Baseline
Measure current volume, elapsed time, manual touches, error or exception rate and tool cost.
ROI
AI agents can return multiples of their cost.
Model productivity, avoided AI spend and programme cost. High-volume agent workflows can reach 500–1,500% ROI when the same capability runs repeatedly at scale.
Working model
Change the assumptions. See what must be true.
The defaults are illustrative. Replace them with your function’s actual baseline.
Illustrative agent cases:
Illustrative annual case
Capacity released$1,814,400
Realised productivity value$997,920
Total annual value$1,197,920
Net annual value$997,920
ROI499%
Payback2.0 months
Illustrative model using 48 working weeks. Returned capacity is not cash savings unless the organisation converts it into lower cost, more output or avoided hiring.How to validate it
Do not scale a forecast. Scale a measured result.
Supervised launch
Run the workflow with human review and record adoption, quality, model usage and exceptions.
Expansion decision
Scale only when the agreed KPI improves and the control model remains acceptable.
Cost control
Route work by quality, policy and cost.
Use efficient models for routine work, reserve premium models for complex reasoning, set budgets by team or workflow and find duplicated external subscriptions.
What to include
The full cost of the current process.
Employee time, delays, errors, AI seats, token usage, integration maintenance, infrastructure and enablement all belong in the baseline.
Start with one valuable function
Build the ROI case for one real workflow.
We will define the volume, current cost, human controls and KPI required for an expansion decision.