AI Project Estimation Model
What will this AI project realistically cost — and how confident are you in that number?
Produce P50, P80, and P90 confidence-ranged estimates for AI projects — with non-linearity multipliers, LLM token cost modelling, and CAPEX/OPEX split. Not a flat contingency buffer.
Delivered as an Excel workbook. Download immediately after purchase.
■ Instant download · ■ No macros · ■ Excel 2016+
Flat contingency buffers are not estimates. They are apologies written in advance.
Most AI project estimates use a single-point figure plus a 20% buffer — which tells a PMO nothing about confidence, risk distribution, or the real cost of AI-specific complexity. When scope shifts, the whole number collapses.
The scoring framework.
Eleven tabs covering every dimension of AI project cost — from PERT-based ranging to non-linearity multipliers and LLM token cost modelling.
- Estimation Model (P50/P80/P90)
- A method for projecting cost or effort as a confidence range rather than a single number — P50 is the median case, P80 and P90 the ranges executives use for contingency planning.
Every tab, explained.
Open the workbook and know exactly where to go and what to enter.
Built for the people who have to defend the number.
Stop presenting single-point estimates that collapse under scrutiny. Produce confidence-ranged figures that hold up when scope shifts.
Aggregate P50/P80/P90 estimates across multiple AI workstreams for accurate portfolio-level cost planning and contingency modelling.
Walk into investment approval meetings with an estimate that includes a confidence range, a CAPEX/OPEX split, and an LLM cost projection — not a guess with a buffer.
Feeds from: the prioritization framework produces the Wave 1 shortlist this model estimates.
View Prioritization Framework →Feeds into: once you have a confidence-ranged cost, the next decision is whether Finance will approve it.
View ROI Model →Questions buyers ask before estimating a project.
What is a P50/P80/P90 estimate?
A method for projecting cost or effort as a confidence range rather than a single number. P50 is the most likely case (50% confidence), P80 and P90 are the more conservative ranges executives use for contingency planning and board sign-off.
Why not use a flat contingency buffer instead?
A single number plus a flat buffer is false precision. It tells a PMO nothing about which assumptions drive the range or how confident the estimate actually is. A confidence-ranged estimate makes the uncertainty visible and defensible instead of hiding it inside one number.
How does this model handle LLM and token costs?
A dedicated tab models monthly and annual inference cost by model choice and usage volume, so token-based inference — a cost line most standard estimation tools ignore entirely — is included in the estimate, not bolted on afterward.
AI Project Estimation Model
Produce P50, P80, and P90 confidence-ranged estimates for AI projects — with non-linearity multipliers, LLM token cost modelling, and CAPEX/OPEX split. Not a flat contingency buffer.