Verizon Job Profile Enrichment Dashboard Mockups

① Skill Health Overview — Stoplight Heatmap

Displays the proficiency gap between required skill levels (per Job Profile Enrichment data) and actual employee proficiency (AI-inferred + self-rated) for every critical skill across selected roles. Color-coded stoplight reveals where talent is strong, stretched, or at risk.

👤 HRBP · Workforce Planning · SVP Staff Meetings

BU: Consumer & Mass Markets

Hierarchy: SVP Chen, Linda (B3)

⚡ AI Automation Signal ON

847

Employees in scope

↑ 100% profile complete

24

Critical skills mapped

Across 12 job roles

11

Skills at strength ●

>50% at/above required

6

Critical gaps ●

≥2 prof. levels below req.

Skill	Job Role	Req. Prof.	Avg. Actual	% at/above	Gap	AI Risk	Status
Network Infrastructure Design	Sr. Network Engineer	L4	L4	72%	None	▸ Low	Strength
Cloud Architecture (AWS)	Sr. Network Engineer	L4	L3	45%	–1 level	▸ Medium	Developing
5G Protocol Engineering	Wireless Eng. Specialist	L5	L3	22%	–2 levels	▸ Low	Critical Gap
Data Analytics & Visualization	Workforce Planner	L3	L2	38%	–1 level	▸ High	Developing
Prompt Engineering / Gen AI	Multiple Roles	L3	L1	9%	–2 levels	▸ Medium	Critical Gap
Project Management (PMP)	Program Manager	L4	L5	81%	Exceeds	▸ Medium	Strength

ⓘ Reading this view: Green = >50% of employees at/above required level. Yellow = avg. 1 level below required. Red = avg. 2+ levels below required. AI Risk flag indicates skills likely to be augmented or automated within 18–36 months per WEF/O*NET signals. Proficiency uses Censia's 1–5 scale: L1 Awareness → L5 Expert.

Strength (≥50% at/above req.)

Developing (–1 level gap)

Critical Gap (–2+ levels)

AI-At-Risk Skill

② Hierarchy Drill-Down — Org Skill Distribution

Navigate from the SVP level down to job families and individual roles. For each node in the hierarchy, see the aggregate skill strength distribution (green/yellow/red breakdown) and top gap areas. HRBPs can surface answers for a business leader's entire org in a single view.

👤 HRBP · Supports SVP/B3 business reviews

SVP Level (B3)

Director (B4)

Manager (B5)

LC

Chen, Linda

SVP Consumer & Mass Markets · B3

847 EEs

RK

Kumar, Raj

Director — Network Operations · B4

312 EEs

Strong

SE

Sr. Network Engineers

Job Family · 89 employees

1 Gap

WE

Wireless Eng. Specialists

Job Family · 223 employees

Critical

MP

Patel, Michelle

Director — Workforce Planning · B4

185 EEs

Moderate

JL

Lee, James

Director — Product & Strategy · B4

350 EEs

Moderate

Skill Health Breakdown — SVP Chen's Org (all 847 EEs) 24 Critical Skills

Skill status distribution across org

46%

29%

25%

Strength

Developing

Critical Gap

Top 3 Critical Gaps in this org

5G Protocol Engineering

–2 levels · 22% coverage

Prompt Engineering / Gen AI

–2 levels · 9% coverage

Cloud Architecture (AWS)

–1 level · 45% coverage

Where high-proficiency talent exists org-wide

5G Protocol (L4+)37 employees · TX, NJ, VA

Cloud Arch. (L4+)104 employees · NJ, NY, TX

* All counts are aggregate. No individual name data surfaced per privacy rules.

③ Org-Wide Talent Finder — Where Does This Skill Exist?

Select any skill and proficiency level to see aggregate counts of employees who hold it across the entire organization — filtered by BU, location, job family, and demographic dimensions. Surfaces internal supply to support Build/Borrow decisions. All data is aggregate-only; no individual names are shown.

👤 HRBP · Workforce Planning · Talent Redeployment

5G Protocol Engineering

Min. Proficiency: L3+

234

Total employees with skill L3+

12

Business units represented

37

At Expert Level (L4–L5)

18

States / Locations

Distribution by BU & Proficiency Level

L3 (Practitioner)

L4 (Advanced)

L5 (Expert)

Consumer & Mass Mkt

68

29

8

Business Markets

34

19

14

Global Enterprise

22

11

9

Network & Technology

88

22

6

Color intensity = employee count. All values aggregate only.

Top 6 Locations — Employees with Skill L3+

Basking Ridge, NJ

72

Irving, TX

54

New York, NY

41

Alpharetta, GA

30

Denver, CO

22

Other / Remote

15

④ Build / Buy / Borrow / Engage — Decision Signal

For each identified skill gap, the dashboard applies rules-based logic to recommend a workforce action: Build (train internally), Buy (hire externally), Borrow (redeploy from within), or Engage (contract/contingent). Recommendations are driven by internal supply, proficiency gap severity, and AI automation risk.

