PlusHuman Competency Diagnostic

4.15

Derivation Chain

Step 1 Spread of PlusHuman (human + AI collaboration) discourse in the AI era

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Step 2 Enterprise AI collaboration competency assessment service

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Step 3 Personal AI collaboration competency self-diagnostic and learning path designer

Problem

As AI tool proficiency becomes a core competency for hiring and promotions, professionals have no objective way to measure 'how well they collaborate with AI.' HR departments also lack standardized tools to assess employee AI literacy, making it impossible to prove ROI on training investments. Companies spend $3,750–$15,000 (approx.) per year on AI training yet cannot quantify outcomes.

Solution

Diagnoses AI collaboration competency by job function (marketing, finance, development, HR, etc.) through scenario-based practical tests, providing industry- and seniority-level percentile rankings. Based on results, it auto-recommends personalized microlearning paths and visualizes training ROI through a team dashboard for HR managers.

Target: HR managers at IT, marketing, and finance companies with 30–200 employees; office professionals in their 30s–40s

Revenue Model: B2B corporate license: ~$37.50/month per 50 users; B2C individual diagnostic: ~$7.50 per session; Microlearning Monthly Subscription: ~$14.25/month

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

5.0/5

V Validation

4.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (73%)

Tech Complexity

29.3/40

Data Availability

23.3/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (57/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

10.5/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [medium] AI/ML [low]

Dashboard