AI + BI: From Answering Questions to Investigating Problems
Most AI tools answer the question you ask. Scoop investigates—using deep reasoning, conversational exploration, and machine learning that discovers what you didn't know to look for.
Investigation, Not Just Answers
The difference between restating what you know and discovering what you don't.
Answering a Question
Surface Level
You
Why did revenue drop?
AI
Revenue decreased 19% compared to last quarter.
Result: A restatement of what you already know.
Investigating a Problem
Deep Investigation
You
Why did revenue drop?
Scoop
Tests multiple hypotheses, analyzes across segments, identifies patterns, traces root causes.
Scoop
35% decline in 25-34 age segment, primarily in electronics (down 58%), started 3 months ago. High confidence.
Result: Actionable root cause with confidence scoring.
How Scoop Investigates
1
Hypothesis Formation
Automatically generates multiple plausible explanations
2
Systematic Testing
Applies appropriate statistical methods to each hypothesis
3
Evidence Ranking
Quantifies correlation strength, statistical significance, sample sizes
4
Impact Analysis
Isolates and measures the contribution of each factor
5
Confidence Scoring
Transparent certainty levels (High/Medium/Low)
6
Alternative Explanations
Shows what was considered and why paths were rejected
The Main Difference
No hallucinations. Real calculations. Traceable to source. You get insights you can trust and act on.
Advanced Analytics, Zero PhD Required
Scoop includes four enterprise-grade ML capabilities that work automatically—no data science expertise needed.
Predictive Relationships
Which factors predict outcomes?
Discovers what drives success using decision tree models and importance scoring.
Explore Predictors →
Smart Segmentation
What are my natural customer groups?
Automatically finds segments using clustering algorithms with business explanations.
Segment & Cluster Discovery →
Group Comparisons
What's different between these groups?
Statistical comparison with effect sizes and significance testing.
Explain & Analyze a Group →
Time Period Analysis
What changed and why?
Quantifies impact of each change, separates signal from noise.
Compare Time Periods →
Why Investigation Requires Both AI and BI
Every AI capability traces back to the BI foundation. You can't bolt AI onto weak BI and expect real investigation.
What AI Needs from BI
Clean Data → Intelligent ingestion that handles messy real-world data
Historical Context → Snapshot datasets that track how entities change over time
Consistent Calculations → KPIs with explicit rules—no hallucinations, same answer every time
Enterprise Scale → Infrastructure to test multiple hypotheses across millions of rows
Reliable Output → Professional presentations and CRM write-back to close the loop
Next Steps
BI Foundation
Learn how clean data, historical tracking, and reliable calculations power AI investigation
Learn More →