👤 CHRO · VP Workforce Planning · Talent Strategy

SVP Chen Org

Critical Gaps Only ✓

🏗️ BUILD

Data Analytics & Visualization

Internal supply exists at L1–L2. 45 employees show adjacent skills. Recommend targeted L&D program to bridge to L3.

45 candidate EEs6–12 mo.L&D

💼 BUY

5G Protocol Engineering

Internal supply is insufficient at required L4–L5. External market has moderate talent supply. Critical business need — recommend targeted hiring.

22% internal coverageHire L4+Urgent

🔄 BORROW

Cloud Architecture (AWS)

104 employees with L4+ Cloud Arch. skill identified org-wide in other BUs. Internal mobility / project rotation recommended before external hire.

104 EEs org-wideInternal MobilityNJ / TX

🤝 ENGAGE

Prompt Engineering / Gen AI

Rapidly evolving skill; AI automation signal is medium. Recommend contingent/contract specialists while internal L&D program is built in parallel.

Contract firstBuild L&D in parallel

ⓘ How recommendations are generated: Build = internal supply ≥40% at L1–L2 with adjacent skills. Buy = internal supply <30% at required level + no adjacent skill pathway. Borrow = org-wide supply ≥L3 in other BUs. Engage = high AI automation risk OR rapidly evolving skill taxonomy.

⑤ Taxonomy Gap Audit — Inferred vs. Existing

Side-by-side comparison of Censia AI-inferred job tasks and skills against Verizon's current job taxonomy. Net-new inferred items (not yet in taxonomy) are highlighted in green. Changed/evolved items in yellow. Removed/obsolete in red. Enables the Job Architecture team to identify where the taxonomy needs updating and where AI automation is reshaping roles.

🗂️ Job Architecture · Talent Management · Comp & Classification

Sr. Network Engineer

⚡ AI Signal ON

+12 New 7 Changed 3 Removed

Job Tasks — Inferred vs. Taxonomy 22 total tasks

AI-Inferred Task

In Current Taxonomy?

✚

Design and optimize cloud-native 5G core network architectures

NEW ▸ AI Low

✚

Evaluate and implement MLOps pipelines for network anomaly detection

NEW ▸ AI Med

↻

Monitor and maintain network performance metrics (evolved → now includes AI-assisted monitoring tools)

CHANGED ▸ AI High

✓

Configure and maintain IP routing protocols (BGP, OSPF)

EXISTS ▸ AI Low

✕

Manage physical hardware provisioning in on-premise data centers

OBSOLETE ▸ AI High

✓

Develop and enforce network security policies

EXISTS ▸ AI Low

✚

Collaborate with AI/ML teams on predictive capacity planning models

NEW ▸ AI Med

Skills — Inferred vs. Taxonomy 18 total skills

AI-Inferred Skill

In Taxonomy?

✚

Kubernetes / Container Orchestration

NEW ▸ AI Low

✚

LLM Prompt Engineering

NEW ▸ AI Med

✓

TCP/IP Networking

EXISTS ▸ AI Low

↻

Network Monitoring (→ AI-Augmented Ops)

EVOLVED ▸ AI High

✕

SONET/SDH Transport

OBSOLETE ▸ AI High

✚

Terraform / Infrastructure-as-Code

NEW ▸ AI Low

✓

Cisco IOS / Network Device Config

EXISTS ▸ AI Med

⑥ AI Automation Risk Map — Skills & Tasks by Exposure Level

Scatter plot mapping every job skill and task by two axes: AI automation exposure (x-axis) and current workforce proficiency gap (y-axis). The upper-right quadrant (high exposure + high gap) flags where Verizon faces the greatest compounding risk — roles where the skill is critically underdeveloped AND likely to be automated.

🗂️ Job Architecture · Workforce Planning · L&D Strategy

All Critical Skills

AI Automation Exposure vs. Proficiency Gap

Each bubble = one critical skill. Bubble size = number of employees in gap. Quadrants indicate priority action.

MONITOR — Low Gap, High AI Risk

⚠ PRIORITY ACTION — High Gap + High AI Risk

High
Gap Low
Gap

5G

AI

DA

☁

ND

PM

API

Sec

Low AI Risk Medium High AI Risk

STRENGTH ZONE — Low Gap, Low AI Risk

INVEST CAREFULLY — High Gap, Low AI Risk

Priority Action Items — Upper Right Quadrant

5G Protocol Engineering ▸ High AI Risk

Gap: –2 levels · 22% coverage · 660 EEs in gap

Automation risk is low for deep engineering tasks, but adjacent tooling (monitoring, provisioning) faces high automation. Recommendation: Buy L4+ engineers now; build tooling adaptability program.

