digivice Inc.
Just by “introducing AI”Management hasn't changed one bit.The reason why the KPIs of the three companies moved. The impact of changing and changing is here.
Decision-making stops on a quarterly cycle, ICT costs balloon, and investment decisions become sensory-based.This is not a problem with AI tools, but a structural problem with management issues. The numbers will change only if the decision cycle, cost structure, and responsibilities are rearranged based on AI. The following three cases are proof of this in practice.
Whether or not to invest in experience is decided based on intuition.Turning “why people were so excited” into numbersGo to trial OS.
Investment decisions for planning, production, EC, and contact points are dependent on viewing sensations, personal judgment, and memory of past performance—this is a management issue. It is integrated into the Experience SSOT with 38 points of contact, 12 layers, 7 AI layers, and 1 original, and learns the amount of heat, trust, and signs of withdrawal. Changed the planning and improvement cycle from monthly to weekly. The CEO will be able to confidently decide which experience investments to continue and which to stop.
Continuation or withdrawal of project investment is decided based on "feelings and meetings." You only realize it after you have incurred a loss.
Viewings, gifts, comments, purchases, and relationships are scattered across each distribution, making it impossible to compare why people got excited about it and why they cooled down. Planning and improvement will now be based on monthly meetings, and withdrawal decisions will not be made until losses are incurred. There is no material that allows CEOs to confidently decide where to make additional investments.
Learn enthusiasm, trust, and signs of withdrawal. Planning and improvement cycle changed from monthly to weekly. Deciding whether or not to invest is determined by numbers.
Integrated into Experience SSOT with 38 contacts, 12 layers, 7 AI layers, and 1 original. Learn the signs of enthusiasm, trust, immersion, and withdrawal. Decide whether to invest in production, contact points, or EC based on the KPIs of continued viewing, gifts, purchases, and revisits. Decision cycle shortened from monthly to weekly. CEOs can confidently make experiential investments.
Three management indicators that experience OS changes
We hierarchically control energy, trust, immersion, and signs of withdrawal, and update the materials that will be used to decide whether to continue or withdraw from the next project every week.
By looking at viewings, comments, gifts, and purchases at the same level of granularity, you can decide to withdraw before you incur a loss.
In the next project, we will examine which presentations and points of contact were effective in encouraging continuation, purchasing, and revisiting, and the CEO can decide based on numbers whether to continue investing or withdrawing.
6 implementation assets to recreate the enthusiasm
Front functions with 38 points of contact, 12 layers of back-end structure, and 7 layers of AI control---each of them takes on a role, and separates the layers that hinder the experience value from the layers that should be invested in.
The psychological/relationship model captures enthusiasm, trust, immersion, and signs of withdrawal, and automatically updates the priority of measures. 1 Original Experience Data will continue to provide the basis for the next project.
5 steps to turn experience signals into investment decisions
Rather than ending the broadcast as a temporary viewing experience, connect it to a learning OS that continues to drive management decisions.
Obtain experience signals from external distribution PF
API / Webhook / OCR can be used to align comments, views, and purchases to the same level of granularity, making it possible to compare KPIs by contact point.
Integration into Experience SSOT
Integrate scattered events and turn success factors, withdrawal factors, and purchasing paths into data that can be reused in the next project.
Learning with psychological/relational models
Learn about enthusiasm, trust, and signs of disengagement, and use it to decide whether to adopt or reject direction, appeal, and product guidance.
Reflected in production, planning, and EC investment
Connect ID, trust, logistics, enthusiasm, SSOT, and AI organizations to determine which experiential investments will return to the profit path.
Perpetuate the learning cycle
Update successful experiences and anti-patterns to enable horizontal deployment to advertising, EC, education, finance, and Enterprise DX.
Investment decisions are made based on intuition and follow-up.Use a trail to understand why you are making this investment nowToward the basis of judgment.
Lack of information, follow-up checks, and intuitive market views: These are not problems with information gathering, but structural problems with investment decisions. 30+ AI monitors global markets in parallel and transforms them into 8-pillar scoring and event-driven snapshots. Investigation man-hours reduced by 80% and decision lead time from 3 months to 2 weeks. Explaining ``why this investment'' with a trail so that the CEO can make a move with confidence.
