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Why ROI Gets Hard for ML Projects
Machine learning initiatives often fail budgeting conversations because teams measure model accuracy instead of business outcomes. Production ML value comes from reduced cost, faster cycles, and revenue lift — not F1 scores alone.
A Simple ROI Framework
We evaluate ML initiatives across three buckets before build:
- Cost reduction — hours saved, error reduction, automation of manual workflows
- Revenue impact — conversion lift, faster sales cycles, better retention
- Risk reduction — fraud detection, compliance automation, quality gates
What to Track After Launch
Define baseline metrics before deployment, instrument dashboards from day one, and review outcomes monthly with product and finance stakeholders. ML ROI is a operating metric, not a one-time project report.