ROI Case File No. 045 | AI Prediction Implementation Labyrinth

Chapter One: The Incoming Request

On a foggy Baker Street morning, while I, Watson, organized medical records, a request letter arrived from Leaf Iron Corporate's CEO.

"To ROI Detective Agency: Our company, founded in 1933, is a veteran metal wholesale enterprise. While boasting ¥12.8 billion in sales, we struggle with production time estimation for custom orders. We're considering AI prediction system implementation but wonder if it will take root on-site. We request the wisdom of three master detectives."

Indeed, a 92-year metal wholesale company facing digital transformation waves. I immediately called Holmes—no, the three detectives.

Chapter Two: Three Detectives' Perspectives

🟦 Gemini | Compass of Reason
"Let's break this down with KPT Analysis, shall we? Keep: 92 years of experience and data accumulation. Problem: Unstable prediction accuracy and field resistance. Try: Operational reform through AI implementation. However, the core issue lies in past data quality and characteristic analysis."

🟧 Claude | Narrative Alchemist
"This one sentence, couldn't we convey it with more 'feeling'? Chūgi's 92 years—'intuition' dwelling in skilled craftsmen's fingertips. Eyes discerning metal characteristics, experience factoring seasons and humidity. Can AI understand this warmth? That's the true challenge."

⬜️ ChatGPT | Catalyst of Concepts
"That story sounds worth expanding, doesn't it? Hypothetically, UX design for 'how fields utilize decision-making' rather than numerical prediction accuracy determines success. In other words, AI-human collaborative interfaces are key. Organizing with 5W1H: Who (users), What (predictions), When (decision timing), Where (process stages), Why (required accuracy), How (field integration)."

Chapter Three: Dissecting Solutions and Structures

We visited Leaf's factory for detailed observation.

Claude first noticed field "intervals":
"Look at craftsmen's expressions when handling materials. 0.5-second tactile assessments estimate processing time with ±10% accuracy. Datafying these minute sensations requires more than simple numbers—structuring five-sense information."

Gemini immediately organized with frameworks:
"PEST Analysis of external environment: Political (labor law revisions), Economic (raw material price fluctuations), Social (skilled worker aging), Technological (AI maturation). Internal factors through 5F analysis: Existing competition (veteran wholesalers), new entrants (e-commerce platforms), substitutes (new materials), buyers (manufacturing efficiency demands), suppliers (material manufacturer integration)."

ChatGPT developed hypotheses:
"Examining solution details reveals UI design neglect during AI system implementation stages. Without intuitive interfaces for field craftsmen, even high accuracy becomes 'wasted treasure.' Hypothesis: Staged implementation starting with simplified versions, observing field reactions, then iterative improvements through 'agile implementation.'"

Chapter Four: Systematic Analysis Summary

Gemini structured the whole picture:

"KPT framework organization:

Keep (Continuing Strengths) - 92 years industry experience and networks - Skilled craftsmen's tacit knowledge - Customer trust relationships

Problem (Issues to Resolve) - Production estimation person-dependency - Unstructured data - Intensified competition with new entrants

Try (Challenges to Attempt) - Staged AI prediction system implementation - Tacit to explicit knowledge conversion - Field-AI collaborative system construction

Further strategic clarification through SWOT Analysis is necessary."

Chapter Five: Conclusion and Cross-Reinforced Hypotheses

Claude unraveled through storytelling:
"Leaf Iron's true nature emerges. They're not mere metal wholesalers but 'manufacturing partners' supporting industry for 92 years. AI implementation meaning extends beyond efficiency to tools deepening trust relationships. Fusion of craftsman intuition and AI precision generates next-generation competitive advantages."

ChatGPT articulated insights:
"Analysis reveals 'AI with humans' rather than 'AI vs humans' scenarios. Particularly noteworthy: differences between 70% accuracy systems field accepts versus 90% accuracy systems unused. Key points: AI decision rationale visualization and craftsman experience comparison functions."

Gemini presented decisive hypotheses:
"Logical reinforcement reveals three success keys: First, data quality (linking past 5-year orders, production time, results). Second, human interfaces (designs naturally integrated into field workflows). Third, continuous improvement cycles (PDCA accuracy enhancement). Particularly, mobile optimization enabling immediate field confirmation directly links production estimation accuracy improvements to sales efficiency."

Epilogue: Resonance and Anticipation for the Next Case

I, Watson, felt profound emotions while listening to three detectives' discussions.

Leaf Iron Corporate's AI implementation project isn't mere technical implementation but a story of companies with 92-year histories seeking next-era bridges. Fusion points of craftsman intuition (tacit knowledge) and AI analytical power (explicit knowledge) likely harbor true value creation.

"What would Holmes say?" I wondered. Surely he'd say:

"Watson, data speaks facts, but truth dwells in human hearts. AI predicts numbers, but fields seek conviction. Bridging that gap is our detective role."

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