Why AI ROI is Hard to Measure
Unlike traditional software investments, AI projects often have indirect and compounding benefits that are difficult to quantify upfront. But that doesn't mean we should give up on measurement -- it means we need a better framework.
The AI ROI Framework
We use a three-tier approach to measuring AI value:
Tier 1: Direct Cost Savings
These are the easiest to measure:
- Time saved: Hours saved x hourly cost
- Error reduction: Errors avoided x cost per error
- Automation: Tasks automated x cost per task
Tier 2: Revenue Impact
More complex but still measurable:
- Conversion improvements: Lift in conversion x average deal value
- Customer retention: Reduction in churn x customer lifetime value
- New capabilities: Revenue from new products/services enabled by AI
Tier 3: Strategic Value
Harder to quantify but often most important:
- Competitive advantage: Market share implications
- Speed to market: Faster decision-making
- Innovation capacity: Ability to experiment and learn
Tips for Presenting to Executives
- Lead with the problem: Start with the business challenge, not the technology
- Use ranges: Provide conservative, expected, and optimistic scenarios
- Include timeline: Show when value will be realized
- Address risks: Be upfront about what could go wrong
Need help building your AI business case? Let's talk.