LF-20505 · AI Leadership series
AI Economics and FinOps for Leaders
Connect AI spending to accepted work, business value, and decision authority.
- 8 hours, including breaks
- Live, instructor led
- In person or virtual
The decisions this course helps you make
FinOps brings finance, technology, and business teams together to manage technology spending and value.
An invoice does not show the full cost or value of AI-assisted work. This course examines usage, shared costs, human review, rework, forecast uncertainty, and the evidence behind a claimed benefit.
Working from prepared data and calculations, compare options by cost per accepted outcome, test a funding recommendation, and decide what response to recommend when spending or value departs from plan. The shared case uses cost per accepted reply.
Who should attend
- Finance leaders, management accountants, and budget holders funding or monitoring AI use.
- FinOps practitioners and technology finance staff working with AI consumption.
- Product, portfolio, and procurement leaders accountable for AI spending or its continuation.
Before the course
- AI Judgment and Responsibility for Leaders (LF-20501), or documented equivalent experience.
- Comfort reading prepared costs, units, periods, and ratios.
- An optional numeracy and terminology check offers preparation support and does not affect enrollment.
- Calculations are supplied. No billing-system access or cost-model construction is required.
What you will practice
- Analyze the costs attributable to an AI workflow, separating usage-driven costs from allocated shared costs.
- Evaluate a supplied forecast when demand and assumptions change.
- Compare options using a consistent definition of cost and accepted work.
- Test a business-value claim against its baseline and distinguish time, capacity, and cash savings.
- Assign financial responsibilities and conditions for releasing funding.
- Recommend a response to a spending or value deviation within the supplied authority.
Course outline
From the first judgment to a defensible decision
Cost Anatomy of an AI Workflow
Examine supplied records for usage, review, rework, shared costs, and unsupported allocations.
Forecasting AI Cost Under Changed Demand
Test a prepared forecast and its uncertainty; practice examining minimum purchase commitments and costs of ending a provider contract early.
Cost per Accepted Outcome
Compare options using the same cost scope, period, and quality definition, including when the ratio cannot be calculated.
Testing the Value Claim
Challenge a benefit claim against its baseline, delivery costs, ownership, and supporting information.
Financial Accountability and Funding Gates
Assign budget, allocation, forecast, and benefit responsibilities, and specify conditions for further funding.
Responding to a Spending or Value Deviation
Write an individual funding and control recommendation, considering authority, continuity of service, and reassessment.
Practice and participant work
Prepared calculations keep the focus on interpretation and judgment. Worked examples and guided practice lead to individual cost annotations, a forecast review, an outcome scorecard, and a funding and control recommendation. Minimum purchase commitments and costs of ending a provider contract early are practiced with feedback rather than assessed.
Participant work: A review of a supplied cost forecast, an outcome scorecard, and a funding recommendation using prepared data and calculations. In the shared case, compare cost per accepted reply.
Delivery and tailoring
Live instruction is available in person or virtually. Prepared case materials support the exercises, with explanations, discussion, and feedback. The curriculum does not depend on one technology provider.
Tailoring can incorporate your approved strategy, policies, role names, and reporting conventions after consultation. Discuss your audience, scheduling, and preparation needs with Learn Flourish.
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Choose courses for the decisions you own. Check each course’s entry requirements; you do not need to take the whole series.
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