To measure AI automation ROI, compare the cost of completing the same work before and after the change. Include the time people spend reviewing, correcting, and handling exceptions, plus software, support, and setup costs. Then separate the value of freed staff time from money the business actually stops spending.
That distinction can change the investment decision. A workflow might free useful capacity without reducing payroll. It might also generate drafts faster while creating enough checking work to erase the gain.
Start with one workflow, one measurement period, and a definition of a completed job. Here is a practical framework for deciding whether a pilot earns a larger investment.
Establish what the whole job costs today
Choose a repeatable task, such as preparing a quote, assembling a weekly report, or sorting incoming requests. If you have not chosen one yet, use the checklist in how to choose your company’s first AI workflow.
Measure the current process across a representative set of cases. Include routine work and exceptions. Record how much time people actively spend, how long customers wait, how often work needs correction, and what counts as a successful result.
Keep active time and elapsed time separate. A task might need 15 minutes of staff effort while taking two days to move through an approval queue. Reducing either can matter, but they are different benefits.
Use the same boundaries for the pilot. If the baseline ends with an approved quote, compare it with an approved quote after automation. A generated draft is an intermediate step.
Count the work that remains after automation
For every completed case, include preparation, human review, corrections, and any manual fallback. Add recurring administration and support time separately. Failed attempts still consume time, even when a dashboard counts only successful runs.
Automation reporting often starts with a configured baseline. For example, Microsoft’s Power Automate savings documentation describes rules that assign time or money savings to successful flow runs. Such a report depends on the baseline you supply. Check it against the work your team actually does.
Compare equivalent workloads and record changes in volume, complexity, or staffing. A quiet week with simple requests cannot establish what will happen during the busiest month. Treat early results as a limited sample and revisit them as more cases arrive.
Work through a monthly example
The following numbers are hypothetical. They illustrate the calculation, not Codi client results, market rates, or an expected return.
Suppose a team handles 200 requests per month. Each currently takes 15 minutes of hands-on work. During a pilot, the average falls to six minutes, including review, corrections, and manual exceptions across all 200 requests.
| Measure | Current process | Pilot workflow |
|---|---|---|
| Completed requests per month | 200 | 200 |
| Average staff minutes per request | 15 | 6 |
| Staff hours for those requests | 50 | 20 |
| Additional monthly workflow administration | None in this example | 4 hours |
| Total staff hours counted | 50 | 24 |
The workflow releases 26 hours per month: 50 minus 24. At an illustrative internal cost of $40 per staff hour, that is $1,040 in monthly capacity value. If software and external support cost another $400 per month, the net monthly capacity value is $640.
That calculation does not mean $640 appears in the bank account. If the same salaried team works the same paid hours, payroll may be unchanged. The benefit depends on what the team can do with the released time.
Use the First AI Project Kit to compare your own candidates. The playbook and editable worksheet help you record the workflow, assumptions, and potential value before commissioning a build.
Calculate ROI and payback using a stated basis
For an illustrative first-year capacity analysis, assume the workflow costs $6,000 to set up and the monthly workload stays constant for 12 months. Assume no ramp-up delay, and keep the $400 recurring cost separate from the staff administration time already counted.
- Annual capacity benefit: 26 hours × $40 × 12 = $12,480.
- Annual investment: $6,000 setup + ($400 × 12) = $10,800.
- Net first-year capacity value: $12,480 minus $10,800 = $1,680.
- First-year capacity ROI: $1,680 ÷ $10,800 × 100 = about 15.6%.
Here, ROI means net benefit divided by total investment over the same period. State your definition and assumptions whenever you share the result. Count a cost once: the four administration hours are already deducted from the 30 hours released by case processing.
Simple payback on the $6,000 setup would be $6,000 ÷ $640, or about 9.4 months, on this capacity-value basis. Actual implementation delays, lower volume, or extra support would extend it. This is an illustrative decision model, not a forecast.
For cash ROI, replace the capacity benefit with verified avoided spending or incremental contribution after associated costs. Examples could include reduced contractor invoices or avoided paid overtime. If this hypothetical team avoids no spending and creates no verified incremental contribution, it has no cash benefit to offset the $10,800 first-year investment. Its first-year cash ROI on this model is negative 100%, even though it gains capacity.
Do not count both the wage value of released time and the full sales value of work done in that time. Choose a defensible valuation and avoid double-counting.
Find the point where the project stops making sense
A useful business case shows how the conclusion changes when the assumptions move.
In the example, each request saves nine minutes before the fixed four monthly administration hours. At $40 per hour, that is $6 of capacity value per request. Monthly administration represents $160, and software/support adds $400.
Covering those ongoing costs therefore needs $560 ÷ $6, or about 94 requests per month. Covering ongoing costs plus the $6,000 setup within 12 months needs ($560 + $500) ÷ $6, or about 177 requests per month. Both thresholds assume the same handling time, quality, and costs, with no new capacity limits.
These are capacity-value thresholds. They do not establish cash break-even. If your real volume is near the threshold, a more expensive month or extra correction work could change the decision. Test a lower-volume case and a higher-support case before committing.
Keep quality beside the financial calculation
A positive spreadsheet result cannot compensate for an unreliable customer commitment or an approval that was skipped. Track errors, exceptions, and the consequences of failure alongside time and cost.
NIST’s AI RMF Playbook recommends evaluating systems in context, documenting metrics, and defining acceptable performance limits. It is risk-management guidance, not an ROI formula or a certification for a pilot.
Use a short scorecard:
| Question | What to record |
|---|---|
| Did we complete equivalent work? | Volume, case mix, and completion criteria |
| Did staff effort fall? | Preparation, review, corrections, exceptions, and support |
| Did quality hold up? | Error counts, severity, and whether errors reached customers |
| Did the business benefit? | Released capacity, actual avoided spending, and how time was used |
| Should we continue? | Named owner, acceptance criteria, and next review date |
Choose acceptance criteria before examining the results. A small sample with no observed errors gives useful evidence, but it does not prove the workflow will never fail.
Make the next investment conditional on the evidence
Continue when the measured benefit is useful, the costs are sustainable, and quality stays within the limits your team agreed. Narrow or stop the pilot when review work grows, exceptions dominate, or the projected value depends on volume you do not have.
If the numbers are unclear, the next step is a better baseline or a smaller pilot. Talk with Codi about mapping the workflow so the first build has a result you can measure and a decision you can defend.