A New Management Framework for Competitive AdvantageToken-Centric Management in the Age of AI Agents

Illustration of numerous data tokens and an AI value creation framework representing Token-Centric Management.

Insight | 2026-9-4

8 minute read

What is Token-Centric Management in the age of AI agents? This article explains how organizations can treat tokens as a strategic resource and convert AI investment into business value and competitive advantage. Drawing on a three-layer enterprise AI framework, key KPIs, and leading industry examples, it outlines the shift from Token-Maxxing to Business Value Maxxing and provides practical recommendations for business leaders.

1. What Changes in the Age of AI Agents?
When Tokens Become a New Strategic Resource

Generative AI has rapidly become part of everyday business by enabling anyone to access advanced AI capabilities through natural language, without requiring specialized expertise or programming skills. Today, that evolution is accelerating. AI is moving beyond conversational assistants toward AI agents capable of autonomous perception, reasoning, planning, and execution. In doing so, AI is evolving from a productivity tool into an active partner in enterprise operations.

As a result, enterprise AI is entering a new phase. Organizations are shifting from experimental deployments to large-scale implementation, making AI an integral part of daily business operations. Because AI agents continuously reason, invoke external tools, and collaborate with other systems, enterprises are increasingly operating on a foundation of massive token(1) consumption.

Traditionally, companies have managed people, capital, and physical assets as their primary business resources. In the AI agent era, tokens emerging as a new strategic resource alongside them. Competitive advantage will no longer be determined simply by whether an organization adopts AI, but by how effectively it allocates tokens and converts them into business value.

Leading organizations are already beginning to shift their focus from Token-Maxxing—maximizing AI adoption through extensive token usage—to Value-Maxxing, where the objective is to maximize business outcomes per token consumed. The key challenge is no longer consuming more tokens, but generating greater value from every token invested.

Against this backdrop, this paper redefines tokens as a strategic management resource for the AI era and introduces the concept of Token-Centric Management—a management framework for designing, allocating, measuring, and optimizing the production, consumption, and value creation of tokens. It then examines the emerging practices of leading organizations and presents strategic recommendations for business leaders seeking to accelerate enterprise AI while maximizing business value.

2. How Should Enterprises Design Management for the AI Era?
The Three-Layer Enterprise AI Framework and Token-Centric Management

Generative AI and AI agents are typically accessed through natural-language prompts. Behind this seemingly simple interaction, however, every prompt is converted into input tokens, processed by AI models and the underlying computing infrastructure, and ultimately returned to users as output tokens. Today, AI service providers charge enterprises primarily based on token consumption, making tokens the most practical and measurable unit of AI utilization.

From a management perspective, this process can be understood as a three-layer value creation framework in which tokens serve as the connecting mechanism across all layers (Figure 1).

Our previous insight paper examined the structural transformation of the semiconductor industry from a technology perspective, tracing how semiconductors generate computing power, which in turn produces tokens that ultimately enable business competitiveness. This paper approaches the same value chain from the opposite direction, starting with business value and examining how enterprises consume tokens to create value, supported by the infrastructure that produces them.

Accordingly, Figure 1 illustrates three interconnected management challenges. Layer 1 (Token Factory) focuses on producing tokens efficiently and reliably. Layer 2 (Token Consumption) addresses how tokens should be allocated and utilized across business activities. Layer 3 (Business Value) concentrates on converting token consumption into measurable business outcomes. Together, these three layers provide an integrated framework for managing enterprise AI.

Three-layer framework linking token production, consumption, and business value

Figure 1. A Three Layer Framework for AI Value Creation and the Role of Tokens

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The AI Era Is Driving Explosive Growth in Token Consumption

For the past several years, enterprise AI adoption has largely been measured by usage. Organizations encouraged Token-Maxxing—maximizing AI utilization—to accelerate experimentation and organizational adoption, supported by falling token prices and relatively limited enterprise demand.
That environment is now changing rapidly.

As AI adoption expands across organizations and AI agents become embedded in daily workflows, token consumption is growing exponentially. OpenRouter, for example, increased its annual token throughput from roughly 100 trillion tokens in May 2025 to approximately 1,500 trillion tokens in May 2026 (2) — a fifteen-fold increase within a single year. Industry forecasts further suggest that global monthly token consumption could grow from approximately 5,600 trillion tokens in 2026 to more than 120 quadrillion tokens by 2030 (3), representing over twentyfold growth in only four years.

Part of this acceleration reflects the Jevons Paradox(4). Although advances in AI models and hardware continue to reduce the computing required to process each individual token, overall demand for tokens is expanding even faster. Every token ultimately requires computing resources. As token demand increases exponentially, total computing requirements—and consequently computing costs—continue to rise despite ongoing improvements in efficiency.

