Automation Backlog Prioritization: Impact × Feasibility

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Overview

Backlogs explode with ideas. Prioritization should be transparent and revisitable as data improves.

Core concept

Definition

Impact × feasibility scoring estimates value (time, revenue, risk) against integration difficulty, data quality, and organizational readiness.

Business impact

Why it matters

Politics picks pet projects; scoring aligns teams—or exposes where data work must precede AI.

Practical model

Framework

Impact rubric

Quantify weekly hours, error cost, or revenue latency.

Feasibility rubric

APIs vs screen scraping, data cleanliness, compliance constraints.

Implementation detail

Detailed breakdown

Revisit quarterly

Feasibility changes as platforms improve and debt is paid down.

In practice

Real-world example

A COO killed a flashy “AI insights” project after scoring showed CRM hygiene blocked every dependent workflow—funded cleanup first.

Avoid these

Common mistakes

Impact only—choosing impossible integrations.
Feasibility only—optimizing busywork with low upside.

Engineering layer

Technical patterns

Scoring rubric

Normalize scores 1–5; document assumptions for impact estimates.
Platform items get bonus weight (unblocks many flows).

Build patterns

Code examples

Weighted score

Transparent ranking function.

TypeScript

export function priorityScore({ impact, feasibility, risk, platform }) {
  return 0.4 * impact + 0.3 * feasibility + 0.2 * platform - 0.1 * risk;
}

System view

System architecture

YAML

[Intake form]
        → [Normalize scores]
        → [Quarterly review board]
        → [Committed roadmap + parking lot]
        → [Feedback to requesters]

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