Prompt Engineering / Gen AI ▸ Med AI Risk

Gap: –2 levels · 9% coverage · 769 EEs in gap

Skill itself is partially self-automating as AI tools improve. Recommendation: Engage contractors short-term; build L&D while monitoring skill evolution.

Data Analytics & Visualization ▸ High AI Risk

Gap: –1 level · 38% coverage · 524 EEs in gap

Routine analytics heavily automatable. Focus L&D on interpretation, storytelling, and decision-making layers. Recommendation: Build — but shift curriculum to AI-augmented analytics.

⑦ Job Analysis Lite — AI-Inferred Task & Competency Profile

Replaces the manual job analysis process by surfacing AI-inferred critical tasks, required competencies, and proficiency thresholds for any role in the taxonomy. I/O Psychology and Talent Assessment teams can validate this output in minutes rather than weeks, then use it directly to inform hiring tool or L&D tool design decisions.

📋 I/O Psychology · Talent Assessment · L&D Design

Wireless Engineering Specialist — Grade 24

Job Profile Enrichment ✓

18

Inferred Critical Skills

vs. 9 in current taxonomy

22

Job Tasks Inferred

12 new vs. taxonomy

3

Critical skill gaps org-wide

Skill absent at req. level

Critical Tasks — By Importance Weight

Task Description	Importance	AI Risk	Status
Design 5G NR radio access network architectures	{{"★★★★★".split('').map(() => '★').join('')}} ★★★★★	▸ Low	In taxonomy
Optimize network slicing for enterprise 5G use cases	★★★★★	▸ Low	NEW — add to taxonomy
Interpret AI-generated network performance anomalies	★★★★★	▸ Medium	NEW — add to taxonomy
Generate automated NOC monitoring reports	★★★★★	▸ High	EVOLVING
Configure physical rack hardware in regional data centers	★★★★★	▸ High	REMOVE — obsolete

Critical Competencies — Required vs. Workforce Supply

5G Protocol Engineering Req: L4 · Org avg: L2.5

Req L4

0%Current: 50%100%

Cloud Architecture (AWS) Req: L4 · Org avg: L3

Network Design & Architecture Req: L4 · Org avg: L4

Prompt Engineering / Gen AI Req: L3 · Org avg: L1

Horizontal bar = current avg. proficiency. Red line marker = required level. Gap = area to the right of bar but left of marker.

⑧ Assessment Design Planner — Hire vs. Develop Decision Matrix

For each critical skill gap, determines whether to invest in a hiring assessment tool (external pipeline) or a learning/development tool (internal pipeline). Decision is driven by: (1) whether the skill exists at all in the organization, (2) the proficiency level required, and (3) AI automation risk. Higher required proficiency → higher-fidelity assessment tool needed.

📋 I/O Psychology · Talent Assessment · L&D Design

Wireless Engineering Specialist

Critical Gaps Only

Skill	Req. Proficiency	Exists in Org?	Gap Severity	AI Risk	Recommended Tool Type	Tool Fidelity Needed	Action
5G Protocol Engineering	L4	Partially (L2–L3)	Critical	▸ Low	🏢 Hiring Assessment	High Fidelity	Work sample + structured interview
Cloud Architecture (AWS)	L4	Yes (L3 exists)	Moderate	▸ Med	📚 L&D Tool	Medium Fidelity	Skills assessment + certification path
Prompt Engineering / Gen AI	L3	Barely (L1 only)	Critical	▸ Med	🔀 Hybrid	Medium Fidelity	Engage contractors + build L&D in parallel
Data Analytics & Visualization	L3	Yes (L2 exists)	Moderate	▸ High	📚 L&D Tool	Lower Fidelity	Self-paced + AI-augmented curriculum
Kubernetes / Container Orch.	L3	No (not in org)	Absent	▸ Low	🏢 Hiring Assessment	High Fidelity	Technical screen + hands-on coding eval

ⓘ Decision logic: Skill absent from org → Hiring Assessment (prioritize high-fidelity work samples). Skill exists at L1–L2 but gap is 1 level → L&D Tool. Gap ≥2 levels AND skill absent → Hiring. High AI automation risk → lower-fidelity assessment is sufficient (skill will evolve or automate). All recommendations are AI-generated and require human validation before tool procurement.

2

Skills requiring Hiring Tools

5G Protocol + Kubernetes (absent or critical)

2

Skills for L&D Programs

Cloud Arch. + Data Analytics (exists, buildable)

1

Hybrid Approach

Prompt Engineering (Engage + Build in parallel)

Skills Intelligence Dashboard Suite
— What Good Looks Like

HRBP Strategic Workforce Planning

Job Architecture Governance

Job Analysis & Assessment Design Support

Skills Intelligence Dashboard Suite— What Good Looks Like

HRBP Strategic Workforce Planning

Job Architecture Governance

Job Analysis & Assessment Design Support

Skills Intelligence Dashboard Suite
— What Good Looks Like