A

AI Layoff-Proof Retirement Scenario Designer

4.15

Derivation Chain

Step 1 AI adoption leads to half of employees being laid off
→
Step 2 Self-diagnosis of AI replacement risk for office workers in their 50s
→
Step 3 Integrated scenario design for severance, unemployment benefits, and pension in case of involuntary retirement

Problem

As restructuring due to AI adoption becomes a reality, office workers in their mid-50s who want to simulate 'what happens if I'm laid off' must check severance pay (by DB/DC type), unemployment benefit duration and amount, early National Pension eligibility, and health insurance conversion across different institutional websites, taking at least 4-5 hours. A wrong decision to take severance as a lump sum can result in a tax difference of several million won (approx. $2,000–$7,500).

Solution

By entering years of service, annual salary, retirement pension type, and family composition, it displays on one screen: (1) a comparison table of after-tax payouts for three scenarios—voluntary resignation, mutual agreement separation, and retirement at retirement age, (2) automatic calculation of unemployment benefit duration and estimated monthly amount, and (3) a break-even graph for early vs. normal National Pension withdrawal. The differentiator is its specialization in 'involuntary retirement' scenarios.

Target: Office workers aged 52-58 at large and mid-sized companies, in industries with active AI adoption (finance, manufacturing, IT), with a spouse and 1-2 adult children, enrolled in DB or DC retirement pension plans.
Revenue Model: Basic simulation is free; detailed scenario report PDF download costs 5,000 KRW per transaction (approx. $3.75); when matching with tax accountants or labor attorneys, a brokerage fee can be converted to a monthly subscription model.
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
5.0/5
M Market
4.0/5
R Realizability
4.0/5
V Validation
5.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (74/100)

Competition
10.0/20
Market Demand
20.0/20
Timing
18.0/20
Revenue Signals
9.0/15
Pick-Axe Fit
10.5/15
Solo Buildability
7.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Frontend [medium] Backend [medium] Data Pipeline [low]
Dashboard