AI is transforming software engineering – but not by replacing engineers. The real shift is what matters most: Data, systems thinking, and how we create value.
This session explores how the Data Analytics Hierarchy of Needs, a practical framework inspired by Maslow’s model, explains why AI and machine learning success depends on strong foundations – from data collection and storage to governance, analytics, and action. Without these layers, even advanced AI tools fail to deliver meaningful impact.
It also examines how AI is reshaping roles across software engineering, including data engineers, data scientists, domain experts, and engineering leaders, as well as how AI coding agents are changing day-to-day expectations and team dynamics. As automation increases, success depends less on writing code and more on systems thinking, data readiness, and cross-functional alignment.
Attendees will leave with a clear view of how AI creates value, how roles are evolving, and how to stay relevant in an AI-driven future.
