ROI Calculation Framework
Calculate the true ROI of AI investments, including hidden costs and timeline to value.
The ROI Calculation Problem
AI ROI is widely overstated in vendor pitches. "Save 40 hours/week" becomes "2 FTE saved = $150K/year" — but this ignores implementation cost, ongoing operations, maintenance, and the productivity loss during transition.
A rigorous ROI framework protects you from bad investment decisions.
Direct Benefits (Easier to Quantify)
Time savings — hours saved × fully-loaded cost per hour. Be conservative: implementation periods, edge cases, and training reduce realized savings by 30-50%.
Error reduction — cost of errors (rework, refunds, penalties) × error rate reduction. Requires baseline data on current error rates.
Revenue increase — new capabilities that directly drive revenue (faster response time, personalization, new product features).
Indirect Benefits (Harder to Quantify)
Employee satisfaction — automating tedious tasks improves morale and retention. Model as reduction in turnover cost (typically 50-200% of annual salary per departure).
Speed advantages — faster execution enables faster decisions and faster customer response. Hard to quantify but real.
Scalability — AI systems scale at near-zero marginal cost. Value this as the cost savings when volume increases without proportional headcount increase.
True Costs
Implementation — engineering time, vendor fees, project management. Often 3-5x initial estimates.
Ongoing operations — API costs, infrastructure, monitoring, maintenance. Often underestimated.
Training and adoption — getting people to use the system correctly. Often 20-30% of implementation cost.
Opportunity cost — what else could this team have built?
The Payback Period
Conservative ROI calculation: benefits at 50% of projections, costs at 150% of estimates. If payback period < 18 months at those numbers, the investment is likely sound.