Investment decisions depend on people's memories and senses. Lack of information and follow-up continue to result in lost opportunities and delays in cutting losses.
News, policy, financial results, supply and demand, and price fluctuations remain separated, and decisions continue to remain in people's memories and senses. The timing of re-evaluation is missed and opportunity losses accumulate. The CEO asks, “Why are we making this investment now? Why are we withdrawing now?” The lack of evidence to explain this continued to widen the gap with the competition.
Investigation man-hours ▲80%. Sense → Eight pillars of evidence. Compress decision lead time from 3 months to 2 weeks.
30+AI constantly monitors global markets in parallel and converts them into 8-pillar scores and event-driven snapshots. We will review the $151.94 trillion market using the same decision-making criteria, and be able to use the trail to determine investment, withdrawal, and revaluation timing. Investigation man-hours reduced by 80%, decision lead time reduced from 3 months to 2 weeks. CEOs can confidently explain why now.
6 judgment data assets that turn “feelings” into “trails”
30+ AI agents comprehensively monitor changes in the world (Yaoyorozu's AI watches over the world). Eight-pillar evaluation scores make investment decisions accountable, and event-updated snapshots enable proactive re-evaluation.
RAG + Multi LLM references external information and internal rules. Even in situations where LLM cannot be used, deterministic rules continue to provide information for judgment.
Three management indicators changed by market judgment AI
Reduce missing judgment materials and follow-up, and significantly reduce monthly investigation man-hours. Shorten decision lead time.
By comparing growth potential, finances, competitive advantage, and risk on the same axis, CEOs can use the trail to ask themselves why they are making this investment now.
Create a snapshot of the market signal and changes after the market close, and be confident in the timing of re-evaluation. Reduce opportunity losses and stop-loss delays.
5 steps to transform global changes into investment decisions
Change your investment decisions from feeling and memory to evidence and comparison axis.
Overlooking the world signals
Distinguish between themes to pursue and themes to abandon based on current events, policy, and price changes.
30+ AI teams collect in parallel
Collect news, policies, financial results, supply and demand, and risks in parallel to reduce manual search.
Basis for investment decisions
Score growth potential, finances, momentum, and risk using RAG + Multi LLM.
Consolidation into the original market judgment
Converting market signals and after-market changes into judgment data that can be compared to 44,000 companies.
Make instant decisions with the decision dashboard
Handle reversal detection, emergency alerts, and post-close reviews on the same screen to avoid confusion when making re-evaluation and withdrawal decisions.
ICT costs are rising, and funds for AI investment are not being generated.Free up defensive man-hours and invest in the next generation.
After 100 people x 4 years, decisions cannot be made, and man-hours continue to be spent on maintenance and research. This is not a "technical debt problem" but a management resource allocation problem. A team of 1 + 36 AI agents integrates code, DOM, SQL, and operation logs into Operational SSOT and drives innovation decisions with a trail. Compressed from 4,800 man-months to 3 man-months (99.9375% reduction). The 4,797 man-months worth of investment funds saved will be reallocated to AI infrastructure, profit improvement, and business innovation.
Defensive man-hours continue to eat into the plan. Funds invested in AI infrastructure are being diverted to legacy systems, and management is not moving forward.
Entrance, dependence, usage history, and responsibility are not connected, and even with 100 people x 4 years, the transition decision cannot be made. Many hours have been spent on maintenance surveys, and the materials for deciding when to renew, at what cost, and in what order are not available. ICT costs were ballooning, and the company continued to be unable to generate funds to invest in AI infrastructure, profit improvement, and business innovation.
4,800 man-months → 3 man-months (99.94% reduction). The 4,797 man-months saved will be used as investment capital for AI infrastructure and business innovation.
1 person + 36 AI established Operational SSOT. Create a trail of code, DOM, SQL, and operation logs, The CEO can decide the order of retention, abolition, renewal, and migration with a trail. Initial decision making is 20 times faster (2 weeks → half day), input efficiency is 1,600 times faster. The cost of defense will turn into a source of investment for growth.
