Enterprise AI

Chairpersons welcome and opening remarks

As organizations mature their data platforms, the focus is shifting from simply storing data to delivering trusted, reusable data products that drive real business outcomes. This panel explores the evolution beyond traditional data lakes, examining data mesh, product-centric ownership, governance at scale, and the technologies enabling faster, more reliable analytics and AI. Industry leaders will share practical lessons on what’s working today, common pitfalls to avoid, and how enterprises can design their next-generation data ecosystems for agility, value, and impact.

As Head of the Global AI Delivery Unit at NTT DATA, I will share what we have learned from delivering AI at scale across industries and how organizations can create value today while preparing for an AI-native future.

The biggest mistake organizations can make with AI is waiting for perfect data readiness. The second is deploying isolated use cases without the architecture and operating model needed to scale.

Successful AI transformation requires more than data and models. It demands a foundation that combines governance, business alignment, talent, and a scalable AI architecture. Through our AI Delivery Unit, we have learned that organizations accelerate AI adoption by standardizing how AI is delivered – not by waiting until every dataset is perfect.

A faulty application change often fails fast. A faulty data pipeline can succeed, write incorrect data, and quietly feed products, dashboards, or AI training until someone notices downstream.

At Datadog, batch data pipelines increasingly power analytical products and AI model training. Their authors are often product engineers, AI researchers, and analysts rather than data specialists. We are making AI coding agents the primary interface for data development. They help engineers move from intent through pipeline design, implementation, and testing, then diagnose failures when they occur.

Generating pipeline code is becoming easier. Trusting it is not. This forced us to rethink how agents test against production data without risking production, distinguish an intended change from a data quality issue, and investigate failures across the stack. This talk follows that journey through our Airflow and Spark platform.

We will share what works today, where autonomous workflows still require human review, and what we learned while opening data engineering to more engineers.

AI terminology is moving fast, making it harder to separate real innovation from repackaged API calls. In this session, Snowflake shares first-hand experience building on the AI Data Cloud to explain agents, skills, and harnesses through live demos and give you a practical framework for choosing the right AI building blocks.

How do you turn master data from an IT concept into a true business enabler in a global organisation like Shell?

In this session, Nathalia Beekman shares her journey of implementing Master Data Management at scale, navigating complexity, breaking silos, and creating a single foundation of trusted data across the enterprise. We’ll unpack the real challenges large organisations face, why MDM is often seen as the “unicorn,” and how we made it tangible and valuable.

You’ll hear practical lessons learned, proven best practices, and how we drive adoption by aligning IT and business perspectives so master data doesn’t just exist, but actively powers better decisions, efficiency, and impact across Shell.

Handing your engineers an AI copilot is easy. Getting your whole organisation to build better software with AI is the hard part. Once everyone is fast, the bottleneck just moves: to your product managers, your reviewers, your release cycle. The gap between your most and least fluent people widens instead of closing, and the output quietly drifts towards generic slop. Speed on its own is not the win.

This talk is the practical, under-the-hood story of how In The Pocket built an agentic software factory: 400 production skills, chained into workflows that run across the entire software development lifecycle, from research and engineering to quality and release. This is the real engine behind how we build today, and how it turned quality from our biggest risk into our biggest advantage. No theory and no hype, just the walls we hit and the system that got us through them.

Expect to leave with things you can apply when you’re back at the office. We’ll get practical about how to roll this out across teams and not just individuals, how to govern hundreds of moving parts with clear ownership and automated quality checks, how to close the gap between your most and least fluent people, and how to keep humans accountable for everything that ships. You’ll walk away with a real playbook for scaling AI-driven development across your organisation, not just another demo.
Building the right data pipelines, monitoring systems, and collaboration frameworks for sustained business value.

Cultural and technical enablers for scaling data-driven decision-making globally.

AI is no longer future technologies, it is reshaping how enterprises operate, compete, and grow. Yet many organizations remain stuck between promising pilots and real business impact.

This session explores how enterprises can successfully transform by embedding AI into their core strategy, operations, and culture. Moving beyond hype, we will look at what truly differentiates organizations that scale AI from those that stall: leadership alignment, data maturity, cross-functional collaboration, and a relentless focus on value.

Through real-world examples and practical frameworks, this talk will help leaders understand how to turn AI into sustainable transformation engines driving efficiency, innovation, and long-term resilience.

How do organisations turn data and AI into measurable business outcomes?

This discussion brings together product, platform, and business leaders to explore operating models that align data foundations with AI goals.

Case studies will show how cross-functional teams bridge silos, balance agility with governance, and deliver ROI from AI initiatives.