Why Enterprise AI Is Over Budget and Under-Delivering

Enterprise AI deployments are often exceeding budgets because usage-based token costs are difficult to forecast, spending is fragmented across vendors and teams, and organizations lack controls over how models and enterprise data are used. The article identifies three main gaps: inefficient routing that sends simple tasks to expensive frontier models, ungoverned data access that overloads models with unnecessary schemas and records, and insufficient cost attribution that prevents teams from identifying wasteful workflows. Citing McKinsey survey and research findings, it argues that AI FinOps practices, dynamic model routing, limited context windows, permission-filtered data access, request-level auditing, and proactive budget controls can reduce costs while improving governance. It presents AI gateways and related control layers as core infrastructure for connecting AI usage to business outcomes, enabling vendor flexibility, and enforcing security and accountability, while positioning CData Connect AI as a product designed to provide governed, schema-aware data access and audit logging.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.