Principle AI FinOps & Tokenomics; Consumption Intelligence, Cost Governance & Value Realization

Position: Principle AI FinOps & Tokenomics (Consumption Intelligence, Cost Governance & Value Realization)
Our vision is to transform how the world uses information to enrich life for all.

Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As the Principle AI Fin Ops & Tokenomics (Consumption Intelligence, Cost Governance & Value Realization) at Micron, you will lead a groundbreaking initiative. This role focuses on making AI consumption transparent, predictable, and highly effective. You will collaborate with our AI Operations Center of Excellence and departments like Finance, Procurement, and business units to establish financial governance for AI consumption.

Your mission is to ensure that Micron can grow AI rapidly while maintaining strict financial rigor. You must ensure every dollar spent on AI is justified by measurable business value. Join us to lead this ambitious journey and be a part of a world-class team!

Responsibilities

Consumption Intelligence & Financial Reconciliation

* Define and own the data and telemetry requirements connected to AI financial oversight. Specify which usage signals (token, GPU, inference, request, seat) must be captured, tagged, and made reconcilable. Partner with AI Operations, who operate the pipelines, to keep consumption data complete, accurate, and reconcilable to financial actuals.

* Define canonical financial units of measure (cost-per-credit,
-token,
-active-user,
-use-case-class,
-outcome), uphold the enterprise unit-economics model, and translate consumption trends, cost spikes, and forecast-variance drivers into business-readable financial narratives.

Role-Based Allocation Policy & Cost Models

* Define the role-based AI allocation policy and entitlement requirements - the financial logic mapping worker personas, work types, and value tiers to model access, token budgets, and tool entitlements - which AI Operations translates into enforceable platform constructs.

* Develop comparative cost models across AI models, vendors, and deployment patterns (API, dedicated capacity, fine-tuned, on-prem, edge) and quantify optimization opportunities (right-sizing, caching, tiered routing), defining the target requirements and business case while AI Operations owns technical implementation.

Vendor Cost Normalization & Commercial Partnership

* Normalize vendor pricing, metering, billing granularity, and SKU taxonomies into one FOCUS-aligned abstraction layer. Lead fiscal aspects in AI vendor negotiations and renewals. Partner with Procurement on discounts, reserved capacity, and cost-transparency clauses. Ensure vendor economics align with the internal credit model.

ROI, Payback & Benefits Realization

Co-owns with Finance the value realization field related to AI within the organization across the benefit portfolio - software development & engineering efficiency, agentic workflows & process automation, personal & knowledge-worker efficiency, customer & commercial outcomes, manufacturing/engineering/R&D acceleration, risk/security/compliance, and cost avoidance & capacity reclamation. Specifically:

* Define and maintain the enterprise AI Benefits Taxonomy and canonical measurement methods (hard-dollar, capacity-equivalent, cost avoidance, revenue, risk-reduction), with defensible baseline and attribution rules that hold up to Finance, audit, and executive scrutiny.

* Define the AI value realization framework - pre-investment argument, in-flight tracking, and post-implementation benefits realization review - for each material AI investment.

* Partner with use-case owners and Finance to convert benefit signals into booked financial outcomes (run-rate savings, capacity redeployment, plan reductions) and report enterprise AI return on investment, payback period, and value-per-dollar-spent at the executive operating cadence.

AI Ledger, Banker & Credit Model

* Own the enterprise AI credit ledger as the financial system of record for AI consumption (allocations, issuance, replenishment, draws, transfers, true-ups, chargebacks) and define banker/credit governance policy: issuance rules, replenishment thresholds, overage handling, reallocation approval, internal pricing, and period close.

* Define the credit guardrail requirements - the thresholds and conditions at which alerts, throttles, or cut-offs trigger, which AI Operations implements and enforces - and ensure showback and chargeback treatment aligns with corporate finance and accounting standards and remains auditable.

Analytics, Reporting & Executive Communication

* Own the enterprise AI cost-and-value analytics and reporting suite (dashboards, scorecards, exception alerts spanning spend, consumption, benefit, and forecast accuracy), and deliver executive reporting for the CIO, CFO, CISO, and BU GMs at operating reviews, steering committees, and board-level updates.

Business Unit Budgeting, Forecasting & Investment Governance

* Partner with BU finance leads to build, validate, and…