How to effectively learn AI Prompting, with the 'AI for Global Head of Finances (Prompt Course)'?

Lead Global Finance with AI: A Practical Course for Real-World Impact

Why this course matters for senior finance leaders

This course equips Global Heads of Finance, CFOs, and senior finance teams with a structured, reliable way to apply AI across core responsibilities-planning, control, reporting, risk, treasury, investment decisions, and transformation. It brings together practical guidance on governance, data use, prompt consistency, and decision support so you can speed up workflows, improve accuracy, and strengthen strategic outcomes without sacrificing oversight.

Across the modules, you will learn how to turn AI into a dependable co-worker: one that produces consistent outputs, follows your organizational standards, respects compliance obligations, and integrates with your teams' day-to-day tools and processes.

What you will learn

• AI fluency for finance leadership: Principles for setting AI policies, defining roles, and embedding AI in finance operations while maintaining control, auditability, and accountability.
• Prompt strategy that scales: How to build reusable prompt frameworks, align them with your reporting standards, and apply them consistently across budgeting, forecasting, analysis, and review cycles.
• Decision-quality outputs: Techniques to obtain structured results, clear assumptions, transparent calculations, and scenario alternatives that support board-level and regulator-ready documentation.
• Compliance and copyright discipline: Practical guidance on safe use of content, rights, and attributions, plus controls to keep sensitive data protected and compliant.
• Financial analysis excellence: Methods to improve budget optimization, cost-benefit assessments, portfolio and investment evaluations, and M&A analysis-supported by clear rationales and sensitivity testing.
• Risk and treasury applications: How to use AI for risk assessment, currency exposure analysis, and liquidity planning with repeatable templates and review checkpoints.
• Tax and regulatory confidence: Approaches for structuring tax planning and regulatory compliance work so that outputs align with jurisdictional requirements and can be reviewed efficiently by internal and external stakeholders.
• Reporting and visualization: Ways to produce consistent management reports, sustainability disclosures, and visual dashboards that follow your style guide and data definitions.
• Market and economic insight: Frameworks to synthesize market research and economic impact analyses, separate signal from noise, and connect insights to financial plans.
• Workflow automation: Steps for automating repetitive financial processes, setting guardrails for quality, and integrating with finance systems and fintech tools.
• Change enablement: Approaches for employee financial training using AI, ensuring adoption while preserving standards, ethics, and control.

How the modules fit together

The course is designed as a cohesive system. You will see how foundational modules on prompt design, custom instructions, ethics, and compliance support every downstream financial use case. Analytical modules-budgeting, forecasting, portfolio, M&A, cost-benefit, and economic analysis-feed into reporting, visualization, and executive communication. Treasury-focused content-cash flow, currency exposure, and liquidity-connect with risk management and investment decisions. Regulatory, tax, and sustainability modules ensure the outputs you generate are defensible and consistent with internal policy and external requirements. Finally, technology integration and automation modules show how to embed these capabilities in daily operations without losing oversight.

Using prompts effectively: proven practices

• Set context first: Define your role, objectives, constraints, and audience. Clarify materiality thresholds, reporting calendars, and data availability. This reduces rework and increases consistency.
• Specify structure: Ask for outputs in the formats you use-tables, bullet summaries, executive-ready briefs, risk registers, decision matrices, or policy checklists. Standard formats speed up review and integration.
• Make assumptions explicit: Require declaration of assumptions, sources used, and limitations. This supports audit trails, regulator discussions, and internal reviews.
• Calibrate with examples of your standards: Provide your style rules, chart preferences, terminology, and metric definitions so outputs align with your finance playbook.
• Use stepwise workflows: Break complex tasks into stages-scoping, data preparation, analysis, validation, and final packaging. This mirrors finance QA processes and improves reliability.
• Guard sensitive data: Avoid exposing confidential or regulated information. Where needed, use anonymized structures, synthetic samples, or internal tools that keep data within your controls.
• Build reusability: Convert frequently used instructions into templates and shared libraries. Version them, document usage, and set ownership for updates.
• Validate and benchmark: Compare outputs to known baselines or historical results, and require variance explanations. This keeps AI contributions grounded in reality.
• Integrate with your stack: Plan how AI outputs feed spreadsheets, BI tools, ERP, treasury systems, and RPA. Define handoffs and acceptance criteria to keep processes smooth.

