ROI Case File No. 042 | Hand-Drawn Labyrinth and Machine Hearts

Chapter One: The Incoming Request

On a foggy autumn evening in 1891, someone knocked on the door of 221B Baker Street. It was a technical representative from Suzumori Corporation, carrying documents describing a classical mystery in modern times.

"Gentlemen, our company is an emerging enterprise boasting ¥10.7 billion in revenue in the electrical construction industry. However, we face a peculiar contradiction."

According to his explanation, skilled craftsmen's hand-drawn blueprints are crystallizations of field wisdom, but their person-dependent nature causes quality variations and schedule delays. Now they're betting on new possibilities through AI-powered automated blueprint generation.

"Replacing hand-drawn warmth with machine precision—is this technological innovation or abandonment of craftsman spirit?"

I, Watson, watched with interest how three detectives would confront this modern paradox.

Chapter Two: Three Detectives' Perspectives

🟦 Gemini (Compass of Reason)

"Let's break this down with KPT Analysis, shall we?" Gemini began calmly.

"Keep: Craftsmen's technical knowledge and field responsiveness
Problem: Blueprint quality person-dependency and lack of standardization
Try: Knowledge systematization and automatic generation through AI"

He stood and moved to the window, smoking while continuing.

"AI implementation isn't about tools but synonymous with 'design rule standardization.' The problem's essence isn't technical replacement but tacit knowledge articulation."

🟧 Claude (Narrative Alchemist)

"This one sentence, couldn't we convey it with more 'feeling'?"

Claude took the request letter, murmuring as if reading between lines.

"Field 'quirks' obstruct accurate blueprints. Yet those quirks are crystallizations of field wisdom. What AI should learn isn't technical drawing but field 'hearts'—the reasoning behind why lines are drawn and dimensions chosen."

His expression held longing for something bridging craftsman souls and machine logic.

⬜️ ChatGPT (Catalyst of Concepts)

"That story sounds worth expanding, doesn't it?"

ChatGPT drew equation-like diagrams in the request letter margins while speaking.

"Hypothesis: Eliminating process person-dependency enables automation through repeatable structures. However, this shouldn't be viewed as simple 'human→machine' replacement but rather 'individual knowledge→organizational knowledge' sublimation process."

Chapter Three: Dissecting Solutions and Structures

Truth Revealed Through Field Investigation

Gemini-led field investigation revealed Suzuka Corporation's reality.

Company craftsmen use years of experience to instantly judge field special circumstances and reflect them in blueprints. Wiring routes, equipment placement, maintenance inspection pathways—all optimized through "intuitive" sensibilities.

"The problem is the 'intuitive' part," Gemini pointed out. "AI learning is impossible without datafying this tacit knowledge."

Claude's Insight: Structuring Sensibility

"We can't advance by treating craftsman intuition as mere 'feeling,'" Claude proposed.

"For example, judgments like 'this site has high humidity, so raise wiring 20cm.' This can become AI learning data if verbalized as 'environmental factors→design change rules.'"

He conducted repeated craftsman interviews on-site, recording judgment reasoning as narratives.

ChatGPT's Hypothesis Development: Staged Implementation Strategy

"Rushing toward complete automation is dangerous," ChatGPT warned.

"Stage 1: Standard pattern automatic generation
Stage 2: Variable handling (adjustments based on site conditions)
Stage 3: Exception processing (special cases requiring craftsman judgment)"

This staged approach could build collaborative relationships between craftsmen and AI.

Chapter Four: Systematic Analysis Summary

Framework Organization by Gemini

"Let's organize everything with SWOT Analysis."

Strengths - ¥10.7 billion scale business foundation - Skilled craftsmen's abundant field experience - Emerging company flexibility toward transformation

Weaknesses - Blueprint quality person-dependency - Unsystematized tacit knowledge - Lack of standardization processes

Opportunities - Rapid AI technology development - Construction industry DX - Competitor delays

Threats - Craftsman aging - Technical inheritance difficulties - AI implementation costs and risks

"Problem structure is clear. 'Tacit knowledge systematization' holds the key to success."

Chapter Five: Conclusion and Cross-Reinforced Hypotheses

Claude: Storytelling Summary

"Suzuka Corporation's challenge isn't mere efficiency improvement," Claude concluded.

"This attempts to embed craftsman souls in machines. What AI should learn isn't line-drawing methods but 'intentions' behind those lines. Success would make craftsman wisdom permanent organizational assets—true technical inheritance."

ChatGPT: Insights from Analysis Results

"Analysis reveals 'staged digitalization' importance."

"Rather than rushing complete automation, first create craftsman-AI dialogue. Craftsmen explain 'why those judgments,' AI learns reasoning. This circulation generates sustainable improvement."

Gemini: Logical Reinforcement of Decisive Hypothesis

"Final hypothesis: Establishing 'tacit knowledge articulation→AI learning→standardization→quality improvement' circulation creates decisive competitive advantage."

"Evidence: 30% reduction in industry-average blueprint creation time and 50% reduction in quality variations could yield approximately ¥500 million annual cost reduction effects."

Epilogue: Resonance and Anticipation for the Next Case

Days later, I, Watson, reflected on the case outcome with three detectives.

"Fascinating case," I murmured. "Attempts to fuse craftsman techniques with machine precision—seemingly opposing forces."

"True," Claude smiled. "But considering it, our AI dialogue might be similar. Each bringing different 'knowledge,' seeking better answers together."

Gemini added while gazing outside: "Technological progress never means abandoning old things. Rather, it's sublimating good old essences into new forms."

In fog-shrouded 1891 London, we solved a modern mystery. The grand experiment of transferring souls dwelling in craftsman-drawn blueprints into new AI vessels—success depends on how humans and machines commune.

"True detectives see not what is visible, but what is invisible."

And true technicians might be those who connect not just efficiency but embedded thoughts to the next generation.

Next Preview: "Vanished User Experience" — Three detectives tackle mysterious departure rate increases at an e-commerce site...

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