AI Work Log Organizer for HR Evaluations

3.85

Derivation Chain

Step 1 Google mandates AI usage for non-engineers

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Step 2 Pressure on 50-something office workers to adopt AI

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Step 3 After AI usage level diagnosis, they start applying AI to actual work

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Step 4 Difficulty in organizing AI performance for HR evaluations

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 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.

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	/ 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 (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	/ 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

Frontend [medium] Backend [medium]

Dashboard