GenAI in finance: from prompt to decision

• Accountants
• Controlling Specialists / Controllers
• Financial Analysts
• Financial Reporting Specialists
• SSC/GBS Professionals
• Accounting Firm Professionals

• understands when it makes sense to use AI — and when it’s better not to;
• knows how to prompt the model in a way that delivers useful results, not just “smart-sounding” answers;
• can verify whether the model’s output is correct — both substantively and numerically;
• understands the boundaries of safe and responsible AI use;
• returns to work with a set of ready-to-use, tested prompts that can be applied immediately.

Module I: A Realistic View of AI — What It Can Do, What It Can’t, and Who Is Responsible

• Generative AI vs. rule-based automation — why this distinction matters in finance
• Overview of LLMs available on the market, including ChatGPT, Microsoft Copilot, Google Gemini, and Claude — key differences and how to choose the right tool for the task
• The AI Act from a user’s perspective — what obligations already apply to finance teams
• Where the model’s role ends and the finance professional’s responsibility begins — a boundary that should not be crossed

Module II: How to Work with AI to Get Meaningful, Not Just “Smart-Sounding,” Answers

• Why the same question produces different results across tools — and what that means in practice
• Building effective prompts with financial context: accounting standards, role, and expected output format
• Prompt libraries — how to create and manage reusable templates for KPI analysis, reporting, and management communication
• Hallucinations — where they come from and how to deal with them

Module III: Where AI Saves Time — Mapping Routine Work and First Implementations

• Diagnostic exercise: mapping repetitive tasks and identifying where AI can support them
• Automating summaries and notes — reducing time spent working with documents
• The trade-off between speed and quality — how to recognize and manage it
• Exercise: the same task completed with and without AI — what changed and what needed correction

Module IV: AI and Numbers — Analytical Support with Human Oversight

• KPI and budget variance analysis — how AI helps structure thinking about causes, not just numbers
• Forecasting and FP&A scenario analysis — AI as a comparison tool, not an oracle
• Financial storytelling and management commentary — turning dense tables into clear messages
• Fraud detection and anomaly identification — where AI signals, and where human judgment is required
• Exercise: full analysis using AI on a sample dataset

Module V: One Message, Different Audiences — Adapting Communication with AI

• Management reporting for different audiences: finance teams, executives, business units — different goals, questions, and narratives
• Automating reporting — reducing preparation time from hours to minutes
• Creating presentations and executive summaries from detailed analyses
• Exercise: transforming one document into three tailored versions for different business audiences

Module VI: Responsibility, Risk, and What You Take Back to Work

• The AI Act explained in plain language — what it means for finance professionals
• What data should never be entered into LLMs — working with sensitive information in line with company policies
• Three questions for every AI output: Is the source reliable? Does the logic hold? Is the risk acceptable?
• Audit and transparency — how to document AI-assisted work to clearly separate model output from human decisions

A workshop, not a lecture. The trainer introduces each topic, and participants immediately apply it on their own devices. Everyone works with the AI tool they already use or want to explore — there is no single mandatory platform.

Each participant receives the presentation in digital format, along with a set of ready-to-use prompts for everyday financial work.

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