Reasoning models and AI agents further reinforce this trend(5). Unlike traditional LLMs, reasoning models perform extended internal reasoning before producing outputs, consuming substantially more computation per request. AI agents amplify this effect by autonomously executing multi-step workflows involving repeated reasoning, tool invocation, memory retrieval, and iterative decision-making. Recent empirical studies show that agentic coding tasks consume orders of magnitude more tokens than conventional code-generation or code-chat tasks, illustrating how enterprise AI workloads are becoming significantly more compute-intensive.

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From Token Maximization to Value Maximization

This structural shift is changing the economics of enterprise AI.

Rapid growth in token consumption can significantly increase operating expenses and, in some cases, generate unexpectedly large AI service bills(6). Consequently, organizations are beginning to experience a widening gap between the objectives of technology leaders—who focus on innovation and rapid AI adoption—and those of finance leaders, who must ensure sustainable returns on AI investments.

In the AI agent era, success can no longer be measured simply by increasing AI usage. Instead, enterprises must maximize business value per token.

This is the central idea behind Token-Centric Management — a management framework that integrates token production, token consumption, and business value creation into a single system. Organizations do not invest in GPUs for their own sake, nor is producing or consuming tokens the ultimate objective. The goal is to create measurable business value through AI applications and AI agents.

For finance leaders, AI invoices have already made AI costs visible. The next challenge is making AI-generated business value equally measurable. In other words, the AI era requires not only a balance sheet for computing resources, but also a profit-and-loss perspective for token utilization.

Table 1 proposes a practical management framework for governing the three layers of enterprise AI. Each layer includes representative KPIs and management priorities that clarify what should be optimized—from efficient token production to effective token allocation, to measurable business outcomes. When integrated into a unified management dashboard, these indicators provide the operational foundation for eliminating unnecessary costs while maximizing enterprise value. The coordinated optimization of all three layers forms the cornerstone of Token-Centric Management.

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Key KPIs and management priorities across the three layers of Token-Centric Management

Table 1. Key KPIs for Token-Centric Management

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Beyond Technology and FinOps

Technology optimization and FinOps alone are insufficient to realize the full value of enterprise AI. Even the most sophisticated dashboards cannot create business value without organizational capabilities that translate AI into operational outcomes.

To make the three-layer framework work in practice, enterprises must complement technology and financial management with organizational transformation. Four management priorities are particularly important:

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Transform organizational structures by establishing cross-functional teams that connect infrastructure, application development, business functions, and finance, while extending traditional FinOps toward AI governance.
Develop role-specific AI capabilities, recognizing that employees working across different layers require fundamentally different skills rather than uniform AI training.
Incorporate token productivity into performance management, including metrics such as token efficiency, token budgets, and transparent governance linking AI consumption with business performance.
Align technology, finance, and business leadership through shared KPIs that optimize business value rather than technology utilization alone.

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By integrating organizational transformation with technology and financial management, Token-Centric Management evolves beyond an operational discipline into a comprehensive management framework for the AI era.

3. How Far Has Token-Centric Management Progressed?
Current Practice and Emerging Opportunities from Early Adopters

In Chapter 2, we introduced Token-Centric Management as a new framework for managing enterprise AI in the AI Agent era. In practice, however, organizations differ significantly in the maturity of their AI adoption. Reaching Business Value Maxxing is rarely an immediate step. Most organizations begin by improving the efficiency of AI utilization. This section examines how early adopters are putting these ideas into practice and what this reveals about the current state—and future direction—of Token-Centric Management.

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(1) From Token Maximization to Model Optimization
The Current State of Model-Maxxing

Across the United States, enterprise AI is undergoing a noticeable shift from Token-Maxxing—maximizing AI usage—to Model-Maxxing—optimizing model selection and deployment(7). During the early stage of generative AI adoption, organizations focused primarily on expanding AI usage across the enterprise. However, the rapid growth of AI agents and reasoning models has dramatically increased token consumption, forcing companies to reassess AI budgets and operational efficiency.

As a result, a new generation of AI service providers has emerged around a simple objective: achieving the same or greater business outcomes with fewer tokens. This structural shift has attracted substantial investment into startups specializing in AI routing, model optimization, and AI cost management.

These services typically provide three core capabilities:

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AI routing, automatically directing each task to the model that offers the best balance of performance, speed, and cost.
Token management, monitoring token consumption and spending in real time to improve visibility and prevent unnecessary costs.
Model and computing optimization, including enterprise-specific fine-tuning and optimized inference infrastructure to reduce both token costs and computing costs.

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Industry evidence suggests that model routing alone can reduce token consumption by 35% to 97%, depending on the workload and model combination, demonstrating substantial improvements in cost efficiency(8).

Table 2 compares two representative business models. OpenRouter and Concentrate AI focus primarily on AI routing, enabling organizations to select and utilize existing AI models more efficiently. Fireworks AI extends this approach by integrating enterprise model optimization with computing optimization, expanding optimization beyond token usage to the underlying inference infrastructure.

