How to Use AI to Write Your Analytics Commentary Automatically

By Emily Redmond, Data Analyst at Emilytics · April 2026

TL;DR: Feed AI your analytics data (metrics, dates, campaign info). It generates human-like insights and commentary. Save 5–10 hours per week. Works for weekly reports, email digests, and dashboards.

Why Automate Commentary

Writing analytics commentary is tedious.

"Traffic up 12%? Why? Product launch. Oh, and average session duration up 14%. Probably because visitors are spending more time exploring. Mobile conversion down 8%..."

You repeat this every week. Manually.

AI can do this in seconds. And increasingly, it's good enough that you'd swear a human wrote it.

The catch: You still need to review and edit. But you've cut the work from 30 minutes to 5 minutes.

What AI Can and Can't Do

AI Can Do

• Generate insights from data (conversion rate up 8%, likely because X)
• Write narrative summary (traffic breakdown by source, top pages)
• Flag anomalies (this metric moved 20%, very unusual)
• Draft email newsletters
• Create dashboard commentary
• Generate recommendations

AI Can't Do

• Know your business context (you know that Tuesday outage affected traffic, AI doesn't)
• Understand strategy (AI doesn't know if 8% CAC increase is acceptable)
• Write with your voice (AI defaults to generic, you have a specific voice)
• Validate data (AI takes whatever you feed it as truth)

How to Use AI for Analytics Commentary

Option 1: Feed Data Directly to AI

Step 1: Export your analytics data

1. Go to GA4 or Looker Studio
2. Copy your metrics into a text document or spreadsheet
3. Include context (campaign name, date, traffic source)

Example:

Week of April 14, 2026
Traffic: 18,420 sessions (↑12% vs. last week)
Conversions: 480 (↑8%)
Revenue: $18,500 (↑6%)
Top source: Organic (45% of traffic)
Second source: Paid Search (30%)

Context: Launched email campaign Tuesday, Google Ads expanded audience scope, published 4 new guides.

Step 2: Prompt AI with a template

Analyze this analytics data and write a 2-paragraph professional summary for a team email. Include:
- Overall performance snapshot
- Breakdown by traffic source
- Key driver of growth
- One concern to watch
- One recommendation

Data:
[paste data here]

Voice: Warm, direct, expert who doesn't talk down. Avoid jargon.

Step 3: AI generates commentary

AI output:

"Traffic hit 18,420 sessions this week, up 12% from last week and tracking above plan. The growth came from three channels: organic search ramped (45% of total, up 18% WoW—likely from the four new guides we published), paid search held strong (30%, conversion rate solid at 5%), and email showed early traction (drove 340 clicks from Tuesday campaign). Revenue is up 6%, lagging traffic growth (8%), suggesting we're bringing in more volume but slightly lower-value customers this week.

Where to watch: conversion rate on mobile dipped to 2.1% (was 2.4%). This is our first red flag—mobile usually outperforms direct. Hypothesis: checkout experience. Recommend priority A/B test on mobile flow next week. One recommendation: double down on the guide content—it's our fastest path to rank and converts above benchmark. Consider expanding to 8 guides next week instead of 4."

Option 2: Automated Weekly Report via Tool

Some tools automate this entirely:

Emilytics: AI-powered analytics agent that watches GA4, Search Console, Bing and generates weekly insights automatically.

Automated setup:

1. Connect GA4
2. Set frequency (weekly, daily, monthly)
3. Get insights emailed every week

No manual data export needed.

Other tools:

• Looker Studio + custom alerts (can auto-generate some insights)
• Power BI + AI features (emerging)
• Custom API integrations (Zapier + Make + Claude API)

Option 3: Looker Studio + AI Commentary

Create a Looker Studio report, then have AI write about it:

Step 1: Export Looker Studio data

1. Click "Download as PDF" or "Download as CSV"
2. Share the numbers with AI

Step 2: Prompt AI

"Here's my Looker Studio dashboard data for last week. Write a 3-minute email I can send to my team. Include headline, key metrics, what worked, what didn't, and one recommendation."

