Who this assessment is for. Appears on the saved report and travels with the analysis.
What is the AI meant to do, and what kind of work is it?
A short label, e.g. "Invoice processing assistant".
Pick the closest fit. This decides how the model is calibrated — it matters, because AI helps a lot on some tasks and can actually hurt on others.
AI typically delivers strong gains here, especially for newer staff.
This sizes the opportunity. Count only the work the AI will actually touch.
Only those who will actually use the AI for this task.
Just the specific task above — be honest/conservative.
Salary + benefits + overhead — not just base pay. Unsure? Use about 1.35× their base hourly rate.
Default 46 accounts for holidays and time off.
Your best estimates. Include the "hidden" costs — integration and change management are where budgets slip.
Software setup, integration, data prep, training, change management.
Licenses/subscriptions, usage/compute, maintenance, ongoing enablement.
A 3-year view is typical.
The annual return the investment must beat. Unsure? Leave 12%.
These four answers drive the odds of the project actually succeeding — most AI projects fail on these, not on the technology.
The single biggest success factor. "Bolted on" tools usually fail to deliver.
Clean, accessible, well-organized data = high.
Connecting to your existing systems.
Research shows back-office and operational automation delivers more reliable ROI.
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