Key areas covered across the course

• Strategy and governance: How to set AI usage policies, responsibilities, and escalation paths. Approaches to manage model variability, quality standards, and documentation.
• Legal, ethics, and rights: Copyright and attribution principles, acceptable use guidelines, and practical methods to reduce legal exposure while enabling creativity and speed.
• Prompt foundations and custom instructions: Building a consistent "voice" and role context for finance work, improving output clarity, and reducing prompt-by-prompt variance.
• Financial planning and analysis: Budget optimization, scenario planning, cost management, and forecast reviews with structured outputs that are easy to reconcile.
• Investment and portfolio decisions: Comparable analysis, risk-return reasoning, and performance reviews that can be tested under different scenarios.
• M&A and strategic initiatives: Screening, synergies and risks, integration considerations, and board-oriented communication.
• Market research and economics: Techniques to synthesize large volumes of information, produce balanced viewpoints, and link insights to financial implications.
• Treasury and currency risk: Cash flow planning, FX exposure mapping, hedging policy communication, and monitoring frameworks.
• Financial and sustainability reporting: Producing consistent narratives, aligning metrics with frameworks, and presenting data visually in ways leaders and regulators expect.
• Tax strategy and compliance: Structuring planning discussions and compliance checks so they are reviewable and easy to update as rules change.
• Automation and fintech integration: Identifying tasks suitable for automation, setting controls, and aligning AI outputs with existing technology and data pipelines.
• People and training: Upskilling finance teams with AI-driven learning aids and SOPs that standardize good practices.

Value for a Global Head of Finance

• Faster cycles with fewer errors: Convert recurring tasks into standardized workflows and reduce manual rework.
• Stronger decisions: Obtain structured, comparable analyses with clear assumptions and sensitivity views.
• Better governance: Maintain compliance, auditability, and documentation while expanding AI use.
• Consistent executive communication: Produce board-ready materials, visuals, and narratives that align with corporate standards.
• Scalable adoption: Roll out prompt libraries and guidance so teams across regions and functions work in a unified way.

How the course is structured

The course progresses from foundation to application to integration. You start with responsible use and prompt structure, then apply those principles across core finance areas. You conclude with automation, visualization, and team enablement. Each module builds on the previous one, reinforcing a consistent approach that reduces variability and supports internal controls.

Data integrity and control

Because finance work requires high assurance, the course emphasizes disciplined methods: versioned prompts, role-based access, clear data boundaries, and validation steps that mirror audit processes. You will learn how to set up review loops, maintain documentation that stands up to scrutiny, and create a change-management process for prompt updates as policies, regulations, or business needs evolve.

Practical outcomes you can expect

• Reusable prompt libraries aligned with your finance standards and reporting cadence.
• Clear workflows linking analysis, risk review, and executive reporting.
• Defined guardrails for confidentiality, copyright, and regulatory adherence.
• Integrated handoffs to your BI, ERP, treasury, and automation tools.
• Training assets that help teams apply AI consistently and responsibly.

Who should take this course

Global Heads of Finance, CFOs, Finance Directors, FP&A leaders, Controllers, Treasury leaders, Tax and Compliance leaders, and finance transformation teams. It is also valuable for strategy, investor relations, and data/automation leaders who collaborate closely with finance.

Why start now

AI can speed up finance work, but value depends on structure, controls, and alignment with your standards. This course gives you a complete framework to apply AI with confidence-across analysis, reporting, compliance, and automation-so your function can operate faster and with greater clarity while maintaining strong oversight.

Outcome: By the end, you will have a coherent approach to applying AI in global finance-supported by prompt strategies, governance practices, and workflows that help your team deliver accurate, consistent, and review-ready results at scale.