What this automation does
Every Monday morning (or whatever schedule you choose), this automation pulls data from your key business tools — CRM, analytics, support desk, financial software — aggregates the numbers, and sends them to AI. The AI writes a structured report with an executive summary, key metrics with week-over-week comparisons, trend analysis, flagged anomalies, and recommended actions.
Most teams spend 2-4 hours every week pulling numbers from different tools, copying them into a slide deck or document, and writing commentary. This automation produces a more thorough report in minutes, with consistent formatting and never-miss delivery.
Tools you need
- Google Sheets or data warehouse: Central place to aggregate data from your business tools
- OpenAI API: GPT-4 analyzes data and writes the report narrative ($0.10-0.25 per report)
- Make or n8n: Pulls data from APIs, aggregates it, and triggers report generation on schedule
How to set it up
Step 1: Identify your key metrics. List the 10-15 numbers that matter most to your business — revenue, new customers, churn rate, support tickets, NPS score, marketing spend, pipeline value, etc. For each metric, identify which tool holds the data and how to access it via API.
Step 2: Create a Make scenario scheduled for every Monday at 7am. Add API calls to each data source — HubSpot for pipeline data, Stripe for revenue, Google Analytics for traffic, Zendesk for ticket counts. Write all values to a Google Sheet with columns for this week, last week, and percentage change.
Step 3: Send the aggregated data table to OpenAI with a prompt that asks for: a 3-sentence executive summary highlighting the most important trends, a metrics table with week-over-week changes and status indicators (up/down/flat), analysis of any significant changes (anything moving more than 10%), and 2-3 recommended actions based on the data.
Step 4: Format the report as an HTML email or Google Doc. Send it to your leadership team's email list. Include a link to the underlying data sheet for anyone who wants to dig deeper. After the first month, survey recipients on what they find most and least useful, and adjust the report structure.
Cost breakdown
| Item | Cost | Notes |
|---|---|---|
| Make or n8n | $15-$25/mo | Weekly scheduled scenario with multiple API calls |
| OpenAI API | $5-$10/mo | ~$0.20 per report, 4-5 reports/mo |
| Google Sheets | $0 | Free with Google Workspace |
| Setup time | 50-90 min | One-time, plus API connection setup |
| Total monthly | $20-$35/mo | Saves 4+ hours/week of manual reporting |
Frequently asked questions
AI excels at pattern recognition and comparison. It consistently spots trends that humans miss because it processes all metrics simultaneously. The insights are most valuable when you provide historical context — give the AI 4-8 weeks of data and it identifies trends, seasonality, and correlations between metrics. The recommendations are directional, not strategic — they flag what deserves attention.
For tools without APIs, use scheduled exports. Most SaaS tools let you set up automated CSV exports via email. Route those exports to a Google Sheet with Zapier or use Google Apps Script to parse the CSV. For truly manual data (like board updates or partner reports), create a simple form that team members fill out by Friday.