Day 17: AI + Google Sheets dashboards – automated insights & reporting

🔗 Knowledge graph – Day 17 visualizes every system

Day 1

Prompts for insight generation

Day 2

Zapier → Sheets data

Day 3

Make → Sheets data

Day 4

OpenAI for narrative insights

Day 5

Lead qualifier data

Day 6

Business metrics

Day 7

3 builds data sources

Day 8

Advanced qualifier data

Day 9

Sales assistant metrics

Day 10

Content performance

Day 11

Support ticket stats

Day 12

Niche-specific KPIs

Day 13

Workflow metrics

Day 14

CRM data

Day 15

API usage dashboards

Day 16

Scraped data visualization

Day 17

AI dashboards

Shared link: Every system you've built generates data. Day 17 teaches you to visualize that data, add AI-powered insights, and deliver professional dashboards that clients love. This is how you turn automation work into ongoing reporting retainers.

🎯 Why AI + Google Sheets dashboards?

📌 From data to decisions

Your automations generate tons of data: leads, sales, support tickets, content performance. But raw data isn't valuable – insights are. AI-powered dashboards:

Automatically update with new data (no manual work)
Use AI to write narrative summaries ("This week leads increased by 20%...")
Spot trends humans might miss
Give clients a reason to pay monthly retainers

Analogy: Your automations are like a factory producing data. Dashboards are the control room – showing what's working, what's broken, and where to focus. AI is the analyst who explains it all in plain English.

🏗️ Dashboard architecture – 4 layers

Layer 1: Data sources

Sheets from Days 2-16: leads, sales, tickets, scraped data

Layer 2: Formulas & metrics

Google Sheets formulas: QUERY, FILTER, pivot tables

Layer 3: Visualizations

Charts, sparklines, conditional formatting

Layer 4: AI insights

OpenAI-generated summaries and recommendations

Layer 5: Delivery

Automated emails, Slack reports, shared dashboards

📊 Google Sheets dashboard fundamentals (from Day 2)

You already used Sheets in Day 2. Now level up with advanced functions.

QUERY function

=QUERY(Data!A:Z, "select A, B where C > 10")

SQL-like queries on your data

FILTER function

=FILTER(Data!A:Z, Data!C:C > 10)

Dynamic filtering

SPARKLINE

=SPARKLINE(A2:A30, {"charttype","line"})

Mini charts in cells

IMPORTRANGE

=IMPORTRANGE("url", "Sheet!A:Z")

Combine data from multiple sheets

📈 Build #1: Sales dashboard (from Day 8-9 data)

KPIs section

// Total leads this month =COUNTIF(Leads!A:A, ">= "&DATE(YEAR(TODAY()), MONTH(TODAY()), 1)) // Conversion rate =COUNTIF(Deals!E:E, "Won") / COUNTIF(Leads!A:A, "<>") // Average deal value =AVERAGEIF(Deals!E:E, "Won", Deals!D:D)

Lead source breakdown

=QUERY(Leads!A:D, "select D, count(A) group by D label count(A) ''")

Creates a pie chart of lead sources

Hot leads this week

=FILTER(Leads!A:G, Leads!F:F >= 80, Leads!A:A >= TODAY()-7)

Follow-up performance

=AVERAGE(Deals!G:G) // Average days to close

🎫 Build #2: Support dashboard (from Day 11 data)

Ticket volume

// Tickets by day (last 30 days) =QUERY(Tickets!A:E, "select A, count(A) where A >= date '"&TEXT(TODAY()-30, "yyyy-mm-dd")&"' group by A label count(A) ''")

Auto-solve rate

=COUNTIF(Tickets!D:D, "auto") / COUNTA(Tickets!D:D)

Priority breakdown

=QUERY(Tickets!A:E, "select C, count(C) group by C label count(C) ''")

Response time

=AVERAGE(Tickets!F:F) // Hours to first response

📝 Build #3: Content dashboard (from Day 10 data)

Content performance

=QUERY(Content!A:F, "select B, sum(E) where A > date '"&TEXT(TODAY()-30, "yyyy-mm-dd")&"' group by B order by sum(E) desc")

Social shares

=SPARKLINE(Content!F:F, {"charttype","bar"})

🤖 AI insights – adding intelligence to dashboards

Use Day 4 skills to generate narrative summaries from your data.

