From augmentation to automation

1. From augmentation to automation

Finance teams run on processes - collecting numbers, turning them into insights, and helping business leaders make decisions.

2. Connecting

But these steps often happen in isolation. You might pull data from one system, analyze it in another, and share results in a separate report or dashboard. Each task might work fine on its own, but when they are disconnected, time, accuracy, and context can slip away.

3. How finance gets smarter

In modern finance, efficiency depends on more than just great analysis - it depends on great systems. By combining large language models with workflow automation tools, you can completely streamline financial processes - from data collection and reconciliation to report generation and decision support. But here's the catch: To do this effectively, you need to think in systems, not just steps. Systemic thinking helps you see how data, models, and people connect - and how one change affects the whole process. It's the mindset that turns a collection of tools into a powerful, reliable workflow. In this course, we won't be building workflows. Instead, we'll explore the systemic thinking behind them - the foundation that allows you to design AI-powered processes that are scalable, transparent, and ready for real-world finance operations.

4. The production line

Think of a workflow like a production line for finance work. Each station handles part of a bigger process. When stages are connected, work moves smoothly and errors are caught early. When they're disconnected, bottlenecks appear.

5. The month-end story

Here's a familiar example. It's month-end. You download data from your ERP, copy it into Excel, clean it, calculate variances, and write a short commentary. Each task takes time and focus, and you might spend hours rechecking formulas or formatting. With AI and workflows, you can extract your ERP data on a schedule, use an AI system to validate the inputs, use another AI system to draft commentary, and have an approval process before sharing with stakeholders. That's a workflow in action.

6. Workflows in finance

These workflows exist across forecasting, variance analysis, anomaly detection, and compliance. In every case, AI assists with structured, repetitive steps, while humans focus on reasoning and storytelling. This balance - humans leading, AI assisting - keeps finance both efficient and accountable.

7. Best practices

Every finance task is a process, so map the main stages: these could be data collection, validation, analysis, commentary, and review. Mapping these steps reveals where time is lost, effort is duplicated, or rework appears. Assign Roles. Decide which steps belong to AI and which need human judgment. Structured, repeatable tasks - like data checks, summaries, or variance commentary - fit AI perfectly. Humans step in at key checkpoints to validate insights, interpret context, and make decisions. This balance keeps automation accountable and ensures finance stays transparent and reliable. Use structured prompts. Good workflows depend on consistent instructions. Remember context, task, data, and output. For example: "Analyze budget vs. actuals and list the top three variance drivers." Structured prompting turns ad-hoc AI queries into dependable workflow components. Measure and refine. A workflow is only as good as its results. Track three outcomes: accuracy, efficiency, and risk control. Use these metrics to refine your process and show value to stakeholders. The goal isn't total automation - it's smarter collaboration between people and AI. By mapping, assigning, and measuring, you turn systemic thinking into tangible impact - building finance workflows that are efficient, auditable, and human-centered.

8. Scale and impact

Workflows can be small and personal, like using Copilot to reconcile expenses, or large and enterprise-wide, like integrating AI forecasting in SAP that feeds dashboards automatically. The goal is the same: connect reasoning, ensure quality, and make the process repeatable. By thinking in workflows, you move from performing isolated tasks to designing systems - systems that make finance smarter, faster, and more resilient.

9. Let's practice!

Let's put that into practice!

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