Scoring Methodology

How we calculate automation potential and transformation waves

Scoring Framework

Our 7-dimension model for assessing role automation potential

Each role is scored across 7 dimensions on a 0-5 scale. Three dimensions are positive factors (higher = more automatable) and four are negative factors (higher = less automatable). The weighted combination produces a composite automation score from 0-100%.

Positive Factors

Repeatability, Data Availability, Tool Maturity

Higher scores increase automation potential

Negative Factors

Human Judgment, Stakeholder Interaction, Compliance Risk, Accountability

Higher scores decrease automation potential

Dimension Weights

How each dimension contributes to the overall score

Repeatability
+
20%

How routine and repeatable are the tasks? Higher = more automatable.

Data Availability
+
15%

Is structured data available to train AI models? Higher = more automatable.

Tool Maturity
+
20%

How mature are AI tools for this type of work? Higher = more automatable.

Human Judgment
-
15%

Does the work require nuanced human judgment? Higher = less automatable.

Stakeholder Interaction
-
10%

How much client/stakeholder interaction is required? Higher = less automatable.

Compliance Risk
-
10%

What regulatory or compliance risks exist? Higher = less automatable.

Accountability
-
10%

How much accountability does this role carry? Higher = less automatable.

Future Score Projections (12-18 Months)

Expected technology improvements and their impact on automation potential

Future scores account for anticipated AI advancements. Tool maturity is expected to improve most significantly (+40%), while the importance of human judgment in certain tasks may decrease as AI becomes more capable.

DimensionMultiplierImpact
Repeatability1x
Stable
Data Availability1.2x
+20%
Tool Maturity1.4x
+40%
Human Judgment0.85x
-15%
Stakeholder Interaction0.9x
-10%
Compliance Risk0.95x
-5%
Accountability1x
Stable

Transformation Waves

How roles are classified into transformation waves based on their scores

Wave 1: Automate
65-100%
Months 1-6

Highly automatable roles. Implement AI-first solutions immediately.

Wave 2: Augment
40-64%
Months 7-12

Moderate automation potential. Deploy AI tools to augment human capabilities.

Retained
0-39%
Ongoing

Human-critical roles. Maintain with selective AI assistance.

Data Sources & Validation

Where our data comes from and its validation status

Role Definitions & Tasks

AOTF Task Summary with Salaries

Verified

Automation Scoring Framework

Industry research + AI capability assessment

Verified

Future Technology Multipliers

Based on AI development trajectories

Estimate

Financial Assumptions

21% staff loading, 13% business overhead

Assumption

Score Calculation Formula

// Positive factors (higher = more automatable)

positiveScore = (repeatability × 0.20) + (dataAvailability × 0.15) + (toolMaturity × 0.20)

// Negative factors (inverted: 6 - score)

negativeScore = ((6 - humanJudgment) × 0.15) + ((6 - stakeholder) × 0.10) + ((6 - compliance) × 0.10) + ((6 - accountability) × 0.10)

// Composite score (normalized to 0-100)

compositeScore = ((positiveScore + negativeScore - 1) / 4) × 100