How to Use Amplitude AI Dashboard Agents to Get Better Insights (Step-by-Step Guide)

Amplitude's AI Dashboard Agent promises instant insights, summaries, and recommendations from your analytics dashboards. But most teams set it up wrong and get generic, unhelpful responses. The problem isn't the AI. It's that teams dump raw metrics into AI without giving it the context it needs to generate useful analysis.

Here's how to set up Amplitude's AI Dashboard Agent properly so it actually helps you make better product decisions.

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What AI Dashboard Agents Actually Do (And Don't Do)

AI Dashboard Agents work as a context and reasoning layer, not a replacement for dashboards.

They're good at:

• Surfacing patterns across multiple metrics
• Generating hypotheses about changes
• Suggesting follow-up questions based on data gaps
• Synthesizing findings into readable summaries

They're bad at:

• Fixing poorly designed dashboards
• Understanding business context without explicit instructions
• Replacing human judgment on what matters

The winning setup always looks like this: Human framing → Clear dashboards → AI-assisted synthesis

Let's walk through how to implement this in Amplitude.

Step 1: Build the Right Dashboard First (Before Touching AI)

Before you enable AI, your dashboard needs a clear job. If the dashboard doesn't already answer a specific question, the AI agent won't either.

Two Approaches That Work:

Option 1: Team-Specific Dashboards

• Product onboarding conversion (for product team)
• Activation metrics (for lifecycle team)
• Feature adoption (for PM team)

Option 2: KPI-Specific Dashboards

• Acquisition performance
• Conversion funnels
• Retention health
• Monetization metrics

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Step 2: Add Context to Your AI Dashboard Agent

Once your dashboard is ready, navigate to the AI Dashboard Agent feature in Amplitude.

Amplitude gives you two options:

1. Auto-fill context using AI
2. Manually define context yourself

Always choose manual context for important dashboards. Auto-generated context is generic and doesn't understand your business.

What Good Context Includes:

1. Business goal of the dashboard

• Example: "This dashboard tracks onboarding conversion to help product team identify drop-off points and prioritize improvements."

2. Who this dashboard is for

• Example: "Product managers and growth team focused on new user activation."

3. What decisions it should support

• Example: "Should we simplify step 3 of onboarding? Which user segment needs the most help?"

4. What NOT to focus on

• Example: "Ignore internal users and test accounts. Focus only on paid plan users in their first 30 days."

5. Key definitions

• Example: "Activation = user completed profile setup + invited at least 1 teammate + created first project."

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Step 3: Let the Agent Analyze (And Ask Better Questions)

Once context is set, Amplitude's AI generates a high-level summary of your dashboard.

The agent will:

• Surface interesting patterns
• Suggest questions based on data gaps
• Let you ask follow-up questions

Example Questions to Ask:

Pattern detection:

• "What changed most week-over-week?"
• "Which segment is underperforming compared to last month?"
• "Are there any unusual spikes or drops?"

Hypothesis generation:

• "What's likely impacting conversion this week?"
• "Why did retention improve for paid users but not free users?"
• "What could explain the drop in activation for mobile users?"

Prioritization:

• "Which metric should we focus on improving first?"
• "What's the biggest opportunity based on this data?"
• "Where are we losing the most users in this funnel?"

What Good AI Responses Look Like:

Bad (generic):

"Conversion is down 5% this week. This could be due to various factors."

Good (specific and actionable):

"Conversion dropped 5% week-over-week, driven primarily by mobile web users (-12%). Desktop conversion remained stable. This coincides with the mobile redesign launched on Monday. Consider checking mobile funnel completion rates by step."

The difference: Good responses tie data to context, suggest specific areas to investigate, and connect changes to potential causes.

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Step 4: Share Insights and Set Up Monitoring

If the AI analysis reveals something useful, don't keep it to yourself.

You can:

• Share AI insights with teammates directly in Amplitude
• Review findings together in planning meetings
• Set up alerts and notifications for key metrics

Why this matters: When something important changes, you're notified without manually checking dashboards daily.

How to Set Up Monitoring:

1. Identify metrics that drive decisions (e.g., activation rate, conversion by segment)
2. Set thresholds for what counts as significant change (e.g., >10% week-over-week drop)
3. Route alerts to Slack or email
4. Include AI-generated context in alerts so you know why it matters

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Step 5: Always Validate AI Outputs

Critical reminder: AI is not always correct.

It can:

• Misinterpret context
• Miss edge cases
• Over-generalize from limited data
• Suggest correlations that aren't causal

Validation looks like cross checking AI findings against raw data, verifying suggested patterns across multiple time windows. Test your hypotheses before accepting them, and make sure to own the judgement, do not leave it to the AI.

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Step 6: Turn Insights Into Decisions

Insights only matter if they lead to action.

Use AI-assisted analysis to:

1. Identify experiment ideas

• "Activation is low for users who skip onboarding tutorial. Test: Auto-play tutorial on first login."

2. Prioritize product changes

• "Mobile conversion is 15% below desktop. Focus mobile optimization before adding new features."

3. Challenge assumptions

• "We assumed pricing was the churn driver, but data shows users churn after reliability incidents, not price increases."

4. Double down on what's working

• "Users who adopt Feature X within 48 hours have 3x higher retention. Prioritize Feature X in onboarding."

AI doesn't make decisions for you. It helps you make better ones, faster.

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Common Mistakes to Avoid

Mistake 1: Skipping Dashboard Design

Teams enable AI on messy dashboards and expect magic. AI will just give you confident-sounding summaries of chaos.

Fix: Build clear, focused dashboards first.

Mistake 2: Using Auto-Generated Context

Auto-generated context is generic and doesn't understand your business priorities.

Fix: Write manual context that includes business goals, definitions, and what to ignore.

Mistake 3: Accepting AI Output Without Validation

AI can misinterpret data or suggest correlations that aren't real.

Fix: Always verify AI findings against raw data before taking action.

Mistake 4: Not Updating Context Over Time

Your business goals change. If your AI context is six months old, responses will feel off.

Fix: Review and update AI context quarterly.

Mistake 5: Asking Vague Questions

"What's interesting here?" gets generic responses.

Fix: Ask specific questions: "Why did mobile conversion drop 12% this week?"

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The Bottom Line

AI Dashboard Agents aren't magic. They're force multipliers.

If you give them:

• Messy dashboards
• No business context
• Vague questions

You'll get confident-sounding noise.

But if you combine clear human framing, intentional dashboard and reasoning, you will get faster clarity, better questions, and stronger decisions.

‍Want help implementing AI-powered analytics workflows? At Adasight, we help product and analytics teams set up Amplitude properly, from dashboard design to AI context configuration to team training. Contact us get more value from your analytics investment.

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Check Out Our Video Deep Dive Here

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