AI knowledge search can reduce task time from 20 minutes to 5 minutes, yielding a net value of $1,950 per month based on 120 tasks and a $75 hourly staff cost. After accounting for a $300 monthly license fee, the investment typically pays back in approximately four days.
For IT and finance leaders, these figures provide a baseline for evaluating AI adoption. The objective is to move beyond simple estimates toward verified time savings tracked against specific workflows without relying on anecdotal evidence.
Workflow ROI worked example
Worked example for AI Knowledge Search ROI using stated NetLift assumptions (replace every input with your own tracked data):
| Input (stated assumption) |
Value |
| Tasks per month |
120 |
| Time without AI (per task) |
20 min |
| Time with AI (per task) |
5 min |
| Loaded staff cost |
$75/hour |
| AI cost per month (licences + usage) |
$300 |
| Computed result |
Value |
| Hours saved per month |
30 h |
| Labour value of time saved |
$2,250 / month |
| Current net value |
$1,950 / month |
| Payback |
about 4 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 net value calculated for AI search?
Net value is determined by subtracting the full cost of the AI—including licenses, usage, implementation, and training—from the labor value of the realized time saved. In a standard workflow, saving 15 minutes per task across 120 monthly tasks generates 30 hours of recovered capacity. At a loaded staff cost of $75 per hour, this represents a significant reduction in operational waste.
What determines the payback period?
Payback measures how long the net value takes to cover the total AI cost to date. When search tasks are frequent and time savings are high, the initial software investment is often recouped within the first week. This deterministic model ensures that future value, which projects recurring savings, is always stated separately from value already realized.
NetLift measures these outcomes by comparing actual work time against objective baselines rather than relying on self-estimates. Each calculation receives an Evidence Quality grade to distinguish between rough estimates and verified data. This allows teams to categorize AI spend into decision states like Expand or Review without using intrusive surveillance like keystroke logging or browser monitoring.