The Problem
Most AI pilots are launched based on correlation — "We saw revenue increase after deploying AI, so the AI must have caused it." But correlation isn't causation. Without causal thinking, you waste $2M+ on pilots that never scale.
The $2M Case Study
A Fortune 500 financial services company was about to invest $2M in scaling an AI recommendation engine. The pilot showed 15% revenue increase, and the correlation seemed clear: AI deployment → revenue increase.
But when we applied causal inference frameworks, we discovered:
- 12% of the revenue increase came from a concurrent marketing campaign
- 2% came from seasonal trends
- Only 1% was actually attributable to the AI
Result: The company saved $2M by not scaling a pilot that would have failed ROI. They redirected the investment to a use case with proven causal impact.
How Causal Thinking Works
Causal inference frameworks separate correlation from causation by:
- Identifying confounders — other factors that could explain the outcome (marketing campaigns, seasonality, external events)
- Establishing counterfactuals — what would have happened without the AI intervention
- Measuring causal effect — the true impact of AI, isolated from other factors
- Validating with experiments — A/B tests, randomized controlled trials, or natural experiments
The Framework: Before You Scale, Prove Causation
Before scaling any AI pilot, ask:
- What other factors could explain the observed outcome?
- What would have happened without the AI intervention? (counterfactual)
- Can we isolate the AI's causal effect from other factors?
- Have we validated with experiments or natural experiments?
If you can't answer these questions, don't scale the pilot. You're likely confusing correlation with causation.
Real-World Impact
Companies using causal thinking frameworks:
- Save $2M+ per year by avoiding wasted pilots
- Increase ROI by 3-5x by focusing on use cases with proven causal impact
- Get board approval faster by proving value with causal evidence
How to Apply Causal Thinking
Use our free tools to:
- Calculate ROI with causal frameworks — before scaling pilots
- Learn causal inference methods — evidence-based frameworks
- Prioritize use cases — based on causal impact, not correlation