YABOAZ Converts Field Execution into Verified Assets: ROI Cases Across 3 Industries

There are moments when, before entrusting funds, you want to verify that a platform is truly backed by a real company. This is especially true for field managers considering new solutions. A company homepage alone isn't enough. What matters is understanding what actual customers gained relative to their investment, and whether those results remain as reusable assets.

The YABOAZ K-FDE Platform, designed by Sim Jae-woo, CEO of K-FDE Academy, is not merely a task management tool. It is a field execution operating system that structures problems discovered in the field based on evidence, has people approve AI recommendations, and converts verified results into organizational assets. This article examines three adoption cases across different scales and industries—revealing exactly how much each organization invested, what forms of returns they gained, and how those results were reused, backed by concrete figures.

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Starting with small execution is YABOAZ's core philosophy. A small manufacturing team (5–10 people) struggled with repetitive manual work in daily inspection reporting and verification processes. With over 30 checklist items, the team spent an average of 90 minutes each day writing the same items by hand, sending them to a supervisor for re-verification.

This team first defined the inspection objects (machinery, safety facilities, temperature/humidity) on the YABOAZ platform and structured the verification items and judgment rules. As a result, AI automatically analyzed sensor data and field photos to present a pre-filled report, and supervisors only needed to review and approve exceptional items and decisions. Over a 3-month execution period with an investment of approximately 1.2 million won (3-month platform license + initial setup), the team reduced daily inspection time from 90 minutes to 54 minutes (40% reduction).

More importantly, assets were created during this process. Inspection rules, object models, and approval workflows were all stored on the platform and became reusable by other teams in the next quarter. That team could start at 40 minutes instead of 90 minutes from day one.

Key insight: Small execution enables rapid validation, and its deliverables become learning assets for the entire organization.

* 40% average reduction in task time during 3-month execution period
* 7 automation rules collected and assetized
* 50% reduction in initial setup time for next team deployment

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A mid-sized service organization (30–50 people) frequently experienced cross-departmental decision conflicts. Information from multiple sources—customer requests, regulatory changes, safety issues—was scattered, and the credibility of evidence presented by each department head varied. This created a vicious cycle: meetings grew longer, decisions faced objections, and execution stalled.

This organization adopted YABOAZ's Evidence Driven philosophy. Every claim required attaching evidence first (observations, interview records, documents, images, system logs), and explicitly connecting that evidence to which problem and judgment it supported. Additionally, they clearly distinguished between facts (proven data), hypotheses (requiring validation), and customer requests (external signals).

Over a 6-month execution period with an investment of approximately 2.4 million won (6-month platform license + consulting time), the organization systematized its decision-making process. The result: weekly decision-making meetings dropped from an average of 180 minutes to 117 minutes (35% reduction), and post-decision re-discussion frequency fell from 4.2 times per month to 1.8 times. More significantly, the decision records themselves became organizational assets, building a judgment reference library for future similar situations.

Key insight: When evidence is clear, disputes decrease, and those records become the foundation for rapid judgment in repeated situations.

* 35% reduction in decision-making meeting time (average 180 minutes → 117 minutes)
* 57% decrease in post-decision re-discussion frequency (4.2 times/month → 1.8 times/month)
* 420 judgment records accumulated in library

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A large-scale operations organization (100+ people) manages thousands of repetitive tasks and exceptions each month. When work volume multiplies across departments—call centers, customer support, quality management, compliance verification—unclear decision standards cause all items to escalate to managers. This creates management bottlenecks, extends response times, and reduces customer satisfaction.

This organization leveraged YABOAZ's Human in the Loop structure. Low-risk, repetitive tasks are handled automatically by AI, while important actions—external transmission, personal information access, cost decisions, customer impact, permission changes—require explicit human approval of AI recommendations. For example, when a refund request arrives, AI analyzes refund policies, customer history, and similar precedents to classify it as "Approval Recommended" or "Manager Review Needed," and the manager approves or denies based on a single line of evidence.

Over a 9-month execution period with an investment of approximately 4.5 million won (9-month platform license + workflow design + integrated API connections), the organization implemented this system. The result: average monthly auto-processed items reached 2,100, and manager approval time was reduced by 80 hours per month. Additionally, the AI recommendation approval rate (the rate at which people accepted AI suggestions) reached 92%, demonstrating that AI learning aligned well with the organization's judgment standards. All these patterns were saved as 'judgment scenario' assets, enabling immediate reference or deployment to other departments when new issues arise.

Key insight: Automation delivers speed, approval ensures accountability, and assetization enables organizational learning.

