The ROI of AI: Measuring What Actually Matters

When executives ask "What's the ROI of AI?", they're often met with impressive-sounding but ultimately meaningless metrics. "95% accuracy!" "10x faster processing!" These vanity metrics tell you nothing about actual business impact. Here's how to measure what actually matters.

The Vanity Metrics Trap

Most AI vendors love to tout technical metrics that sound impressive but don't translate to business value:

• Model accuracy: A 99% accurate model that automates 5% of work is worse than an 80% accurate model that automates 60% of work
• Processing speed: Processing emails 10x faster doesn't matter if humans still need to review every response
• Data volume: Training on millions of examples is irrelevant if the model can't handle your specific use cases

The Four Metrics That Actually Matter

1. Automation Rate (Coverage × Accuracy)

The percentage of work that AI can complete end-to-end without human intervention. This is the single most important metric because it directly correlates with cost savings and capacity gains.

Target: 60-80% for most operations use cases. Higher isn't always better if it means sacrificing coverage.

2. Time to Value

How long from kickoff to measurable business impact? Most AI projects fail because they never escape pilot purgatory. Productized solutions should show ROI in weeks, not months.

• Excellent: 2-4 weeks to production
• Good: 4-8 weeks to production
• Warning sign: "We'll start with a 3-month pilot..."

3. Cost Per Automated Task

What does it actually cost to automate each unit of work? This includes software costs, implementation, training, and ongoing maintenance.

Compare this to your current cost per task (typically $5-$25 for knowledge work). If AI costs more, you don't have ROI yet.

4. Capacity Multiplier

How much more work can your team handle with AI? This is often more valuable than pure cost savings because it enables growth without proportional headcount increases.

How to Calculate Your AI ROI

Follow this simple framework:

Red Flags That Signal Poor ROI

• "We need 6 months to train the model" - Productized solutions should work out of the box
• "Let's start with a pilot" - Pilots rarely lead to production. Demand a clear path to scale
• "We'll need a dedicated AI team" - If it requires specialized staff, it's not truly productized
• "Accuracy will improve over time" - You need ROI now, not eventually
• "We can automate 20% of your work" - That's not enough to justify the investment

The Bottom Line

Good AI ROI comes from high automation rates (60-80%), fast time to value (2-4 weeks), and clear cost savings or capacity gains. Everything else is noise.

If a vendor can't clearly articulate how their solution will achieve these metrics for your specific use case, keep looking. The best AI investments pay for themselves in months, not years.