The bottleneck in AI has shifted downstream. Data structure, access, and orchestration across complex environments now determine what can scale.
As AI systems expand in scope and complexity, gaps in data flow, inconsistent access patterns, and brittle pipeline design are becoming critical failure points. These issues surface most clearly under production workloads, where latency, reliability, and context continuity are non-negotiable.
This roundtable webinar explores how technical leaders are addressing these constraints by engineering a scalable foundation built to support modern AI workloads in production environments.
You'll Learn How To:
- Spot where AI systems break at scale in data flow, access, and latency
- Understand why traditional pipelines fail for generative and agentic systems
- Define what production AI architectures require
- Improve orchestration and reliability at the system level
See for yourself how Perforce Delphix can help you automate compliant data for DevOps.
Presenters
Vikram Kulkarni
Vikram Kulkarni is a global presales leader with deep expertise in helping enterprises turn data strategy and architecture into measurable business outcomes. He leads global presales and solution architecture teams that connect business value to technical capability in complex and regulated environments
Vikram specializes in areas like AI and LLM strategy, data strategy, DevOps and test data management (TDM) architecture, compliance-first design, and the complex enterprise sale.