What does AI enablement actually save you?
Model the 5-year cost gap between your current operating model and an AI-enabled one. Then compare building the capability in-house vs. engaging a specialist. Conservative assumptions throughout — governance-first, not hype.
Describe your current operating model
Enter the numbers for the function or value stream you are considering for AI enablement. Use conservative estimates — the model is more useful when the inputs are honest.
The function or value stream headcount under consideration
Salary + benefits + overhead
Your function's operating cost as a % of the revenue it supports
End-to-end for the priority workflow (e.g. NAV production, claims, KYC)
Breaks, exceptions, rework as a % of total volume
Audit findings, supervisory observations, remediation actions
Remediation cost + fines + management time per finding
Assumptions and methodology
This calculator uses conservative improvement assumptions based on observed outcomes across AI enablement engagements in regulated industries. Year 1 assumes 8% cost reduction (the diagnostic and early workflow redesign); this grows to 33% by year 5 as the data flywheel, operating model changes, and governance machinery compound. These are structural savings, not one-time efficiencies.
The build-in-house model assumes 6 specialist hires at £110,000/year fully loaded, with 20% annual attrition and a 9-month ramp to productivity. The engagement model prices a Diagnostic (£60,000), Strategy & Blueprint (£180,000), and Transformation Programme (£450,000) with an advisory retainer in year 3.
Every projection is directional. Actual outcomes depend on your operating model, data maturity, regulatory environment, political complexity, and the quality of implementation. For a projection calibrated to your specific situation, book a working session.