Three management impacts released by legacy renewal
Concentrate research and specification into Operational SSOT, making inputs 1,600 times more efficient until renovation decisions are made. The 4,797 person-months worth of savings will be invested in the next generation.
Free up man-hours spent on maintenance and research and reallocate to AI infrastructure, profit improvement, and business model innovation. The way we use ICT budgets will fundamentally change.
By moving impact surveys closer to half-day trail reviews, we can speed up initial decisions regarding change requests, legal revisions, and product revisions. Move faster than your competition.
Six trail assets that made legacy renewal possible
Inventoryed 60,000+ classes and 50,000+ actions into units that can be considered for migration. Creates a trail of the influence range of 2,000+ highly dependent actions, and visualizes dependencies that cannot be read manually.
Operational SSOT establishes an original that can track code, tables, DOM, SQL, operation logs, and business menus with the same feature_id. The CEO can decide the order of retention, abolition, renewal, and migration with a trail.
5 steps to turn a huge legacy into an investment decision
Connect huge assets that cannot be read manually to innovation decisions that the CEO can make based on the trail.
Entrance inventory
Visualize business entry points in URL/Action units and link to responsibilities and processing.
Connect usage history and responsibility
Menu / Business mapping connects actual use and business responsibility to the same key.
Trace dependencies
Extract classes and heavily dependent actions to create a trail of the range of influence that cannot be read manually.
Consolidated into Operational SSOT
Consolidate research and specification areas into a decision base to reduce waiting time for innovation decisions.
Connecting innovation decisions to the CEO
Connect the order of retention, abolition, renewal, and transition to the CEO's decision, and return the lost man-hours to growth investment.
Since ancient times,Gods of Yaoyorozuhas nurtured people's lives and culture—the transition to AI Native meansCreating a modern management modelIt is.
“Introducing AI as a tool” and “migrating to AI Native” are fundamentally different. AI resides in various parts of management, prepares the materials for decision-making, and creates an organizational capability in which a small number of elite AI agents are brought together so that investment decisions, withdrawal decisions, and improvement decisions continue to be made on the same track.This is the essence of transitioning to AI Native. The three examples are examples of this practice.
AI resides in various parts of management and automatically prepares decision-making materials
Experience signals, market changes, legacy dependence—AI agents constantly visualize previously invisible information and continue to prepare it as material that CEOs can act on. Eliminate management stagnation due to "lack of information".
Accumulating learning fosters the organization's ability to improve KPIs
Patterns of enthusiasm, market signals, and evidence of innovation—each is fed back into the next decision and accumulated as an organization's ability to execute. Break away from dependence on outsourcing and personalization and change from within.
Working with the CEO to create a next-generation management model
From "I introduced AI, but it didn't change" to "Management is actually starting to move." The organizational ability to continue making decisions on investment continuation, withdrawal, and reallocation based on the same trail is what we are aiming for together with the CEO.
Contact
Decide where to start from the first conversation.
ICT cost reduction, decision-making speed, AI Native transition, legacy renewal—we will determine which management issues to start with to get the KPIs moving in the shortest time, and we will clarify the priorities and execution units during the first dialogue. Rather than a written proposal, we start with a situation where the CEO can decide what to stop, where to reinvest, and when KPIs will change.
We will convert a wide range of business demands and issues into optimal gears (architectures) that can be directly implemented, and build a system that combines AI and optimal architecture to drive self-propelled improvements in the future.
This requires the ability to combine deep experience as a consultant, wide knowledge as an architect, and Silicon Valley-class cloud knowledge in advanced areas, as well as the knowledge to fully utilize the current highest level of AI and an environment that can make this possible.
Personally, I have rarely met people in Japan who possess these skills, whether at a firm or a major integrator. We don't meet each other in Japan, and even overseas we rarely meet. In the first conversation, I would like to talk about the overwhelming support power that can help you organize 5,000 man-months in a few months.
Please feel free to contact us.
- digivice Inc.
AX Consulting / Digital Architecture / AI-Native Execution - CEO & Founder / Kazutaka Takarabe, AX Architect
- linkedin.com/in/kazutaka-takarabe-a4815634
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