Executive Summary
- AI projects fail when they automate rare tasks instead of frequent, low-value ones.
- The calculation formula: (Hourly Rate * Volume * 0.8) - (Amortized Build Cost + Token Cost) = Net ROI.
- Do not calculate AI ROI based on fired employees; calculate it based on delayed future hiring (Capacity Expansion).
Average Token Cost per Transaction
$0.02Falling Fast
While engineering CapEx is high, the OpEx of running an LLM pipeline is virtually zero compared to human labor.
1. Time Auditing the Organization
Before green-lighting a build, require department heads to log the bottom 20% of their team's tasks. If a task is performed fewer than 50 times a month, it is rarely worth the engineering overhead to automate.
Cost Breakdown: Year 1 Automation Deployment
Engineering Build (One-Time CapEx)75
Software Seats & Data Storage15
LLM Token/Inference Cost10
Capacity Expansion vs Cost Reduction
The most profitable companies use AI to handle 5x more clients without hiring. This 'capacity expansion' avoids the cultural toxicity of firing staff while massively expanding profit margins.
2. Pricing the Build
Building an internal AI tool internally diverts your engineering team from your core product. This is why outsourcing the initial implementation sprint to a specialized agency often yields a faster break-even point.
The Break-Even Horizon
A properly scoped automation pipeline should hit financial break-even in 3 to 4 months. If the projected break-even is over 12 months, the project scope is likely too broad.