How To Build Expense Templates in Google Sheets & Excel

The Two Ways To Run Your Monthly Expense Template

For most founders and marketers, monthly expenses live in one of two worlds:

1. A manual spreadsheet you dread opening.
2. A living system that seems to run itself.

Let’s walk through both — first the classic manual approach in Excel and Google Sheets, then how to turn the same template into an automated workflow with an AI agent like Simular.

1. Manual Setup in Google Sheets

Step 1: Create Your Structure

• Open Google Sheets and create a new file.
• Add tabs: Raw_Transactions and Monthly_Summary.
• In Raw_Transactions, create columns: Date, Vendor, Category, Payment Method, Amount, Notes, Month.

Step 2: Import Your Data

• Export your bank or card statement as CSV.
• Go to File → Import → Upload and bring the CSV into Raw_Transactions.
• Ensure dates and amounts are correctly formatted.

Step 3: Add Categories

• Create a data-validated Category column with items like Advertising, Software, Payroll, Contractors, Rent, Misc.
• Manually assign categories or use FILTER and FIND to bulk-update common vendors.

Step 4: Build the Monthly Summary

• In Monthly_Summary, list categories in column A.
• Use formula:

=SUMIFS(Raw_Transactions!$E:$E, Raw_Transactions!$C:$C, A2, Raw_Transactions!$G:$G, "2025-01")


• to total spend per category for a given month.
• Add a total row and a simple bar chart for category spend.

Pros (Manual Sheets)

• Free and cloud-based.
• Easy collaboration and commenting.
• Great for early-stage or small teams.

Cons

• Repetitive monthly imports.
• Error-prone manual categorization.
• Time cost grows as transaction volume increases.

2. Manual Setup in Excel

Step 1: Start From a Template

• Open Excel and search the template gallery for “monthly budget” or “expense tracker”.
• Choose a layout with separate income/expense sections and charts.

Step 2: Adapt to Your Business

• Rename categories to match your business: e.g., Lead Gen, Tools, Team, Ops.
• Add columns for Project/Client to link spend back to revenue.

Step 3: Bring In Transactions

• Import bank CSV files via Data → Get Data → From Text/CSV.
• Map columns to the template structure (Date, Description, Amount, Category).
• Use Flash Fill and rules to classify common vendors.

Step 4: Visualize

• Build a PivotTable by Month and Category.
• Add a PivotChart for quick burn-rate and category breakdown views.

Pros (Manual Excel)

• Powerful formulas, pivots, and charts.
• Works well for large transaction sets.

Cons

• Files can get heavy and version-sprawl quickly.
• Still requires monthly import, cleaning, and categorization.

3. Turning Your Template Into An Automated System With AI Agents

Now the fun part: keep the same Google Sheets or Excel templates, but let an AI computer agent do the drudge work.

With Simular’s computer-use agents, you’re no longer writing brittle scripts or clunky RPA flows. You’re delegating the exact clicks and keystrokes you would normally perform across desktop, browser, and cloud apps.

What the Agent Can Do For You

• Log in to bank or card portals.
• Download the latest CSV statements.
• Open Excel or Google Sheets and the correct template file.
• Paste new data into Raw_Transactions.
• Apply your categorization logic (rules, lookups, or prompts).
• Refresh formulas, PivotTables, and charts.
• Save, back up, or email your monthly report to stakeholders.

Pros (AI-Driven Automation)

• Massive time savings every month once set up.
• Far fewer manual errors or missed transactions.
• Works across Google Sheets and Excel without separate scripts.
• Transparent execution: every action is visible and replayable.

Cons

• Requires initial onboarding to teach the agent your exact workflow.
• Periodic human review is still recommended for edge cases or unusual transactions.

4. A Simple Hybrid Workflow

Most knowledge workers start with a hybrid approach:

1. Month 1–2: Build and refine your template manually in Sheets or Excel.
2. Month 3: Record one full run while the Simular agent “observes” — download files, clicks, and categorization.
3. Month 4+: Let the agent execute the routine end-to-end. You review summary tabs and anomalies it flags.

The result: the same trusted spreadsheet, updated by a tireless AI operator instead of late-night manual work. You stay in control of the model and the money — while reclaiming hours every month.

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