AI Risk Report Auto-Generator

3.65

Derivation Chain

Step 1 'AI will disrupt everything by 2028' — Wall Street research outlook

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Step 2 Enterprise AI risk assessment service

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Step 3 Automated Report generation Infrastructure for AI risk assessment

Problem

As Wall Street and global research firms release a flood of AI-driven industry disruption forecasts, corporate planning teams at domestic SMEs (50-300 employees) must report their company's AI risks to the board. However, professional consulting costs 30-50 million KRW (~$22,500-$37,500) per engagement, and without internal analytical capability, Report preparation takes 2-4 weeks.

Solution

A SaaS that takes a company's industry sector, revenue structure, and workforce composition as input, then auto-generates a customized AI risk Report (executive summary, department-level impact assessment, response roadmap) based on the latest AI trend research and industry-specific AI replacement rate data. Quarterly update reports are automatically published.

Target: Corporate planning teams at SMEs with 50-300 employees, CFO/CSO

Revenue Model: 490,000 KRW (~$368) per Report, 190,000 KRW (~$143)/month with quarterly auto-renewal Subscription. 15% discount with Annual Subscription.

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

3.0/5

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 (70%)

Tech Complexity

29.3/40

Data Availability

20.4/25

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 (56/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

16.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

10.5/15

Solo Buildability

5.0/10

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 [low] AI/ML [medium]

Dashboard