Consumption-based pricing for enterprise artificial intelligence (AI) coding tools has broken the seat-based budgeting assumptions on which enterprise software procurement has long relied. Documented 2026 cases illustrate the mismatch: Uber exhausted its annual AI coding budget four months into the year, Microsoft began cancelling internal AI coding licences weeks later, and an Nvidia executive reported that AI compute now costs more than the engineers who use it. This paper reframes these events not as failures of AI, but as evidence of a structural governance gap: adoption succeeded while cost governance did not. Using a multiple-case synthesis of secondary evidence combined with a design-science construction of a management framework, the paper proposes a Tiered Access and Proficiency Framework that couples three disciplines already present in most enterprises—financial operations (FinOps), skills development, and security governance—into a single system in which demonstrated proficiency determines access, access determines cost, and cost is monitored by existing FinOps infrastructure within boundaries set by recognised governance standards. An illustrative financial model for a 2,000-engineer organisation estimates a 42% first-year and 51% steady-state cost reduction while retaining approximately 87% of productivity value, with the value-to-cost ratio improving as the workforce matures. The framework is positioned as an integration layer rather than new bureaucracy, mapping proficiency to the Skills Framework for the Information Age (SFIA) and governance to the NIST AI Risk Management Framework and ISO/IEC 42001. Limitations, applicability boundaries, and implementation risks are discussed.

Keywords

Enterprise AI governance, FinOps, AI proficiency, cost management, and operations management.