ROI Case File No. 039 | Cloud Labyrinth Hidden in IoT Farm Equipment's Shadow

Chapter One: The Incoming Request

"Watson, another intriguing request has arrived."

London 1891, fog settling over Baker Street 221B on a morning when I was preparing Holmes' breakfast. Door knocking echoed, revealing a sturdy gentleman—Technical Director of Revonstan R&D Corporation.

"Detectives, I need your help," he began breathlessly. "Our company, established in 1957 with ¥19.8 billion revenue, is an agricultural machinery manufacturer. Trying not to fall behind the times, we're advancing agricultural equipment IoT development, but..."

He clutched documents reading "AWS-compatible IoT construction, annual budget within ¥5 million."

"We can attach sensors and collect data. However, AWS design, communication stability, and especially the gap with field workers—these stand as enormous walls."

Chapter Two: Three Detectives' Perspectives

I called three detectives, each illuminating this complex mystery from different angles.

🟦 Gemini | Compass of Reason spoke first.

"Let's break this down with KPT Analysis, shall we? Keep (continue): Existing sensor technology and data collection capabilities. Problem: AWS architecture complexity and operational costs, field disconnection. Try (challenge): Staged cloud migration and usability design."

Gemini continued calm analysis. "IoT essence lies in 'utilization,' not 'collection.' We should design investments including BI and analysis pipelines, not just sensor design. With ¥5 million annual budget constraints, starting with monolithic configurations rather than microservices would be wise."

🟧 Claude | Narrative Alchemist stood gracefully.

"This one sentence, couldn't we convey it with more 'feeling'?" Claude's voice had distinctive resonance. "Engine sounds echoing in fields versus cloud's silent communication. This gap hinders human understanding."

"Consider this: Farmers are specialists reading soil scents, wind directions, crop expressions. Suddenly asking them to find meaning in dashboard number arrays is like asking poets to read accounting ledgers. We need empathy bridges between data and fields."

⬜️ ChatGPT | Catalyst of Concepts leaned forward.

"That story sounds worth expanding, doesn't it?" ChatGPT's eyes sparkled. "Let's hypothesize: AWS introduction bottlenecks aren't technical problems but 'operator learning costs.'"

"In other words, gaps exist in bridging edge computing and cloud services. Interface design allowing intuitive field worker understanding and sophisticated backend analysis function connections might be insufficient."

Chapter Three: Solution and Structure Dissection

Three detectives began detailed investigation of Revonstan R&D's current state.

Data Collection Flow Verification

Gemini first tackled existing system structural analysis. "Let's organize with 5W1H: Who: Agricultural equipment operators, What: Soil/weather/work data, When: Real-time to daily, Where: Fields, Why: Productivity improvement, How: Via IoT sensors."

"The problem is this How part. Sensor-to-AWS paths are too complex. LoRaWAN→Gateway→4G/LTE→AWS IoT Core→Lambda→RDS→QuickSight... Data gets lost somewhere in this long journey."

User Experience Storytelling

Claude began speaking as if having visited fields. "Imagine 5 AM when farmer Tanaka starts his tractor engine. His mind holds today's weather, yesterday's soil conditions, and next week's harvest plans."

"But current systems tell Tanaka 'Please check CPU usage rates on AWS CloudWatch.' This is collision between poetic and mechanical languages. It should convey 'Today's soil is 3% drier than yesterday; we recommend advancing watering timing by 2 hours.'"

Hypothesis Development and Verification

ChatGPT presented interesting perspectives. "Let's view this situation from different angles. Revonstan R&D might be transitioning from 'agricultural equipment manufacturer' to 'agricultural data company.'"

"Suppose IoT data insights develop beyond mere machine operations to 'optimal work pattern proposals' and 'harvest predictions'? Annual ¥5 million investment becomes seeds for new business models, not mere costs."

Chapter Four: Systematic Analysis Summary

Gemini stood and approached the blackboard. "Let's organize overall picture with SWOT Analysis."

Strengths - 67 years agricultural equipment manufacturing track record and deep field understanding - Strong trust relationships with existing customer base (farmers) - Basic sensor technology already implemented

Weaknesses - Insufficient cloud architecture design experience - Underdeveloped IT operational systems - Digital literacy gaps among field workers

Opportunities - Smart agriculture market rapid growth - Government DX policies - Increasing demand for data-driven agriculture

Threats - Major IT companies entering agriculture - Securing competitiveness with limited budgets - Keeping pace with technological innovation speed

"The core is how to integrate 'data service company' functions while maintaining 'agricultural equipment manufacturer' identity. Staged approaches are necessary."

Chapter Five: Conclusion and Cross-Reinforced Hypotheses

Claude | Storytelling Integration

"This case's essence is a 'translation' problem," Claude spoke gently. "Revonstan R&D needs to become translators between farmers speaking soil and sweat languages and clouds speaking in zeros and ones."

"I propose 'three-layer architecture': Layer 1: UI aligned with field sensibilities ('Additional watering needed today'), Layer 2: Business logic (work efficiency analysis and proposals), Layer 3: Technical foundation (AWS data processing). This structure allows farmers to focus on agriculture while systems work quietly."

ChatGPT | Insights and Development Possibilities

"What emerges from analysis results are possibilities beyond 'IoT implementation,'" ChatGPT spoke passionately. "If data collection→analysis→insights→action proposal cycles establish, Revonstan R&D could provide 'agricultural consultant' value."

"Specifically: Paid 'work optimization plans' based on collected data, anonymized comparison services with other farm data, and further development into 'performance guarantee services' linked with equipment lease contracts."

Gemini | Decisive Hypothesis Logical Reinforcement

"The final hypothesis is this," Gemini stated confidently. "Annual ¥5 million budget constraints actually present 'selection and concentration' opportunities. Rather than AWS-implementing everything simultaneously, strategic approaches with staged migration starting from high-ROI functions are success keys."

"Specific recommendations: 1. Phase 1 (¥2 million budget): Existing data visualization and dashboard construction 2. Phase 2 (¥1.5 million budget): Predictive analysis function addition 3. Phase 3 (¥1.5 million budget): Automation and alert function implementation

This staged approach allows experiencing effects at each phase while making next investment decisions."

Epilogue: Resonance and Anticipation for the Next Case

After the Technical Director departed, I reflected on three detectives' discussions.

"Fascinating case, wasn't it, Watson?" Holmes might have said. However, this mystery wasn't simple criminal pursuit. It was a story of finding wisdom for traditional companies to ride rather than be overwhelmed by new era waves.

Revonstan R&D's challenge epitomizes issues many manufacturers face. When acquiring new tools like IoT and cloud, how to harmonize them with essential company values—answers to this question may lie beyond technical solutions.

Three detectives demonstrated the value of viewing identical problems from different perspectives. Logical analysis, intuitive understanding, creative thinking—when these overlap, 'new possibility discovery' emerges beyond mere problem-solving.

Who will knock on Baker Street's door next, carrying what mysteries? What's certain is that with appropriate perspectives and analytical methods, light will always emerge from any complex problem.

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