factory for the future.

Ani has over 23+ years of experience in leading business and technology architecture to build and scale up products across companies like Accenture, EMC, IBM and GE. He started as a Data Engineer, evolved to become a Technology and Digital (Data, AI, Cloud) Architect and finally a technology strategy and innovation leader harnessing value for customers at the crossroads of business strategy and technology evolution.

Ani has an MBA in Strategy from IIM Ahmedabad and an M. Tech from IIIT Bangalore. He holds over 10 Global Patents and in the last 10 Years has done more than 25+ Tech Certifications across Architecture and Engineering (Cloud, AI, ML, Data, Analytics) across all major Cloud Hyper-scalers and leading Data & AI Vendors.

As organizations scale to serve the “nextillions” – the next billion+ users entering the digital ecosystem – data becomes both a powerful enabler and a complex challenge. This session explores how we can humanize big data by bridging the gap between massive datasets and meaningful customer experiences.
From hyper-personalization and intuitive decision-making to building trust through data ethics and compliance, the talk will highlight how businesses can move beyond numbers to truly understand and serve the diverse needs of emerging digital users. Real-world insights and case studies will demonstrate how human-centric data platforms, scalable architectures, and inclusive design principles are shaping the future of customer engagement at scale.

Developing reliable AI models requires high-quality data. Due to the shortcomings of traditional methods, there is a need to adopt data reliability engineering. This approach involves applying principles from manufacturing and considering systems as data factories. It entails significant adjustments to people, processes, and tools.
– Data Testing: Validates data through migration testing, pipeline certification, and big data reconciliation.

– Data Monitoring: Tracks data pipeline health using business rules, and handles exceptions in real time.

– Data Observability: Uses AI/ML for anomaly detection, compliance measurement, and defect rate prediction.

The talk will highlight these pillars—Data Testing, Data Monitoring, and Data Observability—showcasing their importance in preventing errors, ensuring compliance, and optimizing AI performance. Case studies and industry practices will demonstrate how to engineer quality data effectively.

The AI Hackathon on the Government e-Marketplace (GeM) brought together innovators to solve real-world public sector challenges using cutting-edge technologies—Generative AI, Deep Learning, and Machine Learning. This session showcases standout use cases that emerged from the hackathon, demonstrating how AI can enhance transparency, efficiency, and user experience on GeM.
At the heart of these innovations lies a solid Data Engineering foundation—powering everything from data ingestion and transformation to model training and deployment. Without it, these AI solutions wouldn’t scale or succeed. This talk will highlight how robust data engineering practices made the leap from idea to impact possible.