About

Every data leader faces the same fundamental tension: the business demands data products faster than engineering capacity allows. While our current delivery frameworks provide essential governance, scaling them to meet modern demands often requires a linear, and expensive, increase in headcount. The solution isn’t to abandon our engineering disciplines, but to supercharge them.

In this session, we will unpack the transition to the AI-Augmented Data Delivery Lifecycle. Moving beyond the hype, we will share practical examples of how leading teams are utilizing context-driven agents and automation to optimize the most time-intensive phases of delivery—from conceptual modelling and pipeline generation to robust testing and CI/CD.

Attendees will leave with a clear blueprint for integrating AI into their existing workflows, reducing manual engineering overhead, and freeing their teams to focus on what truly matters: high-value architecture and driving strategic business impact.

Takeaways:

• The Evolution of Delivery: How to seamlessly integrate AI into your existing development, testing, and documentation workflows to drastically accelerate deployment cycles—without sacrificing quality or governance.
• The Augmented Lifecycle in Practice: A pragmatic, step-by-step blueprint mapping today’s standard phases (requirements, modeling, build, deployment) to an automated, agent-assisted future.
• Building the Foundation for AI: The critical architectural prerequisites you need right now—including semantic layers, ontology design, and rigorous quality gates—to make reliable, AI-driven delivery a reality.
• The Leadership Mandate: How adopting an augmented model fundamentally shifts your team’s value proposition, reducing operational drag and radically accelerating time-to-market for new data products.