AI FinOps & Token Economics Lead

Demand & Investment Governance 

• Own the MBO framework for vendor token demand and consumption, including intake, demand forecasting and role- or workload-level requirements. 

• Define approval thresholds and the exceptions process, budget and funding accountability, and portfolio prioritisation across vendor AI use cases. 

• Operationalise the per vendor per month threshold, with exceptions requiring justification against contracted outcomes and measurable business value. 

AI FinOps & Consumption Management 

• Create a single view of vendor AI consumption and run a regular operating rhythm covering actual versus approved consumption, forecast and run rate, variance and outliers, optimisation opportunities and consumption controls. 

• Align with the emerging internal AI FinOps model, which focuses on visibility, allocation, optimisation and business outcomes rather than cost control alone. 

• Own FinOps and tokenomics for Copilot, Cowork, including usage visibility, allocation and optimisation. 

Value Realisation 

Link token investment to measurable outcomes through a clear value chain: 

Token investment  →  AI capability  →  Productivity / capacity  →  Business KPI  →  Financial value 

Vendor Commercial Governance 

• Connect token investment to the broader vendor commercial model, determining where Microsoft versus the supplier should bear AI consumption cost. 

• Ensure AI productivity is reflected in supplier capacity and commercials, so token investment does not become incremental vendor cost. 

• Incorporate token economics into SOWs, renewals and vendor transformation conversations. 

• Apply the existing framework that distinguishes a Microsoft-directed requirement, a supplier productivity choice and a joint transformation investment. 

Optimisation & Controls 

• Drive continuous optimisation of how vendors consume AI: using the lowest-cost capable tool or model, setting appropriate limits, identifying abnormal consumption and eliminating unnecessary usage. 

• Move workloads towards more efficient models and configurations, with specific focus on long-running agents, recurring automations, parallel sessions, large contexts and unnecessarily expensive models. 

• Recommend increased investment where the economics justify it. 

Token Strategy & Operating Model 

• Evolve MBO towards treating AI as a form of capacity investment alongside vendor headcount, embedding token economics in planning, prioritisation and vendor strategy. 

Required 

• Bachelor's degree in Finance, Business, Economics, Engineering or a related field AND 4+ years of experience in FinOps, cloud or technology cost management, FP&A, or vendor/commercial management, OR equivalent experience. 

Preferred 

• 6+ years of experience in FinOps, technology cost management, FP&A or commercial management. 

• Hands-on cloud or AI consumption FinOps experience (e.g. Azure cost management, token- or usage-based pricing). 

• Understanding of large language models (LLMs), AI agents and token economics. 

• Experience with vendor or outsourcing commercial models, SOWs and renewals. 

• Deep forecasting and variance reporting skills using tools such as Power BI and Excel. 

• FinOps Certified Practitioner or similar certification; excellent executive communication and influencing skills. 

Key Competencies & Behaviours 

The role calls for strong commercial acumen and data-driven decision making, together with the ability to influence without authority and to manage stakeholders and vendors with confidence. We are looking for structured problem solving, a growth mindset and a customer-obsessed focus on business outcomes, working as One Microsoft across organisational boundaries. 

Success Measures (First 6–12 Months) 

• Token governance framework and per vendor per month threshold and exception process live. 

• A single view of vendor AI consumption in place, with a monthly operating rhythm running. 

• Forecast accuracy improving, with variance and outliers actively managed. 

• Documented optimisation savings from model, configuration and usage changes. 

• Token economics embedded in SOWs and renewals. 

• Value realisation reporting in place, linking token spend to business KPIs.