Evolving from Modern Data Engineering to AI Readiness: An Enterprise Execution Blueprint Abstract
About this session
Every major organization is racing to capitalize on AI, but a consistent operational challenge has become clear: AI outcomes are completely dependent on the maturity of the underlying data ecosystem. In production, the limiting factor is rarely model capability. Instead, it is whether your engineering teams can reliably deliver trusted, clean, and structured data at enterprise scale. True AI readiness is not a standalone initiative; it is the natural extension of a disciplined, scalable data engineering strategy.
Drawing on experience leading global data engineering teams and driving modern analytics transformations within a high-volume, consumer-facing enterprise environment—including building Customer 360 and core restaurant data platforms—this session shares the practical mechanics of preparing an enterprise data footprint for AI.
Goal is to focus on the execution required to evolve from legacy, warehouse-centric architecture to flexible, cloud-first platforms like AWS and Snowflake, while managing real-world trade-offs in cost, complexity, and delivery velocity.
The discussion focuses on five practical pillars that shift an enterprise from basic data management to sustained AI capability:
* Transitioning to Enterprise Data Products: Evolving beyond fragmented, siloed datasets toward governed, reusable data products (such as a unified Customer 360 layer) with clear business domain ownership. * Architecting for Scale and Speed: Upgrading legacy systems to support the distinct performance, scalability, and near-real-time latency demands that AI applications introduce. * Operationalizing Data Trust: Embedding automated data quality checks, metadata management, and lineage tracking directly into pipelines so business stakeholders have confidence in AI-driven insights. * Leveraging AI within the Lifecycle: Safely integrating AI-assisted tools within the data engineering workflow itself to accelerate pipeline development, testing, and documentation under strict governance frameworks. * Cross-Functional Alignment: Structuring a cohesive operating model that aligns data engineers, analysts, and business executives around shared, measurable business outcomes.
Speaker
Key takeaways
- Attendees will leave with a grounded, practitioner-tested framework to evaluate their own data maturity, identify critical architectural gaps, and prioritize the exact engineering investments needed to scale AI from isolated experimentation to core enterprise capability.