AI ROI Calculator
Model build vs buy ROI before you commit budget or roadmap
Adjust adoption, implementation cost, payback, and risk assumptions in real time to pressure-test AI investment decisions.
Estimated weekly hours saved: -
Typical 30-70%
Typical 20-60%
Confidence model
Probability-weighted confidence based on 5,000 simulations.
5000 simulations
Deliver & share
Optional email. Results are never gated.
AI ROI Calculator FAQ
What is a good ROI for AI tools?
For most product teams, a strong ROI starts above 30% with payback under 12 months. Use adoption and automation rates to validate that you can achieve this.
How do you calculate AI payback?
Payback is total upfront cost divided by monthly net benefit (time savings minus ongoing tool costs). This calculator uses your inputs to compute it automatically.
How do I estimate adoption risk?
Adjust the adoption rate slider and sensitivity ranges to see how ROI shifts. The Monte Carlo results show the probability of positive ROI.
What inputs matter most for AI ROI?
Adoption rate, time saved per task, and tool cost drive most outcomes. Revenue uplift and margin matter most in the strategic model.
Is this calculator finance-grade?
Yes. It includes operational costs, ramp-up effects, and a strategic finance model with NPV and IRR, plus Monte Carlo risk analysis.
Can I use this for board approval?
Yes. The decision memo and board-ready one-pager are designed to summarize ROI, payback, and risk in executive language.
AI ROI playbook for build vs buy decisions
Product leaders need a model that supports roadmap and budget decisions, not just a surface-level ROI estimate. Use the sections below to pressure-test assumptions before sharing recommendations with leadership.
How AI ROI is calculated
This model is designed for product leaders choosing whether to build or buy AI capabilities. It starts with measurable time savings and adds finance-grade cost and risk layers.
- Quick ROI: values annual time savings against annual tool spend for a fast directional check.
- Operational ROI: adds implementation, training, admin overhead, and ramp-up to reduce optimistic bias.
- Strategic ROI: adds margin-adjusted revenue impact and discounts future cash flows into NPV and IRR.
How to interpret ROI, payback, and confidence
Use all three views together. ROI without payback can hide cash timing risk, and payback without confidence can hide adoption risk.
- ROI: useful for comparing opportunities; positive ROI is necessary but not sufficient.
- Payback: strong plans usually recover costs in under 12 months for production rollouts.
- Confidence model: use probability of positive ROI and percentile bands to pressure-test assumptions.
Build vs buy checklist for product teams
Before committing roadmap and budget, validate the inputs that move outcomes the most.
- Speed to market: estimate delivery timeline differences for build versus buy.
- Integration depth: check API coverage, data access, and workflow fit with your stack.
- Operating burden: include ongoing maintenance, model tuning, governance, and support load.
Common AI ROI modeling mistakes
Most bad estimates come from optimistic adoption and missing operational costs.
- Using peak adoption assumptions in year one instead of ramping adoption over time.
- Ignoring implementation and training costs when comparing build and buy options.
- Assuming unrealistic automation rates without validating process-level constraints.