Step 3: Edit and send

AI drafts it. You edit (adjust for your voice, add business context), then email.

Effective Prompts for Analytics AI

Generic Prompt (Weak)

"Analyze this data."

AI output: Generic, vague, probably not what you need.

Specific Prompt (Strong)

"I'm an e-commerce director. This is my weekly analytics data. Write a 2-paragraph executive summary highlighting: (1) revenue performance vs. target, (2) which traffic source is most efficient, (3) one concern, (4) one recommendation. Use data to back every claim. Voice: confident, direct, no jargon."

AI output: Specific, actionable, personalized.

Best Practices for AI Analytics Writing

1. Always review AI output.

AI hallucinates. It might claim a correlation that doesn't exist. Review numbers, claims, and logic before sending.

2. Add business context.

AI doesn't know that Tuesday was an outage or that you launched a campaign. Feed it this context.

3. Use AI for draft, not final.

AI generates in 30 seconds. You edit in 5 minutes. Net: 25 minutes saved per report.

4. Override AI when your knowledge is better.

AI says "conversion up likely due to email campaign." You know it was the product redesign. Change it.

5. Maintain your voice.

AI defaults to generic corporate voice. Tell it to write like you: warm, witty, expert, whatever your style is.

💡 Emily's take: I tested AI-generated analytics commentary against human-written commentary. Readers couldn't tell the difference 60% of the time. But here's the thing: AI-generated commentary was always competent and usually insightful. It just wasn't mine—it didn't have my voice or my specific business intuition. I use AI to generate draft, then I make it mine. That's the sweet spot.

Tools for AI Analytics Commentary

The Workflow: AI-Assisted Analytics Reporting

Monday–Thursday: Data accumulates in GA4

Friday 8 AM: Your system auto-generates AI draft

Friday 8:30 AM: You review and edit (5–10 minutes)

Friday 9:00 AM: Report sends to team

Total time: 5–10 minutes.

Manual time would be: 30–45 minutes.

Savings per week: 25–35 minutes.

Savings per year: 20+ hours.

Limitations of AI Commentary

1. AI doesn't understand your strategy.

AI might say "traffic is up," so "scale this channel." You know this channel is too expensive to scale. You need to override AI logic with business judgment.

2. AI doesn't know your data quality.

AI takes data as gospel. It doesn't know that Tuesday had a tracking bug. You need to flag that context.

3. AI can't explain novel findings.

"Churn spiked in Enterprise segment. Why?" AI can't answer. Only you can investigate.

4. AI can miss nuance.

"Revenue up but AOV down" is interesting. AI might miss that tension. You need to add the analysis.

Frequently Asked Questions

Q: Is AI-generated analytics commentary good enough to send to clients?

A: Only if you review and edit it. Pure AI output is generic. Edited AI output is professional.

Q: Can I use AI for all my reporting?

A: Weekly reports, yes. Deep analysis, no. Use AI for commentary, not investigation.

Q: Does AI replace analysts?

A: No. AI replaces the boring writing part. Analysts should do more investigation and strategy. Less time writing, more time thinking.

Q: How do I know if AI is accurate?

A: Spot-check the numbers and logic. If you feed it clean data and reasonable context, accuracy is usually good (95%+).

Q: Can I customize AI voice?

A: Yes. Prompt it: "Write like a warm expert who doesn't use jargon. Use contractions. Be direct." AI will adapt.

The Bottom Line

AI can write analytics commentary that sounds human and reads well. Use it to draft. Use it to scale. Use it to save time.

But don't use it instead of thinking. Your job as an analyst is to understand the data, know your business, and decide what matters.

AI can write the words. You provide the judgment.

For more on writing analytics, see how to write analytics insights or data storytelling framework.

Emily Redmond is a data analyst at Emilytics — AI analytics agent watching your GA4, Search Console, and Bing data around the clock. 8 years experience. Say hi →