This webinar explores why AI initiatives stall not at the model, but at the data pipeline layer. Adastra and AWS walk through a realistic enterprise scenario, showing how weak data engineering foundations derail production-ready AI. We introduce AI-DLC, Adastra's methodology for applying AI across the full development lifecycle, through a live demonstration using AWS Kiro. Viewers discover where their pipelines create risk, which disciplines separate shipping teams from stalled ones, and how to adopt AWS-native tooling safely.
Key Takeaways From the Webinar
- AI projects don't fail because of the model, they fail because of the pipeline.
- Methodology and discipline matter more than tooling alone.
- Spec-driven development makes data engineering predictable and scalable.
Led by Adastra and AWS, with expert insights from Seran Chandrapalan (Adastra), Nikole Rocha (Adastra), and Lesley Ajanoh (AWS), this webinar covers why AI initiatives stall at the data pipeline layer, how AI-DLC brings structure and governance to the full development lifecycle, and how AWS Kiro brings spec-driven development to life for production-ready AI.
Ready to see what it really takes to get AI into production? Watch the recording now.
