Before building anything, I need to know the numbers work. Here's the framework I use to evaluate every AI automation opportunity.
The Three-Variable Test
Every AI automation opportunity comes down to three variables:
- Current cost — What does this process cost today? (Time, money, errors, opportunity cost)
- Automation potential — What percentage of this process can AI reliably handle?
- Implementation cost — What does it cost to build and maintain the system?
If (Current Cost × Automation Potential) > (Implementation Cost × 3), it's worth building. The 3x multiplier accounts for unexpected complexity, maintenance, and iteration.
Measuring Current Cost
Most companies underestimate their current costs because they only count direct labor hours. The real cost includes:
- Direct labor: Hours spent × fully loaded cost per hour
- Error cost: Mistakes × average cost per error
- Speed cost: Revenue lost to slow processing
- Opportunity cost: What else could your team be doing?
I've seen companies discover their "simple data entry process" actually costs $180,000/year when you include error correction and the senior engineer who spends 5 hours a week fixing integration issues.
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Automation Potential
Not everything should be automated. The sweet spot is processes that are:
- Repetitive — Same pattern, many instances
- Rule-based — Clear decision criteria (even if complex)
- Data-rich — Inputs and outputs are structured or can be structured
- Error-tolerant — A 5% error rate is acceptable (with human review)
If a process requires deep domain expertise, involves high-stakes decisions with no room for error, or changes constantly — AI automation isn't the right tool. Yet.
Implementation Cost
Be honest about this one. Include:
- Development time (architect + build + test)
- API costs (LLM calls, integrations)
- Ongoing maintenance (monitoring, updates, edge cases)
- Training and change management
A good rule of thumb: whatever your initial estimate is, multiply by 1.5 for the first version and add 20% annually for maintenance.
The Decision
Run the numbers. If the ROI is clear, build it. If it's marginal, don't. The worst AI projects are the ones that are "kind of" worth it — they get built, sort of work, and never get the investment needed to actually deliver results.
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