AI Decision Engine — AIDE
Decision-making engine for the AI era
Implement decision-making as a continuously evolving engine.
The AI Decision Engine structures the rationale behind decisions and transforms that accumulated data into an organization's competitive advantage.
Management Issues — The Constraint
The amount of data has increased.
The decision-making process has not yet changed.
Even if investment in AI speeds things up on the ground, if management and business decisions don't change, performance won't improve.
The discussion is growing, but the points of contention are not aligned.
The basis for judgment is confined to the individual, and neither success nor failure remains within the organization.
What needs to be changed is not the amount of data.
It is the structure of the decision-making process itself.
The AI Decision Engine (AIDE) designs that structure.
AIDE's Composition
Three cores driven by humans and AI
platform
Data science consulting and engineering.
AIDE functions as an engine only when professional services provided by individuals with three specialized skills are combined with a unique AI platform.
AI Decision Engine
CORE 01 — Decision Insight
Semanticization of Data
Restructure data not merely as facts, but as a "system of mechanisms that drive business."
Market structure (Where to Play?), customer value (What to Win?), marketing investment (How to Win?), organizational execution (Where to Commit?).
We will identify the hidden driver from four different perspectives.
Data only becomes a basis for decision-making when it is used in the right way.
Market Structure
Market structure mechanism
Customer Value
Customer Value Mechanism
Organizational Execution
Organizational execution mechanisms
Marketing Performance
Marketing Mechanism
Over 200 accumulated features support the resolution of model design.
Since its founding in 2012, our analytical design framework (features) has been developed through working with over 300 enterprise companies. Appropriate features are combined according to the structure of each client's challenges, enabling mechanism elucidation that balances reproducibility and uniqueness.
Tailor Made— Case-by-case
Decision Insight is a consultant-driven approach. Data scientists and strategy consultants individually design analytical models for each client's unique challenges, data conditions, and industry structure.
Main analytical models for elucidating the mechanism
Brand Choice Driver
Unraveling the customer selection mechanism
A CMM (Consumer Mix Modeling) analysis platform for which a patent application is pending.
We quantify the impact of the 4Ps, CX, and brand assets on customer choice and identify the structure of "why customers choose us."
Performance Driver
Unraveling the Investment Mechanism
A patented multi-stage MMM (Marketing Mix Modeling) analysis platform.
We quantify direct and indirect contributions across channels, separate long-term brand effects from short-term sales effects, and derive the optimal budget allocation solution.
AI Decision Engine
CORE 02 — Decision Refinement
Advanced decision-making
Accelerate organizational thinking and decision-making processes through dialogue with AI.
We will connect the insights gained from unraveling the mechanisms and elevate them into a winning strategy for our company.
Where will we fight? What will we use to win? How will we fight? From the front lines to management, everyone operates with the same conviction.
Transform the "discoveries" of analysis into "confidence" that enables the entire organization to take action.
Consulting-Led
Consulting
We develop strategies based on data science insights, engage stakeholders, and consistently design everything from the initial planning to the 4P briefing sheet.
AIP-Native
AI Assist
An AI assistant that operates within AIP. It utilizes the analysis results from CORE 01 and the organizational knowledge from CORE 03 as context to accelerate strategic planning, scenario planning, and decision-making dialogue at platform speed.
— Consulting
The process of assembling the blueprint for battle
Step 00
Key driver
Extraction
By analyzing the mechanism, we extract the drivers that directly lead to a winning strategy.
Step 01
Our company/customer
Deepening the connection point
Interviews by hierarchical level /
Topic Modeling
Step 02
Towards a winning strategy
sublimation
POP / POD / POF
Step 03
Conceptualization and
Verification
Marketing concept /
Acceptance survey
Step 04
briefing
Sheet format (4 pages)
Product Department/Agent/
To sales
Deliverables— What we offer
An example of output to management and strategy levels
- Deriving integrated insights from various analyses
- Strategic Design for POP/POD/POF (Keystone Logic)
- Decision support (bridging the gap between analysis results and tactics)
An example of output to the on-site implementation layer.
