ROI Case File No. 010 | Don't Move People, Let Systems Move—Liberation from Manual Input Hell

Chapter I: An Incoming Request

1891 London, the detective office at 221B Baker Street. Through morning fog, a consultation letter arrived. As I, Watson, opened the seal, it revealed the serious concerns of a modern enterprise.

"I'm Tanaka from the Information Systems Department. We manually process sales, order, and inventory data monthly, requiring 50 hours. Among 3,000 data entries, 5% contain errors, and the entire department is exhausted. Please lend us your assistance."

With morning coffee in hand, Mr. Tanaka must be sitting bewildered before his computer. I could picture him drowning in seas of numbers, fatigue accumulating in his fingertips as they strike the keyboard.

"A fascinating case," Holmes murmured. "Human limitations intersecting with system possibilities."

Chapter II: Three Detectives' Perspectives

🟦 Gemini's Logical Analysis

"Let's break this down with a KPT Analysis, shall we?"

Gemini took the letter and began calm analysis.

Keep (points to maintain) - Awareness of data accuracy - Responsible staff structure

Problem (issues) - Time cost from manual input: 50 hours monthly - 5% error rate (150 out of 3,000 entries) - Lack of inter-system connectivity - Inefficient human resource allocation

Try (improvement measures) - Data integration API construction - Input validation function implementation - Phased transition plan establishment

"Structurally speaking, this is a transition case from 'human-dependent' to 'system-dependent.'"

⬜️ ChatGPT's Hypothesis Development

"That's quite intriguing to explore further, isn't it?"

ChatGPT began speaking while deep in thought.

"Let's imagine Mr. Tanaka's day. 9 AM, opens Excel. Visually copies sales data from the core system. Transcribes order data from another system. Inventory data from yet another location... He's like an information courier."

"But what if these systems could talk directly? Data flows automatically, and Mr. Tanaka could focus on exception handling and analysis. Isn't that the true role of an Information Systems Department?"

Emerging hypotheses included: - Visualization of each department's workflow - Optimization of input timing - Automated error detection

🟧 Claude's Narrative Insight

"Perhaps we should convey this with more 'feeling'?"

I, Claude, viewed the case from another angle.

"Through Mr. Tanaka's fingertips flows the company's lifeblood. Sales, orders, inventory—these are corporate lifelines. But the era of humans carrying information is ending."

"Consider this: would a postal worker memorize and carry letters one by one? Information has its pathways, humans have their roles. Mr. Tanaka shouldn't direct information traffic, but interpret information's meaning and paint the future."

Chapter III: Dissecting Policies and Structures

Data Integration Architecture Design

Gemini explained while drawing structural diagrams:

"First, let's organize current information flow with 5W1H" - What: Sales, order, inventory data - Who: Information Systems Department (mainly Mr. Tanaka) - When: Monthly, manual timing - Where: Excel, various core systems - Why: Data integration for management decisions - How: Manual transcription work

"The problem's essence is data existing in 'island' states. Implementing automatic API integration will resolve this structural challenge."

Migration Process Human Engineering

ChatGPT developed specific scenarios:

"I propose phased migration" 1. Phase 1: Automate most frequent sales data first 2. Phase 2: Add order data integration 3. Phase 3: Build inventory data and exception handling mechanisms

"What's important is Mr. Tanaka not feeling 'left behind.' His experience and knowledge are essential for system design."

Cultural Transformation Articulation

Claude proposed psychological support during transition:

"We need a narrative of 'work evolving' rather than 'work being stolen.'"

Words to use during transition: - "Your experience becomes the system's wisdom" - "Manual input accuracy creates automation standards" - "From data guardian to data translator"

Chapter IV: Systematic Analysis Summary

Gemini provides an overview:

SWOT Analysis for Current State Assessment

Strengths - High responsibility and data quality awareness - Understanding of existing systems

Weaknesses - Time constraints from manual work - 5% error rate quality issues

Opportunities - Work hour reduction through automation - Focus on more strategic tasks

Threats - Resistance to change - Risk of temporary operational confusion

ROI Calculation Model

Current Costs:
- Labor: 50 hours × ¥3,000 = ¥150,000/month
- Error handling: 5% × 20 min × ¥3,000 = ¥15,000/month
- Total: ¥165,000/month

Post-improvement Costs:
- Labor: 5 hours × ¥3,000 = ¥15,000/month
- System operation: ¥10,000/month
- Total: ¥25,000/month

Monthly reduction: ¥140,000
Annual effect: ¥1,680,000
Initial investment: ¥500,000
ROI: 236%, 3.6-month payback period

Chapter V: Cross-Reinforcement of Conclusions and Hypotheses

Claude's Storytelling

"Mr. Tanaka's story is a modern parable repeated in many companies. Talented personnel buried in routine tasks, unable to demonstrate their true value. However, this is also a beautiful transformation opportunity where technological progress and human growth join hands."

"A company's true stance demonstrates that 'valuing people' and 'pursuing efficiency' aren't contradictory. The organization's future lies in Mr. Tanaka's growth from information courier to information architect."

ChatGPT's Insight Development

"What emerges from this analysis is the boundary between 'operations' and 'innovation.' Mr. Tanaka's experience is a valuable asset for system design. Without his knowledge, truly usable automation systems cannot be created."

"Point to articulate: The key to success is permeating the philosophy that 'efficiency exists not to diminish human value, but to enhance it.'"

Gemini's Logical Reinforcement

"Let me solidify the final hypothesis with logic"

Hypothesis: "By automating manual input tasks and reallocating human resources to higher value-added work, we can improve overall organizational ROI."

Evidence: 1. Time efficiency: 90% work time reduction (50h→5h) 2. Quality improvement: Dramatic error rate decrease (5%→<0.1%) 3. Cost efficiency: 3.6-month investment recovery period 4. Strategic contribution: Work hour shift to data analysis and improvement proposals

"This numerical foundation is solid. Only execution remains."

Epilogue: Resonance and Future Expectations

As I, Watson, listened to the three detectives' discussion, I reflected anew. Mr. Tanaka's concerns aren't merely operational efficiency issues. This is a contemporary philosophical challenge questioning the relationship between humans and technology, and the meaning of work itself.

Gemini's logic, ChatGPT's ideas, Claude's sensitivity—each illuminated the same truth from different angles. Humans aren't replaced by machines; humans return to where they should be.

When Mr. Tanaka's fingertips no longer need to strike keyboards, those hands will begin drawing new futures. He who was drowning in data seas will now hold the compass that determines company direction as a data navigator.

In the clearing morning fog of Baker Street, we await the next case.

【Case Resolution Points】 - Time efficiency: 90% reduction (50h → 5h monthly) - Quality improvement: Error rate from 5% to <0.1% - Cost savings: ¥140,000 monthly (¥1.68M annually) - ROI: 236% with 3.6-month payback - Human transformation: From data courier to data architect

"A true detective sees not what is visible, but what is invisible. And true improvement isn't about people moving, but finding the pathways information should flow. The future is supported not by hands, but by structure."