Skip to main content
AITE M1.4-Art72 v1.0 Reviewed 2026-04-06 Open Access
M1.4 AI Technology Foundations for Transformation
AITF · Foundations

Template 2 — Role Exposure and Skills-Adjacency Workbook

Template 2 — Role Exposure and Skills-Adjacency Workbook — Technology Architecture & Infrastructure — Advanced depth — COMPEL Body of Knowledge.

6 min read Article 72 of 48

COMPEL Specialization — AITE-WCT: AI Workforce Transformation Expert Artifact Template 2 of 5


How to use this workbook

The workbook is structured in four sheets (in a spreadsheet format; reproduced here in tabular form for narrative text).

  • Sheet 1 — Role context and task inventory. Populated first; sets the baseline.
  • Sheet 2 — Task classification. Populates exposure, augmentation, and human-centricity scores against the inventory.
  • Sheet 3 — Role-level aggregation. Derived from Sheet 2; produces the role-level view.
  • Sheet 4 — Skills-adjacency mapping and redeployment recommendation. The synthesis sheet.

Complete the workbook per role. For a portfolio analysis, aggregate workbooks at the portfolio level in a separate summary document.


Sheet 1 — Role context and task inventory

Role identification

FieldValue
Role title (current)e.g., Commercial Underwriter
Role code (HRIS)
Function
Approximate incumbent count
Geographic distribution
Tenure profileaverage + distribution
Current AI touchpoints (brief summary)
Date of decompositionYYYY-MM-DD
Decomposition author
Source triangulationincumbent interviews / observation / output analysis — name each

Task inventory

List 15–25 tasks per Article 24 discipline. Include at least three coordination or knowledge-work tasks per the corrective in that article.

Task IDTask nameApproximate time per week (hours)Brief description (1 sentence)Elicitation source
T1
T2
T3

Sheet 2 — Task classification

For each task from Sheet 1, score on the three dimensions.

Task IDTask nameAI exposure (0–3)Augmentation value (L/M/H)Human-centricity (L/M/H)Rationale (1 sentence)
T1
T2
T3

Classification scale reference

AI exposure (0–3):

  • 0 = No current AI capability can do meaningful portions of this task.
  • 1 = AI can do portions with significant human adjustment.
  • 2 = AI can do most of the task with light human review.
  • 3 = AI can do end-to-end with only exception handling.

Augmentation value (L/M/H):

  • Low = Augmentation would produce minimal value (task is low-frequency or low-value).
  • Medium = Moderate value — noticeable but not decisive.
  • High = Substantial value, affects the role’s performance materially.

Human-centricity (L/M/H):

  • Low = Mechanical or procedural; human contribution is limited.
  • Medium = Requires some judgment but not ambiguity-rich.
  • High = Judgment-rich, relational, professional-accountability-dependent.

Bias-check

Confirm for the classification:

  • Classification is against plausible 12–24-month capability, not against the specific tool now licensed.
  • Classification weights value-and-judgment, not only speed-and-volume.
  • Classification is independent of the performance-system’s current measures.
  • Coordination and knowledge-work tasks are included in the inventory with appropriate scores.

Sheet 3 — Role-level aggregation

Derive from Sheet 2.

Exposure profile

Exposure levelFraction of role timeExample tasks at this level
0
1
2
3
Total100%

Augmentation-value distribution

Augmentation valueFraction of role timeValue concentration notes
Low
Medium
High

Human-centricity profile

Human-centricityFraction of role timeCore human-centric tasks
Low
Medium
High

Total time check

MetricValue
Sum of task hours per week
Expected role working hours per week
Gap (hours)
Gap interpretatione.g., hidden coordination work under-counted; unsustainable load; etc.

Redesign implications

2–4 sentences summarising what the exposure + augmentation + human-centricity profile implies for redesign direction. Is this a candidate for redesign (most likely), for retirement (rare), or for retention with tool-integration only?


Sheet 4 — Skills-adjacency mapping and redeployment recommendation

Skills-adjacency map

Using the ESCO European skills taxonomy, Lightcast open subset, or internal skills framework as reference.

Current role skills (top 10)

List the skills that the current role requires, sourced from the task inventory and the existing role description.

Short-development-distance adjacent roles (≤6 months)

Roles where the incumbent’s skills are directly transferable with modest additional development.

Adjacent roleSkills sharedSkills to develop (6-month horizon)Redeployment feasibility

Moderate-development-distance adjacent roles (6–18 months)

Adjacent roleSkills sharedSkills to develop (6–18-month horizon)Redeployment feasibility

Substantial-development-distance adjacent roles (beyond 18 months)

Adjacent roleSkills sharedSkills to develop (>18-month horizon)Redeployment feasibility

Redeployment recommendation

For incumbents whose role is redesigned or retired: which of the adjacent roles are the strongest redeployment candidates, for which cohort, with what development pathway.

RecommendationCohortTarget roleDevelopment pathwayTimeline
1
2
3

Executive summary for coalition

2–3 paragraphs suitable for inclusion in a coalition paper. Covers: the role’s exposure profile; the redesign-versus-retirement-versus-retention recommendation; the redeployment pathways available; the next-step resource commitment required.


Quality rubric — self-assessment of template

DimensionSelf-score (of 10)
Completeness (all four sheets present and operable)10
Discipline (task-level decomposition method enforced)10
Transferability (usable for any knowledge-worker role)10
Fidelity to credential content (Articles 4, 5, 24)10
Bias-check integration9
Weighted total49 / 50