FinOps & AI Technology Economics Lead
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- Connect technology spend to ownership, business context and value
- Forecast, optimize and translate cost into product economics
- Govern AI economics and automate decisions across the technology estate
Benchmark: FinOps Foundation State of FinOps 2026 + current AI/cloud cost-management practice Market refresh: FinOps has expanded beyond cloud into AI, SaaS, licensing, data centers and private cloud; current practice emphasizes proactive architecture decisions, FOCUS-standard cost data, unit economics, forecasting, commitments, allocation/showback, token/GPU economics, automation and value attribution.
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Connect technology spend to ownership, business context and value
FinOps operating model + technology-value management
Coverage: Cloud Architecture, Migration & FinOps / Operating Models
Cost allocation, FOCUS + showback foundations
Coverage: FinOps / Data Modeling / Cost Allocation / Governance
Forecast, optimize and translate cost into product economics
Forecasting, commitments + workload optimization
Coverage: FinOps / Forecasting / Cloud Architecture / Optimization
Unit economics + product/business value metrics
Coverage: Unit Economics / Product Metrics / Financial Modeling
Govern AI economics and automate decisions across the technology estate
AI/GPU/token cost management + value attribution
Coverage: AI Product Economics / AI Infrastructure / FinOps
Cost intelligence automation + proactive governance
Coverage: FinOps / Automation / Governance / Platform Engineering
Role-Readiness Proof / Capstone
Create a FinOps operating model for a mixed cloud-and-AI portfolio. Normalize spend with a FOCUS-style model; build ownership/allocation and showback; forecast demand and commitments; define workload optimization and anomaly workflows; calculate unit economics for at least one product; model token, inference and GPU costs for an AI service; set budgets and guardrails; then deliver a quarterly technology-value review with investment, optimization and architecture decisions.
Proof Rubric
- Show reliable cost/usage data
- clear ownership and allocation
- forecast accuracy and scenario ranges
- commitment and workload-optimization logic
- unit-cost metrics tied to business outcomes
- AI token/GPU cost visibility
- architecture-level cost tradeoffs before deployment
- automated anomaly/guardrail workflows
- value attribution beyond savings
- executive-ready recommendations
- and evidence that finance, engineering and product incentives are aligned