In the Field, Decisions Are Constantly Required
In real-world business operations, decisions are required at all times.
- Which customer should be prioritized?
- Which proposal has the highest probability of closing?
- How should a particular issue be handled?
Customer Engineers (CE), System Engineers (SE), and Sales teams
continuously make these decisions at high speed.
However, the following challenges are always present in the field.
Structural Challenges in Field Operations
① Information Is Fragmented
- Customer data is in CRM
- Incident data is in another system
- Past interactions are in emails or chats
👉 Necessary information is not available in one place when needed.
② Decisions Are Dependent on Individuals
- Experienced staff are fast and accurate
- Less experienced staff take more time
👉 Skill differences directly translate into performance differences.
③ Work Is Reactive
- Actions are taken only after problems occur
- Proposals come too late
👉 Opportunities are lost.
④ Success Is Not Understandable
- Reasons for winning deals are unclear
- There is no reproducibility
👉 Revenue becomes unstable.
The Core Problem
The essence of these challenges is:
Field operations are a continuous chain of decisions,
yet they are not structured.
Solution Approach
Decision Trace Model × Multi-Agent
The key to solving this problem is:
Treating decisions in the field as a system.
Decision Trace Model
In the Decision Trace Model, decision-making is defined as:
Event → Signal → Decision → Execution → Human → Log
Next-Generation Field Operation Structure
When applied to field operations, the structure becomes:
Customer / Event
→ Signal (AI / Data Integration)
→ Decision (Next Action / Proposal / Priority)
→ Execution (Call / Visit / Proposal / Fix)
→ Human Feedback
→ Decision Log
Key Point
👉 Work is treated not as “tasks,” but as decisions.
Decomposing Work with Multi-Agent
Field decisions are handled by role-based agents:
- Customer Agent: estimates customer state (engagement, issues)
- Opportunity Agent: detects opportunities and win probability
- Issue Agent: analyzes problems
- Solution Agent: generates proposals
- Policy Agent: enforces pricing, contracts, compliance
- Risk Agent: evaluates risks (loss, complaints)
- Execution Agent: supports execution
👉 The thinking process of the field is directly systemized.
Key Differences from Traditional Approaches
① From Reactive to Proactive
Traditional:
- Focused on responding to inquiries
New Model:
- Anticipates customer needs and proposes proactively
👉 No missed opportunities.
② Elimination of Individual Dependency
Traditional:
- Relies on experienced individuals
New Model:
- Decision logic is shared (DSL / Policy)
👉 Everyone achieves high performance.
③ Traceable Sales and Operations
Traditional:
- Unknown why a deal was won
New Model:
Why was this deal won?
→ Signal (increased usage + emerging issue)
→ Decision (upsell proposal)
→ Policy (within pricing constraints)
→ Execution (proposal delivered)
👉 Success becomes reproducible.
④ Faster Decision-Making
Traditional:
- Time spent gathering information
New Model:
- AI integrates and summarizes data
👉 Instant decisions and actions.
⑤ Robust Field Operations
Traditional:
- Wrong decisions lead to complaints
New Model:
- Checked by Policy / Risk Agents
- Human-in-the-loop
👉 Prevents incidents.
⑥ Practical Execution with Asynchronous Processing
Field operations are not only real-time.
Decision → Queue → Worker → Execution
- Night-time analysis
- Morning prioritization
- Real-time recommendations
👉 Fits real-world workflows.
Use Cases
Customer Engineers (CE)
- Detect anomaly signals → proactive response
- Suggest optimal resolution procedures
System Engineers (SE)
- Translate customer issues → solution proposals
- Provide design support
Sales
- Identify high-probability deals
- Optimize timing of proposals
Revenue Impact
This model is not just about operational improvement.
👉 It directly transforms revenue.
① Higher Win Rates
- Proposals at optimal timing
② Increased Upsell / Cross-Sell
- Proposals based on customer state
③ Reduced Opportunity Loss
- Proactive sales approach
④ Improved Sales Efficiency
- Optimized prioritization
Organizational Impact
① Faster Talent Development
- Shared decision processes
② Knowledge as an Asset
- Accumulation of success and failure
③ Scalable Organization
- Reduced dependency on individuals
The Fundamental Shift
The essence of this approach is:
Transforming field operations
from “task execution”
to “decision orchestration”
Traditional
- Task processing
Future
- Decision processing
Conclusion
The next evolution of field operations is not efficiency.
It is:
👉 Structuring decisions
With Decision Trace Model × Multi-Agent:
- Decisions become visible
- Reproducible
- Faster
- Directly tied to revenue
CE, SE, and Sales evolve:
👉 From operators
to decision-makers
AIシステム設計・意思決定構造の設計を専門としています。
Ontology・DSL・Behavior Treeによる判断の外部化、マルチエージェント構築に取り組んでいます。
Specialized in AI system design and decision-making architecture.
Focused on externalizing decision logic using Ontology, DSL, and Behavior Trees, and building multi-agent systems.
