AI Workforce ROI: The Honest Math

Why Most AI ROI Calculations Are Wrong

The average AI software vendor claims a 3–10× ROI. They're using the same math: take the largest plausible time savings, multiply by the highest plausible hourly rate, and divide by the software cost. It always looks good. It's almost never what happens in practice.

The two most common errors:

• 1.100% automation assumption. No AI Worker automates 100% of a workflow. Exceptions, edge cases, and escalations require human attention. A realistic automation rate is 60–80% of task volume for well-defined workflows.
• 2.Ceiling rate vs. effective rate. Vendors use the employee's annual salary divided by 2,080 hours as the "saved" rate. Employees' freed time doesn't disappear — it gets reallocated. The real value is the next-best use of that time.

The CC ROI calculator uses a 70% automation rate and your actual hourly rate — not a best-case ceiling. This produces a conservative estimate you can defend to a CFO.

The 4 Inputs That Actually Matter

Strip away the noise and AI ROI comes down to four numbers:

The 70% Automation Rate — Where It Comes From

CC uses 70% as the base automation rate for well-scoped workflows. Here's the breakdown:

• —McKinsey (2023): 60–70% of time spent on "highly automatable" tasks for knowledge workers
• —OpenAI / MIT (2023): GPT-4-class models can complete 50% of exposed tasks at human parity or better
• —CC internal: Across 40+ deployments, the 60–75% range appears consistently for tier-1 sales, follow-up, and reporting workflows

For highly structured workflows (form processing, data entry, templated reporting), rates reach 80–90%. For judgment-heavy workflows (complex sales negotiation, creative briefing), rates drop to 40–55%. The 70% figure is the mean across a typical first-deployment workflow mix.

Running the Math

At this input set, Maya Starter pays back in under 4 months and generates $64K net in year 1. The calculator at /infinity-agent-roi runs this math live with your inputs.

Common ROI Math Traps

• ✗Counting tasks instead of outcomes. Automating 1,000 email replies isn't valuable if those replies weren't leading anywhere. Always measure the downstream metric (meetings booked, tickets closed, reports delivered), not the task count.
• ✗Ignoring implementation cost. A cheap software license with a 6-month integration project isn't cheap. CC pricing includes deployment, integration, and maintenance — no hidden professional-services fees.
• ✗Treating freed time as pure savings. If a freed employee shifts to sales calls, that's incremental revenue — measure it. If they don't have productive reallocation, the savings are partially theoretical.
• ✗One-year horizon only. AI Workers improve over time. Year 2 ROI is almost always higher than year 1 because the escalation queue shrinks and the worker handles a broader edge-case set.

Year 2 Compounding

The ROI calculator shows year-1 numbers because that's the break-even horizon most finance teams need. But the real case for AI Workers is in years 2 and 3.

In year 1, workers are learning your specific edge cases, integration quirks are ironed out, and team adoption is ramping. In year 2, the automation rate typically rises from 70% toward 80%, escalation volume drops by 30–40%, and the same plan cost covers a larger workflow surface as you add additional tasks.

A $64K net ROI in year 1 often becomes $85–100K net in year 2 on the same plan cost — because the investment is fixed but the coverage expands.

Run Your Numbers

The CC ROI calculator uses the methodology above — 70% automation rate, your actual hourly rate, real CC plan pricing. Two minutes, no email required to see the number.