A Gartner report predicts AI coding expenses, driven by consumption-based token pricing, will exceed average developer salaries by 2028. Enterprises struggle with opaque billing and uncontrolled agent workflows, forcing CIOs to consolidate usage tracking and tie token consumption to business value.

• Gartner predicts AI coding tool costs will exceed average developer salaries by 2028 as consumption-based token pricing replaces flat subscriptions
• Only about 25% of C-suite leaders actually know what their AI systems cost to run, per KPMG
• Autonomous AI agents often run unmonitored for days, racking up premium-model token costs with no throttling
• KPMG recommends a "tokenomics" model tying usage to cost, ownership, and measured business value to avoid budget blindsiding

Data lakehouses combine low-cost, flexible storage with warehouse-style governance to power enterprise AI. Companies like DocuSign and Lemongrass use them to feed and train AI agents, but impose strict security reviews, access controls and audit trails. Vendors are adding vector indexing, MCP connectivity and semantic layers to ensure agents grasp business context and operate safely.

• Lakehouses now bolt on vector indexing and MCP connectivity so AI agents can directly query and be trained on enterprise data, with Gartner citing 65% client adoption.
• DocuSign restricts agent access to low-risk data (product specs, web content) while locking down customer records, running every dataset through ingestion and egress security reviews.
• Lemongrass is ditching its four-year-old custom-governed AWS S3 setup for a standard lakehouse, drawn by native Claude integration and zero egress fees when data and models share a cloud.
• Autonomous agents pulling data on their own (vs. per-use-case RAG permissions) demand new audit trails, role-based access and semantic controls to avoid runaway costs and compliance risk.