- Marketing concept development and acceptance testing
- Creating a 4P/CX briefing sheet
- Strategic planning / Scenario planning
Case Study
The wolf is a member of the wolf family.
Japan's leading company, ST Corporation, has taken on the challenge of "evaporating demand."
Despite boasting the top market share with its flagship brand "Mushuda," the company was facing a shrinking market. They deciphered the mechanisms of the market, customers, investment, and organization, and then translated that structure into an AI context. The result was not merely an extension of functionality, but a redefinition of value itself.
*Brand Summit Spring 2026 Event Report
AI Decision Engine
CORE 03 — Decision Intelligence
Assetization of org. knowledge
Create a system that allows the organization to continuously learn by accumulating decision-making processes and their results.
Decisions made do not remain within an organization simply because they have been implemented.
"Decision Intelligence" is the process of accumulating the background, hypotheses, and results of decisions within an organization.
We combine organizational transformation consulting and engineering to redesign decision-making processes and performance indicators, building a knowledge infrastructure rooted in the organization.
Even when people change, the structure of decision-making remains within the organization. That is the assetization of decision-making.
Decision Log
Turning decision-making into an organizational legacy.
In organizations where AIP is implemented, the background, hypotheses, and results of decisions are accumulated together. By documenting the process of "why that point was reached" and "what mechanisms were used to make the decision," each decision is transformed into reproducible knowledge.
AI Platform
AI Platform (AIP)
A decision-making infrastructure that runs through three core elements.
The foundation that runs through these three cores and enables a seamless cycle of analysis, decision-making, execution, and learning is the "XICA*AI Platform".
By continuously monitoring the analysis in CORE 01, having AI support decision-making in CORE 02, and accumulating decision logs in CORE 03, this cycle continues, allowing the organization's decision-making to evolve exponentially.
CORE 01
From data to "meaning"
It constantly presents vast amounts of data not merely as charts, but as "insights" necessary for decision-making. AI that understands the business context automatically detects signs of change and anomalies.
CORE 02
Enhancing strategic "dialogue"
An AI assistant that utilizes CORE 01's analytical data and CORE 03's organizational memory as context. It supports strategic planning, scenario comparison, and real-time dialogue in management meetings at platform speed.
CORE 03
Turning decision-making into an organization's "asset."
The past decisions and their results accumulated in the Decision Log form the context for the CORE 02 AI Assistant. The more data is accumulated, the higher the quality and speed of decision-making becomes. Time itself is directly converted into a competitive advantage.
Accumulate decision-making as organizational knowledge, compounding its effects over time.
Two structural advantages emerge when AIDE becomes established within an organization.
Proprietary Intelligence · Organizational Knowledge as a Unique Asset
The more you accumulate, the more unique assets you can build.
- ↳The speed and resolution of decision-making will increase throughout the organization.
- ↳Success and failure experiences transcend individual experiences and spread rapidly throughout the entire organization.
- ↳The accumulated judgments become rooted in the company's business and organization, becoming irreplaceable management assets.
Organizational Resilience · The durability of an organization
Even if the people involved change, the structure of decision-making remains.
- ↳Even with retirements, transfers, and generational changes, the history of past decisions is passed down within the organization.
- ↳New leaders and the next generation of management can make decisions by inheriting the decision-making structures of the past.
- ↳While assuming the presence of highly skilled individuals, we aim to build an organizational structure that does not depend on their abilities.
The Philosophy of Growth — XICA SPIRAL
When AIDE becomes ingrained in the organization, growth becomes inevitable.
When the three cores begin to cycle, decision-making ceases to be a one-way process. Data generates insights, insights become strategies, strategies are implemented in the field, and that implementation generates the next set of data.
The "XICA Spiral" is a structural map that continuously drives this cycle on an organizational scale, compounding over time. This framework, systematized based on Senior Advisor Takashi Nawa's management transformation theory (Möbius model), becomes the engine of organizational growth when connected with AIDE, as the evolution of decision-making becomes the organization's growth engine itself.
