ROI Calculation Framework

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.