Aug 31, 2026 · 31 min
Companies still cannot prove their AI investments pay off
Ep 852: Measuring AI ROI: Why you’re doing it wrong and the 7 Steps to fix it (Start Here Series Vol 11)
Without baselines and business metrics, organizations risk mistaking faster work or better outputs for genuine returns on AI spending.
- 1AI ROI requires measurable baselines rather than broad claims about speed, productivity, or improved outputs.
- 2A useful evaluation tracks time saved, cost reduction, quality, throughput, utilization, and broader business impact.
- 3Sustained returns depend on implementation, training, and ongoing model testing—not deployment alone.
Don't miss
Wilson’s central reframing is that AI success must be measured against a baseline across multiple business metrics, not inferred from enthusiasm or speed alone.
The brief
Jordan Wilson opens with a simple challenge: companies cannot call AI successful merely because work feels faster or outputs look better.
The episode argues that traditional digital-transformation playbooks fail when they lack a baseline, making it impossible to distinguish real gains from optimistic impressions.
Its proposed scorecard spans time saved, cost reduction, quality, throughput, utilization, and business impact, turning AI evaluation into a measurable operating process.
The framework also treats implementation and training as part of ROI, while ongoing model testing checks whether performance holds as tools and workflows change.
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