B

AI Work Log Organizer for HR Evaluations

3.85

Derivation Chain

Step 1 Google mandates AI usage for non-engineers
→
Step 2 Pressure on 50-something office workers to adopt AI
→
Step 3 After AI usage level diagnosis, they start applying AI to actual work
→
Step 4 Difficulty in organizing AI performance for HR evaluations

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

As AI usage begins to be reflected in HR evaluations, office workers in their 50s face the problem: 'I've used AI at work, but how do I record this as a performance?' They may have used ChatGPT to draft reports or attempted data analysis with AI, but they don't know how to write these activities in their HR evaluation self-assessment. They need quantitative descriptions like 'Reduced report writing time from 3 hours to 1 hour using AI,' but they don't keep records, so they rely on memory during evaluation season.

Solution

Users can simply record AI-related work on the web (date, task name, AI tool used, estimated time saved), and the system automatically generates summary sentences for HR evaluation self-assessments on a quarterly/semi-annual basis. It provides quantitative statements like 'This quarter: 15 AI usages, average 40% time reduction, main areas: report writing (8 cases), data analysis (5 cases).'

Target: Office workers aged 45-58 at large enterprises, employees at companies that reflect AI usage in HR evaluations, and those who find writing self-assessments difficult.
Revenue Model: Free: 10 records per month. Unlimited records + auto-generation of HR evaluation self-assessment: 4,900 KRW/month (approx. $3.70). B2B corporate group subscription.
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
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 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 (68%)

Tech Complexity
24.0/40
Data Availability
24.4/25
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 (61/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
18.0/20
Revenue Signals
10.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
8.0/10
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]
Dashboard