Certified AI Executive: Chief Financial Officer

What You Will Be Able to Do

• Explain what AI can and cannot do, test a team's or vendor's claims, and count the true cost of using it
• Choose where AI creates value, build a business case on measured baselines, and move pilots to scale or stop them
• Set accountability, risk controls and compliance for AI using recognized frameworks and regulation
• Lead the organization through AI: operating model, skills, workforce commitments, change and adoption
• Apply AI across the close, forecasting, payables, receivables, fraud detection and tax, with measures and safeguards that keep finance numbers accurate
• Keep internal control over financial reporting effective where AI is used, with evidence, auditor engagement and sound governance of model-based estimates
• Control AI costs with FinOps practice, appraise AI investments with NPV and risk-adjusted benefits, and track benefits without double counting
• Steward guidance, capital allocation, AI vendor contracts and finance talent so that the company's numbers and commitments stay sound

Modules

1. Part 1: Executive Core

   Part 1 is the same in every executive certification.

2. 1AI Essentials for Executives

   Leaders do not need to build AI, but they do need to judge it. This module explains the main kinds of AI system, what models can and cannot do, why data sets the limits, how AI costs behave, and the vocabulary and tests a leader needs to question a team or a vendor.

   • Rule-based systems, machine learning, generative AI and AI agents
   • What models do and where they fail: errors, bias, drift and explainability
   • Data, economics and the vocabulary for questioning teams and vendors
   • Spotting hype and testing vendor claims on your own data

   Exercise. List the AI tools and features already in use in your area, including those built into software you buy. For each, note whether it is rule-based, machine learning, generative or an agent, what data it uses, and who reviews its outputs before they are acted on.

3. 2AI Strategy, Value and the Business Case

   AI is worth funding only where it changes a measure that matters. This module covers where AI creates value, how to build and rank a portfolio of use cases, when to build, buy or partner, how to write a business case that counts the full cost and tracks realized benefit, and how to move from proof of concept to scale or stop.

   • Where AI creates value and how to measure it
   • Use-case portfolios: value, feasibility and risk; build, buy or partner
   • The business case: baselines, total cost of ownership, payback and ROI
   • Proof of concept, pilot and scale; why pilots stall; stage gates

   Exercise. Take one AI proposal now under discussion in your organization. Write down its baseline measure, list every cost it will carry over three years (including data work, integration, change, monitoring and review effort), name the owner of each benefit, and set the criteria for the next stage gate.

4. 3AI Governance, Risk and Regulation for Leaders

   AI needs clear owners, a view of what is in use and controls that match the risk. This module covers accountability structures, risk tiering, the main frameworks and standards, how AI and privacy law apply in the US, Canada and the EU/UK, and the confidentiality, security, vendor and incident risks that leaders must manage.

   • Accountability, the AI governance committee, RACI and the three lines
   • AI inventory, risk tiering and risk appetite
   • NIST AI RMF, ISO/IEC 42001, and AI and privacy law across jurisdictions
   • Confidentiality, AI security, vendor risk and incident response

   Exercise. List the AI systems and tools used in your area, including AI features inside software you already buy. For each one, name the business owner, assign a risk tier using the questions in this module, and note one control that is missing.

5. 4Leading People and the Organization Through AI

   AI value depends on how people and work are organized around it. This module covers operating models and leadership roles, AI literacy, workforce impact and honest commitments, change management, shadow AI and acceptable use, and how to measure real adoption and lead by example.

   • Operating models and AI leadership roles
   • AI literacy and workforce impact: tasks, redesign and reskilling
   • Leading change: Kotter's eight steps, involvement and resistance
   • Shadow AI, acceptable use, real adoption and communication

   Exercise. Pick one team that has access to an AI tool. Find out how many people used it in the last week, which tasks they used it for and what stops the others, then agree two changes with the team and measure use again a month later.

6. Part 2: Chief Financial Officer

   Written for chief financial officers.

7. 5AI in the Finance Function

   Finance is one of the richest places in a company to use AI, and one of the least forgiving of error. This module looks at AI from the CFO's seat across the close and reconciliations, forecasting and FP&A, payables and receivables, fraud and anomaly detection, and tax: where it helps, how to measure it, and which safeguards keep finance numbers accurate and defensible.

   • Matching AI techniques to finance processes and measures
   • AI in the close, reconciliations and FP&A forecasting
   • Payables, receivables, fraud detection and AI-enabled fraud
   • Tax work and the finance operating model for AI

   Exercise. List your finance function's ten highest-volume or most time-consuming processes. For each, record the current measure (such as days to close, cost per invoice or forecast error), whether AI would propose, decide or only explain, and who would review its output. Pick the two with clean data, a clear owner and a measurable baseline as your first candidates.

8. 6Controls, Audit and Financial Reporting with AI

   When AI touches the numbers, it touches internal control over financial reporting. This module covers how AI fits into control frameworks and certification regimes in the US, Canada and the UK, how to design controls and keep evidence for AI-assisted processes, what external auditors will expect, and how to govern model outputs that feed accounting estimates.

   • AI inside internal control over financial reporting
   • Certification regimes in the US, Canada and the UK
   • Control design, evidence and the audit trail for AI
   • Auditor expectations and model outputs in estimates

   Exercise. Map every AI tool that touches your financial reporting to the accounts and processes it affects. For each, note whether it performs a process step, performs a control or produces information used in a control, who reviews its output, what evidence is kept, and whether your external auditor knows it exists.

9. 7Valuing, Funding and Controlling the Cost of AI

   AI spending behaves differently from most technology spending: much of it is consumption-based, it grows with use and it is spread across many budgets. This module covers how the CFO brings AI costs under control with FinOps practice and an understanding of pricing models, appraises AI investments with methods beyond simple payback, and tracks benefits so that they are real and counted once.

   • Why AI costs behave differently, and how to account for them
   • FinOps for AI, pricing models and token costs
   • Investment appraisal: NPV, risk-adjusted benefits and options
   • Benefits tracking, double counting and governing AI spend

   Exercise. Gather last quarter's AI spending from every source: cloud bills, software add-ons, vendor contracts and expense claims. Allocate it to use cases, calculate a unit cost for the three largest, and compare the benefits claimed for them in the benefits register with what appears in the budget.

10. 8The CFO as Steward of Capital, Guidance and Talent

    The CFO is the steward of the company's numbers, its capital and much of its commercial discipline. This module covers using AI forecasts responsibly in guidance and investor reporting, reporting AI spending and returns consistently, applying capital allocation discipline to AI, negotiating AI vendor contracts and pricing, and building a finance team with the skills to work with AI.

    • AI forecasts, guidance accuracy and forward-looking information
    • Reporting AI spending and returns in investor materials
    • Capital allocation discipline and post-investment reviews
    • AI vendor contracts, pricing and finance talent

    Exercise. Take your company's most recent earnings guidance and write down how it was built: which models fed it, which human adjustments were made and why, and where the basis is documented. Then list your five largest AI contracts with their commitment, price protection, notice of change and exit terms.