USE CASE
AI Reporting Agent: Replace Tableau, Looker, and Power BI with a Single Agent
Build a reporting AI that pulls data from anywhere, generates beautiful reports on schedule, and costs a fraction of enterprise BI subscriptions.
Your team pays $50-70 per user per month for business intelligence software. For a 20-person analytics team, that's $12,000-$16,800 per year—just to visualize data that already exists.
What if an AI agent could answer your questions directly, surface insights proactively, and generate reports automatically?
The Hidden Cost of Business Intelligence Tools
Let's look at what enterprise BI tools actually cost for a 20-person team:
| Tool | Price/User | Annual (20 users) |
|---|---|---|
 Tableau | $70/user/mo | $16,800 |
 Power BI | $10/user/mo | $2,400 |
 Looker | $50/user/mo | $12,000 |
Mode | $40/user/mo | $9,600 |
Prices based on professional tiers as of December 2025
What if your reporting team was an AI that cost nothing and answered questions in seconds?
What BI Tools Really Are (Demystified)
Strip away the marketing and what are you left with?
| Feature | What It Really Is |
|---|---|
| Interactive Dashboards | SQL queries rendered as charts |
| Data Modeling | Join definitions and column mappings |
| Scheduled Reports | Cron jobs that email PDFs |
| Alerting | Threshold checks on scheduled queries |
| Collaboration | Comments on chart URLs |
You're paying for visualization software. An AI agent can query, analyze, explain, and deliver—no dashboard building required.
What an AI Reporting Agent Actually Does
An AI reporting agent isn't just a chatbot that runs SQL. It's a persistent analyst that actively monitors your business:
Answers questions in natural language
Surfaces insights proactively
Generates reports on schedule
Explains the 'why,' not just the 'what'
Connects data across sources
A Day in the Life: AI Reporting Agent in Action
Here's what it looks like when an AI agent handles your reporting. Real conversations, real insights:
Revenue Overview
via Slack - #executiveHere's your December performance summary:
Enterprise drove most of the growth (+23%). SMB was flat. I've saved a detailed breakdown to /reports/december-summary.md
Here's the revenue breakdown by customer segment:
Anomaly Detection
via Slack - #alertsHeads up — I noticed an unusual pattern in your churn data:
Churn spiked to 5.2% on Friday, up from your normal 2.1% baseline. This started after Thursday's pricing page update.
I analyzed the 25 cancellations. Here are the main reasons:
| Cancellation Reason | Count | MRR Impact |
|---|---|---|
| Price increase concern | 12 | $4,600 |
| Switching to competitor | 8 | $3,100 |
| No longer needed | 5 | $1,900 |
Weekly Sales Report
Weekly Sales Report — December 16-22, 2025
Demo-to-close rate improved to 42% (was 31%)—the new demo script is working.
Pipeline is down slightly, but quality is up—average deal size increased 18%.
Full report saved to /reports/sales-weekly-12-22.md
Ad-hoc Analysis
I analyzed acquisition channels by LTV. Here's what I found:
| Channel | Leads | CAC | Avg LTV |
|---|---|---|---|
| Organic Search | 312 | $45 | $2,300 |
| Paid Ads | 198 | $140 | $1,950 |
| Referral | 156 | $20 | $3,050 |
Recommendation: Double down on Referral. It has the lowest CAC and highest LTV. Paid Ads have 3x the CAC with lower retention.
The Killer Feature: Your Own Data Intelligence Layer
Here's what makes this fundamentally different from any BI tool you've used before.
In Lazarus, your reporting agent maintains its own knowledge of your business. It learns your metrics, understands your data model, and remembers context from past conversations. It's not just running queries—it's building institutional knowledge about your numbers.
No dashboard maintenance
Context-aware analysis
Proactive, not reactive
Natural conversation, not clicks
This isn't a tool that helps you build reports. It's an analyst that generates reports.
Step-by-Step: Build Your AI Reporting Agent
Here's how to build your own AI reporting agent in about 15 minutes:
Step 1: Create the agent and write instructions
Open Lazarus and create a new agent. Name it 'Reporting Agent' or 'Data Analyst.' Then describe what you want:
"You are my reporting analyst. Connect to our data sources and answer questions about business performance. Generate weekly reports every Monday. Alert me to anomalies or significant changes. Always explain the 'why' behind the numbers."
Step 2: Connect your data sources
Connect your database, spreadsheets, or analytics tools. The agent learns your data model and starts building context.
Step 3: Define your key metrics
What do you care about? Revenue, MRR, churn, CAC? Tell the agent your key metrics and how to calculate them.
Step 4: Set up scheduled reports
Configure weekly, monthly, or quarterly reports. The agent generates and delivers them automatically.
Step 5: Start asking questions
The agent is always ready. Ask anything. No dashboard required.
That's it. You've built a reporting team that works 24/7, never misses an anomaly, and costs almost nothing.
Advanced: Build a Multi-Agent Analytics Team
Once you're comfortable with a single reporting agent, you can build a team of specialized agents:
Data Analyst Agent
Report Writer Agent
Anomaly Detector Agent
All agents share the same data context. When one learns something about your business, they all benefit.
Stop building dashboards. Start getting answers.
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