In our first session, we established what MCP is: a standardized connectivity layer that sits in front of your architecture, not a replacement for what’s underneath it. In this session, BA Insight technical experts will discuss the decision that sits directly behind MCP: how your data is accessed, and what that choice costs you in performance, scalability, and control.
There’s a growing tension in enterprise AI architecture right now between two approaches. Real-time access via MCP reaches into source systems on demand: flexible, current, and easy to connect. Indexed architectures pre-process and enrich content to deliver the relevance, performance, and scale that enterprise environments require. Understanding where each approach excels, and where it breaks down, isn’t always common knowledge between stakeholders.
This session walks through both models honestly. We’ll cover where real-time access works well and where it introduces latency, cost, and growing dependency on underlying systems at scale. We’ll examine why indexing continues to underpin reliable enterprise AI, and what enterprises give up when that indexing is managed entirely by an LLM platform as a black box, with limited visibility or control over how content is prepared or ranked.
The key framing throughout: MCP is not an alternative to indexing. It’s a standardized interface that can sit in front of either architecture. The question isn’t MCP or indexing. It’s what sits behind MCP, and whether that architecture is built for what your agents actually need to do.
You’ll leave with a practical framework for evaluating cost, performance, and scalability trade-offs before you commit to an architecture, not after it’s already in production.
