Day 2 | Hall 1 - Thought Leadership and Strategic Insights

Many organizations have made bold investments in AI, yet the journey from strategy to real-world execution remains a significant challenge. This session explores how enterprises can bridge the gap between ambition and impact, moving beyond pilots to scalable, production-ready AI. From aligning business and data teams to building the right infrastructure, governance, and talent, discover what it truly takes to operationalize AI and deliver measurable outcomes.

As GenAI & Agenitc systems are increasingly built on complex data pipelines, real‑time platforms, and shared data products, traditional governance models struggle to keep pace. For data engineering leaders, the challenge is no longer limited to data quality or access controls—it is about ownership across pipelines, platforms, and AI outputs. This session examines how modern data engineering organizations are redefining accountability, control, and trust for GenAI‑driven systems. Focusing on platform design, data contracts, lineage, observability, and privacy‑by‑design, the talk presents leadership‑level patterns that embed governance directly into data and AI pipelines for Enterprise scale—enabling scale, reliability, and responsible AI adoption without slowing down engineering velocity.

In the era of Generative AI and autonomous decisioning, enterprises are rapidly investing in AI
models. However, the reality remains stark most AI initiatives fail not due to model limitations,
but due to fragmented, unreliable, and non-AI-ready data ecosystems. This session challenges
the conventional narrative of AI being model-centric and repositions data as the true foundation
of AI success.

Enterprises are moving beyond traditional data foundations toward real-time, AI-ready ecosystems. This session explores how to modernize data architectures to enable faster insights and intelligent decision-making to solve business use cases at scale.

Many data platforms fail to deliver impact due to fragmented data and lack of clear business alignment. This session focuses on what truly moves the needle, use-case-driven strategies, better data quality, and integrated ecosystems that deliver real outcomes.

As audio data grows exponentially, building scalable storage systems becomes critical for efficient search and retrieval. This session will explore architectures and strategies to handle high-volume audio data while ensuring performance, reliability, and cost efficiency.

As AI rapidly reshapes how organizations build intelligence, this session explores what it truly means to go beyond the algorithm. Drawing from real enterprise experience at one of India’s most iconic consumer brands, the talk traces the evolution from classical analytics and ML to multimodal GenAI and agent‑driven decision systems. Using the end‑to‑end product lifecycle as a lens from design and manufacturing to go‑to‑market and continuous intelligence – it demonstrates how AI delivers impact only when grounded in strong data foundations, business context, and governance. The session also reflects on the changing role of data scientists into AI orchestrators, highlighting why system thinking, judgement, and business storytelling matter more than ever in the age of AI.

What if your data pipeline could generate user intelligence before a single real user touches your product? This session introduces Synthetic Users – AI agent swarms that simulate real human behaviour at scale and the data pipeline architecture that transforms their interactions into actionable product intelligence in hours, not months.

In the era of AI-driven decision-making, data quality has become more critical than ever. This session explores how poor-quality data can amplify errors at scale, leading to flawed insights and risky outcomes. It will highlight the importance of robust data governance, validation frameworks, and continuous monitoring to ensure AI systems are built on reliable, high-quality data.

Internal data platforms are becoming a core competency. The panel can explore how data engineering is borrowing from DevOps and platform engineering self-serve infrastructure, developer portals, golden paths and whether it’s working in practice.