Agent ROI Proof Builder

2.65

Derivation Chain

Step 1 AI agent enterprise software proliferation

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Step 2 Need for AI agent adoption decision support

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Step 3 Post-adoption ROI proof and internal reporting automation

Problem

Mid-level managers (team leads/department heads) at companies piloting AI agents must demonstrate ROI to senior executives, but spend 8-12 hours per month collecting quantitative data such as 'time saved' and 'error rate reduction' and compiling it into reports. Insufficient proof leads to agent budget cuts, causing team productivity to decline again.

Solution

Automatically collects pre/post-agent task processing time, error counts, and costs to generate an ROI dashboard. Auto-generates one-page impact reports for executive briefings and provides department-by-department and agent-by-agent comparative analysis. 'Before vs. After' visual comparison charts make results instantly understandable for non-technical executives.

Target: Mid-level managers (department heads/team leads) at companies with 30-100 employees (IT, Marketing, CS departments)

Revenue Model: SaaS monthly flat rate $37/department (up to 5 agents), company-wide plan $150/month unlimited, 15% discount for annual billing

Ecosystem Role: Consumer

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

2.0/5

U Urgency

3.0/5

M Market

3.0/5

R Realizability

3.0/5

V Validation

2.0/5

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 (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

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 (50/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

7.5/15

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