Artificial Intelligence is often seen as a breakthrough in models and algorithms. But in reality, AI became practical and impactful only because of one thing: data — and the infrastructure that supports it.

Although the concept of AI has existed since the 1950s, it is only in recent years — with the rise of cloud computing, scalable storage, and modern data platforms — that AI has become truly usable at scale. Without data, AI remains just a theory.

In real-world systems, data is often messy, fragmented, and undocumented — yet we expect AI to make sense of it.

In this session, we will explore why data is the true foundation of every AI system. While most discussions focus on tools, frameworks, and models, the real challenges lie in understanding, preparing, and managing data at scale.

Through real-world experiences working with decades of legacy systems, we will uncover the hidden complexities of data — including inconsistent formats, missing relationships, duplicate records, and lack of data ownership.

We will also connect these challenges to modern AI systems and show how data pipelines, validation strategies, and infrastructure design directly impact the success of AI initiatives.

This session provides a practical perspective on how data flows through real systems, where it breaks, and how to build a strong foundation before applying AI.

What you will learn:
Why AI success depends more on data than models
How modern infrastructure made AI practical
Common data challenges in real-world systems
A practical approach to preparing data for AI
How to think about data as a long-term asset, not just input