# Scoring automations without hiding the math

Canonical: https://stevenmoriasi.com/blog/automation-coe-toolkit
By [Steven Moriasi](https://www.linkedin.com/in/steven-moriasi/).
Source: https://github.com/steven-moriasi/automation-coe-toolkit
Date: 2025-11-14

## Context

Someone arrives with a process, a hoped-for saving, and a request to automate it. The damage starts when scoring lives in a spreadsheet nobody can replay: the assumptions that funded the work disappear.

The [Automation CoE Toolkit](https://github.com/steven-moriasi/automation-coe-toolkit) exists so those assumptions stay inspectable.

## Decision

A bounded JSON intake goes through schema validation, then value, feasibility, risk, and priority scores. Annual benefit, risk-adjusted cost, payback, and a three-year ROI are calculated from the numbers you supplied. Evidence, security, privacy, and continuity gates can still hold a candidate when the score looks attractive. The output is decision support.

## What I rejected

A model that invents a business case from a paragraph of process description, and an autonomous “fund this” action. Both hide the arithmetic and move money or identity without a person on the record.

## Failure scenario

A high-ROI access-review proposal is missing attestation. The toolkit still produces a plan: retain, revoke, or send to manual review. Hash-chained audit records and a visible `side_effects_executed: false` flag keep privilege changes with a person.

## Evidence

Python CLI, Pydantic intake, explainable scores, and the access-review example that refuses to execute side effects.

Inspect: [Repository](https://github.com/steven-moriasi/automation-coe-toolkit) · [Run locally](https://github.com/steven-moriasi/automation-coe-toolkit#readme)

_—by [Steven Moriasi](https://www.linkedin.com/in/steven-moriasi/)_