AI Month-End Close ROI
By Paige Gilmore, Founder, NetLift · Published 2026-07-28 · Updated 2026-08-07
AI for month-end close generates ROI by reducing the time required for recurring tasks, often paying for itself within days when measured against loaded staff costs.
AI adoption in the month-end close typically yields a net value of several thousand dollars per month by cutting task time by more than half. For a standard workload of 100 tasks, the payback on software costs can be achieved in less than a business week.
Financial leaders use these metrics to move beyond hype and quantify exactly how much margin is recovered through automation. By tracking the delta between manual and AI-assisted workflows, firms can determine if a specific tool deserves to be expanded or stopped.
Workflow ROI worked example
Worked example for AI Month-End Close ROI using stated NetLift assumptions. The table below is illustrative — to run this calculation with your own numbers, use the free AI ROI calculator:
| Input (stated assumption) | Value |
|---|---|
| Tasks per month | 100 |
| Time without AI (per task) | 60 min |
| Time with AI (per task) | 25 min |
| Loaded staff cost | $75/hour |
| AI cost per month (licences + usage) | $500 |
| Computed result | Value |
|---|---|
| Hours saved per month | 58 h |
| Labour value of time saved | $4,375 / month |
| Current net value | $3,875 / month |
| Payback | about 3 days |
Every input above is an assumption until you track real work. In NetLift the same calculation runs on verified time blocks, so the result carries an Evidence Quality grade instead of being an estimate.
How is the net value of AI calculated?
The net value is the realized labor savings minus the total cost of the AI, including licenses and usage fees. By applying a loaded hourly rate to the hours saved—such as the 58 hours recovered in our worked example—finance teams can see the direct impact on the bottom line. This deterministic model ensures that value is only claimed when time is actually removed from the workflow.
When should we expand our AI spend?
Decisions are driven by the evidence quality of the savings. NetLift assigns one of five states to every measured area: Expand, Continue, Review, Improve, or Stop. If a workflow shows a high net value with a "Verified" evidence grade, it is a candidate for expansion. If costs outweigh the labor value of time saved, the workflow is flagged for review.
NetLift provides a clear view of AI performance by comparing tracked work against objective baselines. Unlike simple estimates, our methodology accounts for the full cost of ownership—including training and review time—to produce an Evidence Quality grade. This ensures that finance leaders are making renewal decisions based on verified time blocks rather than anecdotal feedback.
Frequently asked questions
Four months in, 64 users and 900+ tasks completed, still no clear return. What is the honest read?
Volume does not guarantee ROI. NetLift measures the current net value by subtracting the full AI cost from the labor value of time saved. If 900+ tasks haven't recovered the software and staff implementation costs, the status would likely be 'Review' or 'Stop' rather than 'Expand'.
How do you calculate the actual time saved per task?
Time saved is the duration the work would take without AI minus the actual time taken with AI. In our close-process example, reducing a 60-minute task to 25 minutes results in 35 minutes of saved time per task.
Does this require monitoring staff screens or keystrokes?
No. The methodology measures work and value, not individual productivity. It is not employee surveillance and does not use screenshots, keystroke logging, or browser monitoring.
What is the difference between realized and future value?
Realized value (current net value) is the labor savings already achieved minus costs. Future value is an estimate based on expected recurring savings and expected volume, and it is always stated separately from verified results.
About the author
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption. Paige Gilmore on LinkedIn