You don’t need another article about AI. You need your first real AI win.

If you’ve been reading about AI and thinking "someday," but haven't taken real action yet, today’s the day to change that.

Last week, we talked about how to choose your first AI use case. This week, we are taking it one step further: validating your idea in just 30 minutes.

In this issue, I’ll walk you through a simple, practical exercise to find 3 to 5 places in your workflows where you can apply AI immediately for tangible results.

This exercise is designed to take only 30 minutes. Block a single half-hour on your calendar, follow the steps below, and you will walk away with a validated AI opportunity you can act on immediately.

Validate Your First AI Idea: How to Choose the Right Workflow

Most finance leaders already know AI is essential.
But few have the time to study it in depth.

The truth is, you do not need to master AI to start benefiting from it. You only need to:

• Spot tasks ripe for automation
• Test one or two simple AI tools like ChatGPT, Claude, or Gemini
• Capture a small, meaningful win

The 30-Minute Validation process helps you do exactly that, quickly, safely, and practically.

How to Validate Your First AI Automation

Step 1: List Potential Tasks (15 minutes)

Once you have listed potential tasks, use the following table format to assess them quickly across four simple criteria:

• First, note how frequently the task occurs, such as weekly, monthly, quarterly, or occasionally. Tasks that occur frequently offer the best opportunities for early AI wins.
• Next, assess whether the task is pattern-based. Mark "Yes" if the task follows a clear, predictable structure, such as standard reporting formats or routine reconciliations.
• Then, consider whether the task involves handling confidential information. If it clearly does, mark "Yes."
• Finally, make a preliminary judgment about AI feasibility. If you are unsure, mark "Check" for later testing.

Focus on moving quickly through this evaluation without overanalyzing. The purpose of this step is to filter out the obvious opportunities and identify a few tasks worth exploring in more detail during the next phase.

Step 2: Test Feasibility with a Language Model (10 minutes)

For each potential task, open an AI tool such as ChatGPT, Claude, or Gemini.

Describe the task in simple language and ask the model to outline step-by-step instructions to automate or simplify it.

Practical Example Tasks You Can Try

1. Weekly Sales Revenue Tracking Report

You are an AI assistant helping a finance analyst. I prepare weekly sales reports using ERP exports and salesperson revenue targets available in Excel or CSV files. Please provide a step-by-step plan to help me automate this reporting task using an AI tool like ChatGPT. I want to clean the data, match invoicing to targets, calculate performance metrics such as percent of goal achieved and remaining revenue to target, and format the final report for easy review in Excel.

2. Weekly Overdue Accounts Receivable Summary

You are an AI consultant assisting a finance team. Each week, we export accounts receivable data, including invoice numbers, client names, amounts, due dates, and aging buckets into Excel. Please outline how I could use an AI tool like ChatGPT to help automate the creation of a summary report showing total overdue balances by aging bucket (30, 60, and 90+ days) and highlighting the top 10 largest overdue accounts. Include steps for cleaning, grouping, and formatting the data.

3. Weekly Finance Team Meeting Agenda Preparation

You are an AI automation advisor supporting a finance leadership team. Every week, I need to prepare a meeting agenda based on inputs like Excel KPI dashboards, project management updates from Asana, and team email summaries. Please describe how I could use ChatGPT to automate drafting the meeting agenda, ensuring it includes KPI updates, project statuses, outstanding action items, and any new risks or issues, organized clearly and logically.

• If it recommends using tools you do not know or have, such as Python, Zapier, or Make, either re-prompt it to suggest simpler solutions or move on to the next task.
• If the proposed steps seem overly complex, such as requiring major reformatting, advanced scripting, or large data restructuring before even starting, move on.
  

The goal is to validate practicality, not to create more complexity.

Step 3: Identify Your Quick Wins (5 minutes)

Choose one or two tasks where:

• The AI’s suggested steps make sense to you.
• You already use the relevant tools (Excel, PowerPoint, Asana, ERP exports, etc.)
• Implementation feels manageable without major system changes.

These will become your first real AI wins.

Block 30 minutes this week.

Validate one or two real AI opportunities.

Get your first tangible AI win without needing an IT department or long project plans.

Remember, action beats endless research every time.

Top Mistakes to Avoid When Testing Your First AI Workflow

If you want your first AI project to succeed, knowing what not to do is just as important as following the right steps. Many finance teams get discouraged not because AI "doesn't work," but because they fall into common traps that derail early progress.

Here are the top mistakes to avoid when validating your first AI use case:

1. Starting with a Complex Task

Choosing a task that involves multiple systems, heavy data transformation, or multiple approvals is a fast way to get stuck. In your first attempt, stick with simple, self-contained tasks that you can test independently without involving IT or other departments.

2. Expecting a Fully Polished Output

AI tools will not deliver a final, presentation-ready result immediately. Think of the AI output as a strong first draft. It will likely need human review and minor adjustments. Expecting perfection on the first try leads to unnecessary disappointment.

3. Skipping Clear Success Criteria

Many early tests fail not because the AI did a poor job, but because no one defined what success actually looks like. Before starting, set a clear goal such as "reduce manual steps by 50%" or "generate a usable draft agenda in 5 minutes." Without a measurable outcome, it is hard to know if the experiment worked.

4. Testing Without Real-World Inputs

AI works best when fed realistic inputs, not idealized examples. If you want to automate a report, use the messy real-world files you actually work with, not a perfectly formatted sample. Otherwise, you risk building workflows that break under real conditions.

5. Trying to Scale Too Quickly

After a small success, it is tempting to immediately look for ways to automate entire processes. Resist that urge. Focus on repeating small wins first. Build experience and confidence before moving into larger-scale implementations.

6. Failing to Document What Worked

Even a simple workflow test can generate valuable insights. Keep a record of what worked, what needed adjusting, and which prompts or methods gave the best results. These notes will be crucial when you start scaling up AI use across other workflows.

Treat your first AI experiments as learning experiences, not finished projects.

Choose wisely, stay focused, and use early successes to build momentum.

Closing Thoughts

Validating your first AI use case does not have to be overwhelming or complicated. A smart, focused 30-minute effort can create the momentum you need to start using AI meaningfully in your finance workflows.

Remember: early experiments are about learning, not perfection. By choosing manageable tasks, setting clear success criteria, and learning from each attempt, you build the foundation for larger, more strategic AI integration in the future.

If you found today’s guide helpful, share it with a colleague who might also be ready to take their first real step into AI.

And if you want to take it even further, stay tuned — next week, we will dive into how to turn your first win into a repeatable AI adoption framework for your finance team.

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