Framework · Evidence published

Applied AI opportunity assessment

A simple shared frame for forecasting and automation ideas: score value, data readiness, complexity and operational risk — then decide Pilot, Assess or Hold. Synthetic examples only; no client AI systems.

AI opportunity scoring board with value readiness complexity risk and Pilot Assess Hold decisions — Assessment board — the visual for this topic is the decision frame, not a report dashboard.

Business context

Teams generate more AI ideas than they can safely deliver. Without a shared assessment language, effort drifts toward novelty instead of decisions that improve finance or operations.

The problem

Ideas arrive without a consistent way to judge business value, data readiness and operational risk — so weak cases consume the same attention as strong ones.

My role

Design a lightweight assessment frame and apply it to synthetic finance/ERP-adjacent ideas so the method can be shown publicly.

Approach

Score each idea 1–5 on value, data readiness, complexity and operational risk.
Compute a simple priority score that rewards value and readiness, penalises complexity and risk.
Map to Pilot / Assess / Hold decisions for stakeholder conversations.
Keep governance explicit: automation only where the frame justifies it.

Controls

Shared criteria before tooling choices
Hold is a valid outcome (not a failure)
Examples are synthetic — not deployed client models

Result

Published scoring board and visuals for six synthetic use cases.

Scatter of business value versus operational risk — Value vs risk

Decision mix across assessed AI ideas — Decision mix

Source table:

What I am learning

How to keep applied AI conversations tied to reporting and process decisions — so “can we automate?” comes after “should we, with this data and risk?”

Public-disclosure note

Use cases and scores are invented for portfolio evidence. This page does not describe employer AI systems, models or outcomes.