On-Demand Event
AI initiatives rarely fail because of model quality. They fail because the underlying data systems were never designed for reliability, context retrieval, or operational consistency.
In fact, analysts predict that 60% of AI projects will be abandoned in 2026 due to lack of AI-ready data. As organizations move AI from experimentation into real workflows, the data engineering challenges fundamentally change. Pipelines must support continuously changing data, real-time retrieval, lineage tracking, and repeatable outputs. Without these foundations, AI systems drift, hallucinate, and produce results users cannot trust.
To explore the architectural patterns and operational practices required to make AI dependable at scale, DBTA brought together industry experts for a special roundtable webinar.
Watch the recording to learn:
Why AI accuracy is primarily a data engineering problem
How retrieval and context pipelines differ from traditional ETL
Designing pipelines for freshness, traceability and repeatability
Preventing drift and silent failures in production AI applications