Prepare data summary for AI

In a separate sheet, aggregate key metrics into a text block:

This week: 25 new leads, 5 deals won ($50,000), 12 support tickets.
Last week: 20 new leads, 3 deals won ($30,000), 15 support tickets.
                        

Send to OpenAI (Make.com or Zapier)

Prompt: "You are a business analyst. Write a 3-sentence summary of this weekly data, highlighting trends and suggesting action items. Data: {{data}}"
                        

Display insight on dashboard

Paste the AI response into a cell at the top of your dashboard. Use =IMPORTRANGE or webhook to update automatically.

Pro tip: Include emojis and formatting in your AI prompt to make insights client-ready: "📈 Leads up 25%... ⚠️ Support response time slowing..."

🔄 Automated updates with Make.com (Day 3)

Schedule daily refresh

Make.com scenario runs daily at 8 AM:

Pull latest data from all sources (CRM, support, etc.)
Append to Sheets raw data tables
Dashboards auto-update via formulas

Generate AI insight

After data refresh, call OpenAI to generate new summary, update dashboard cell.

Email report to client

Send PDF or link to dashboard with key insights (Day 9 email skills).

🔄 Apply dashboards to every previous day

Day 5/8 – Lead qualifier

Dashboard: leads by score, conversion by source, BANT breakdown.

Day 9 – Sales assistant

Dashboard: open rates, click rates, replies by sequence.

Day 10 – Content engine

Dashboard: content performance, top topics, engagement.

Day 11 – Support router

Dashboard: ticket volume, auto-solve rate, satisfaction.

Day 14 – CRM

Dashboard: pipeline stages, deal velocity, revenue forecast.

Day 16 – Web scraping

Dashboard: competitor prices, trends over time.

8 hands-on practice exercises

📊 Exercise 1: KPI dashboard

Create a sheet with 5 KPIs from your Day 14 CRM data. Use QUERY and FILTER.

📈 Exercise 2: Lead source chart

From Day 8 data, create a pie chart of lead sources. Use QUERY to aggregate.

🤖 Exercise 3: AI insight generator

Build a Make scenario that takes weekly stats, calls OpenAI, and posts insight to Sheet.

🎫 Exercise 4: Support dashboard

From Day 11 data, create ticket volume chart and auto-solve rate.

📝 Exercise 5: Content dashboard

Track performance of 10 blog posts (fake data). Create sparklines for views.

🔄 Exercise 6: Automated refresh

Set up a daily Make scenario that appends new data and updates dashboard.

📧 Exercise 7: Email report

Combine Day 9 skills: send automated weekly report with AI insights to yourself.

🏢 Exercise 8: Client dashboard

Create a complete dashboard for a real estate or agency client using niche data.

📄 Client proposal – Automated reporting dashboard

📊 Automated Reporting Dashboard – Service Overview

What I'll build:

✅ Real-time dashboard in Google Sheets
✅ Key metrics: leads, conversion, revenue, support tickets
✅ AI-generated weekly insights ("This week leads increased by...")
✅ Automated daily data refresh
✅ Email report every Monday morning

Data sources: Your CRM, support system, Google Analytics

Investment: $1,500 setup + $250/mo (includes AI costs)

ROI: Save 5 hours/week on manual reporting, spot trends faster

📚 Resources

Day 17: You're now an AI dashboard expert

✔ Built 3 complete dashboards (sales, support, content)
✔ Mastered Sheets formulas for reporting
✔ Added AI-generated insights to dashboards
✔ Automated daily updates and email delivery
✔ Applied dashboards to all previous days
✔ Client-ready reporting service proposal
✔ 8 hands-on practice exercises