Measuring AI ROI: set the baseline before you build

Many AI projects are declared successful because they feel faster. Few can say by how much, or what that is worth. The difference is almost always the same: nobody measured the starting point before the system was built.

What we measure

In Discovery, alongside the root-cause work, our consultants establish a baseline for the process the system is meant to improve. The baseline is agreed with the client before any build begins.

• Cycle time: how long the process takes from start to finish today
• Effort: how many staff hours it consumes each week, and at what cost
• Error and rework: how often outputs are wrong and must be redone
• Leakage: money lost or delayed, such as disputed invoices or spoiled stock

Turning hours into naira

For each industry arm we maintain cost benchmarks: a typical cost per staff hour and the share of repetitive hours that AI can usually take over. Combined with the measured baseline, they give an early estimate of value that leadership can weigh against the cost of the build. The estimate is then replaced by real measurement once the system is live.

An estimate gets a project approved. A baseline gets it proven.

Proving the return after launch

Once a system is in production, UniversAIMS tracks the same measures against the agreed baseline. The monthly review reports time saved and costs reduced in the terms leadership signed off at the start, which is how our published results are reported: less time spent screening CVs, less time producing reports, less content-creation work.

1. AgreeChoose the measures that matter for this process and agree how they will be counted.
2. MeasureRecord the baseline from real data and interviews before anything changes.
3. EstimateUse sector benchmarks to estimate value and set expectations.
4. ProveReport actual change against the baseline every month after launch.