AI ROI Calculator
Estimate how much time and money your team could save with structured Claude AI deployment. Adjust the sliders to match your situation.
How AI ROI actually compounds
AI savings look unimpressive at the single-task level. Forty-five minutes saved on a quote. Twenty minutes on a report. A few hours a week on onboarding emails. These are the numbers people quote to dismiss AI deployment as not worth the investment — and they’re wrong, because they’re measuring the wrong unit.
What actually matters is the same task, performed by the same team, compounded over a year. A quoting workflow that runs twenty times a week, saving forty-five minutes each time, is the equivalent of thirty-five full working days per year — for one workflow, in one department. Stack that across five or six deployed projects, and you’re measuring in FTE equivalents rather than hours.
The calculator below handles the math, but the inputs matter more than the arithmetic. Be honest about how many employees will actually use AI daily (usually less than you think), how structured those tasks are (more structured = higher reduction), and what your fully loaded cost per hour really is (usually 1.3-1.5x base pay after benefits and overhead).
Estimated Savings
Weekly hours saved
125
Monthly hours saved
541
Annual cost savings
$487,500
Equivalent FTEs freed
3.1
Weekly hours on repeatable tasks
| Metric | Before AI | After AI |
|---|---|---|
| Weekly task hours | 250 | 125 |
| Annual task hours | 13,000 | 6,500 |
| Annual labor cost | $975,000 | $487,500 |
Based on results from Settle's first engagement: Orient Printing saw 85% faster document generation, reducing task time from 4 hours to 30 minutes.
These are estimates. Want to see what's realistic for your team?
A discovery call maps your actual workflows and identifies which savings are achievable in your first 90 days.
Book a Discovery CallReading your estimate
The output is a floor, not a ceiling — assuming the deployment quality is strong. Orient Printing’s actual numbers beat our early ROI estimates because structured Claude AI projects, with properly engineered instructions and knowledge files, outperform generic prompt-based AI usage by a large margin. The difference between 40% and 85% time reduction almost always comes down to instruction quality and knowledge curation, not the AI model.
The two inputs that produce the most variance in your estimate are (1) the percentage of your team that will actually adopt the AI daily and (2) the AI time reduction rate. Adoption is an organizational problem, not a technical one, and it’s the single biggest reason ROI estimates miss. Reduction rate is a workflow property — templated, repeatable, rule-based work lands toward the top of the range; analytical or relational work lands toward the bottom.
Treat the annual number as a planning input, not a promise. If the estimate justifies a deployment, the discovery session does the second-order work of validating which workflows are actually in each bucket.