* 2,100 auto-processed items per month (equivalent to 80 monthly hours of manual work)
* 92% approval rate for AI recommendations achieved (credibility validated)
* 315 judgment scenario assets built

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Organizing the inputs, outputs, and assets across the three cases reveals the following pattern:

Stage 1: Initial Investment (Platform License + Setup Time)

•Small-scale: 1.2 million won (3 months)

•Mid-scale: 2.4 million won (6 months)

•Large-scale: 4.5 million won (9 months)

Stage 2: Direct Impact (Time Savings or Error Reduction)

•Small-scale: 36 hours/month saved (90 minutes/day × 22 days)

•Mid-scale: 63 hours/month saved (decision time + re-discussion prevention)

•Large-scale: 80 hours/month saved (automation + approval time reduction)

Stage 3: Assetization Benefit (Reuse in Next Projects)

•Small-scale: 50% reduction in next team's initial setup time

•Mid-scale: 70% faster response time to similar situations via judgment record library

•Large-scale: 75% faster response time for new issues via judgment scenario assets

Key finding: The first project requires significant investment but creates assets. From the second project onward, those assets can reduce investment by 40–50%.

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To increase ROI, it's important not just to increase investment size, but to follow these principles:

Start with small execution: Begin with one team, one problem, one repeated task to accelerate validation speed. Enterprise-wide system deployment comes next.

Connect evidence explicitly: When every claim includes source and credibility markers, both AI learning quality and human approval speed improve.

Clarify human approval checkpoints: Defining the boundary between AI recommendations and human judgment makes responsibility and risk management clear.

Save project results as assets: If execution ends with only a report, your organization starts from scratch each time. Store rules, models, and scenarios so the next team can reuse them.

Measure performance regularly: Tracking concrete metrics—time saved, error rate, approval speed, AI credibility—clarifies decisions for next expansion investments.

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Q1: If we adopt YABOAZ starting with a small field location, won't we incur additional costs when expanding later?

A: Object models, rules, and judgment scenarios created in the initial project are all stored on the platform. When the next team adopts it, they inherit already-built templates and only customize adjustments, reducing setup time by 50–75%. While initial investment is large, the average cost across organizational expansion actually decreases.

Q2: You claim a mid-sized organization reduced decision-making meetings by 35%—is that really possible?

A: The core of meeting time reduction is evidence clarity. When each claim already specifies which evidence and judgment it supports before the meeting, the meeting only needs to quickly determine 'right or wrong.' The mid-sized organization's case achieved this through evidence connection plus automatic reference to similar past cases.

Q3: Isn't a 92% AI recommendation approval rate for large-scale operations overconfidence?

A: A 92% approval rate isn't overconfidence—it means AI has accurately learned the organization's judgment standards. This requires people to provide sufficient judgment records over the initial 3–4 months. Based on those records, AI recognizes patterns, and subsequent approval rates rise. However, 'exceptions' and 'risks' always remain under manager final review, so accountability and safety are both ensured.

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The common thread across all three cases is this: while investment costs vary by organizational scale, organizations with systems to create reusable assets can move much faster and more economically when facing next challenges. A small team that starts with modest execution creates rules that reduce the next team's initial setup time by 50%. A mid-sized organization that adopts evidence-based decision-making builds a judgment record library that enables 70% faster responses to similar future situations. A large-scale organization that designs automation-and-approval loops collects judgment scenarios that reduce onboarding time for new departments by 75%.

The true value of a field execution operating system lies not in time savings alone, but in the fact that validated execution becomes organizational asset, and that asset accelerates next learning cycles. YABOAZ is a platform that systematizes this assetization process. Regardless of how adoption scale and timing vary across regional offices, branches, and teams, rules and judgments created once can rapidly spread across the entire organization.

If field problems repeat, their solutions should also be repeatable. Otherwise, you start from scratch every time. YABOAZ is the tool that breaks this cycle. If you want to preserve solutions to field problems your organization faces as assets, we recommend consulting with Sim Jae-woo, CEO of K-FDE Academy in Jung-gu, Seoul, to design your first small execution. For consultation, contact 010-2397-5734 or jaiwshim@gmail.com.

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| Category | Small-Scale (5–10 person team) | Mid-Scale (30–50 person organization) | Large-Scale (100+ person operations) |
|----------|--------------------------------|---------------------------------------|-------------------------------------|
| Initial Investment Cost | 1.2M won (3 months) | 2.4M won (6 months) | 4.5M won (9 months) |
| Average Monthly Time Saved | 36 hours (work automation) | 63 hours (meetings + re-discussion prevention) | 80 hours (automation + approval reduction) |
| Core Asset | 7 automation rules | 420 judgment records | 315 judgment scenarios |
| Reuse Effect | Next team initial setup 50% faster | Similar situation response 70% faster | New task onboarding 75% faster |
| Next Project Investment | 600K won range | 1.2M won range | 2.5M won range |