The rise of AI-assisted tooling has introduced new pathways for natural language access to enterprise databases, beyond the classical NL2SQL paradigm. Oracle Autonomous Database supports two distinct mechanisms for natural language BI: Oracle Select AI, which embeds AI reasoning natively within the database using DBMS_CLOUD_AI, and Oracle SQLcl MCP Server, which externalizes database access to AI agents through a standardized protocol. Both reflect different philosophies regarding where intelligence should reside—inside the data platform or within the orchestrating agent. This paper presents a comparative architectural analysis examining four governance dimensions: reasoning locus, trust boundary placement, audit completeness, and workload fit. Drawing on deployment experience across mortgage lending and healthcare analytics use cases, we propose a practitioner decision framework for enterprise architects evaluating natural language BI strategies on Oracle Autonomous Database 26ai. We further examine hybrid patterns wherein the Oracle Autonomous Database MCP Server exposes Select AI Agent tools to external MCP clients, offering a convergence pathway between the two models.