These developments demonstrate that competitive advantage is no longer defined by using more AI, but by creating greater business outcomes with fewer tokens. Model-Maxxing should therefore be viewed as the first practical stage of Token-Centric Management. As enterprise AI continues to mature, the focus will naturally evolve from optimizing models to maximizing business value through enterprise-wide Token-Centric Management.

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Comparison of AI routing providers and model optimization platforms supporting Model-Maxxing

Table 2. Evolution of AI Services - Business Models Supporting Model-Maxxing

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(2) From Model Maxxing to Business Value Maxxing
Putting Token-Centric Management into Practice

Today, many organizations have begun optimizing token consumption through model routing and reducing the cost of AI usage. However, as discussed in Chapter 2, enterprise AI creates value through a three-layer architecture: Token Production (Layer 1), Token Consumption (Layer 2), and Business Value Creation (Layer 3). The essence of Token-Centric Management is therefore not simply minimizing AI costs, but managing these three layers as an integrated system to maximize business value.

This perspective is also beginning to gain traction across the industry. At FinOps X 2026, held in June 2026, the launch of the Tokenomics Foundation was announced. The initiative aims to establish AI tokens as the fundamental unit for measuring spending on intelligence, while developing open standards and benchmarks for AI usage, consumption data, and cost management. This represents an important shift: token management is evolving from a purely technical concern into a strategic management discipline.

Table 3 summarizes representative early-stage implementations of Token-Centric Management based on the three-layer enterprise AI architecture introduced in Chapter 2. Together, these case studies illustrate how organizations are moving beyond cost optimization toward Business Value Maxxing, demonstrating the current state of Token-Centric Management in practice.

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Six Token-Centric Management cases across business value, consumption, and token production

Table 3. Representative Practices of Token-Centric Management Across the Three-Layer Enterprise AI Framework

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What Table 3 Reveals About the Current State of Token Management

The six cases in Table 3 reveal a common shift: AI competitiveness is moving beyond model or GPU performance toward a management capability—how efficiently tokens are produced, optimally allocated, and ultimately converted into business value. Leading companies are first improving infrastructure and inference efficiency to enhance token production, then optimizing consumption through model routing and token management, and increasingly linking these efforts to AI governance and ROI management(9).

In other words, the focus is beginning to shift from Token-Maxxing—how much AI is used—to Business Value Maxxing—how much business value can be created from limited token resources. Token management is a management framework for designing and managing these three layers as an integrated system, and is emerging as a new organizational capability for building competitive advantage in the AI era.

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4. Where Should Companies Begin?
Three Recommendations for Adopting Token-Centric Management

As discussed throughout this paper, enterprise AI is evolving from organization-wide deployment of conversational AI tools toward enterprise-wide business transformation powered by AI agents. This transition brings not only higher productivity but also greater AI investment and operating costs. Consequently, the key management question is no longer whether AI has been adopted, but how AI investment can be translated into measurable business value. Leading organizations are already moving beyond Token-Maxxing, through Model-Maxxing, and toward Business Value Maxxing—maximizing business outcomes from every token consumed.

What, then, should organizations do next? This paper proposes three recommendations for implementing Token-Centric Management.

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(1) Establish a Token-Centric Management Framework

Tokens are not only the fundamental unit of AI computation but also the smallest measurable unit of AI spending. The first step is therefore to recognize tokens as a visible and manageable business resource.

As AI agents become widely deployed, many organizations naturally focus on reducing token consumption in response to rising AI costs. However, the objective of Token-Centric Management is not cost reduction alone. Its purpose is to maximize the value created from every token consumed by optimizing both efficiency and business outcomes.

This paper proposes a management framework built on three interconnected layers—Token Production, Token Consumption, and Business Value Creation—supported by a set of key performance indicators (KPIs) for managing each layer.

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(2) Manage the Three Layers as an Integrated System

Today, AI infrastructure, applications, business units, finance teams, and external AI service providers are often optimized independently. Such fragmented optimization, however, cannot deliver Business Value Maxxing.

Organizations should instead manage the three layers as one integrated management system. The starting point should not be technology, but business objectives. Desired business outcomes and KPIs should drive decisions on AI models, applications, token allocation, and computing infrastructure. In other words, enterprise AI should be managed from a strategic business perspective rather than a technology-first perspective.

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(3) Transform the Organization and Build a Culture that Supports Token-Centric Management

Token-Centric Management is not achieved through technology alone. As AI agents become embedded in enterprise operations, they will reshape business processes, organizational structures, and employee roles.

Organizations must evolve from traditional functional structures toward end-to-end, AI-enabled operating models that maximize collaboration between people and AI. At the same time, continuous reskilling and upskilling should be accompanied by a culture in which employees embrace change, adapt proactively, and continuously improve the way AI creates value.

In the AI era, sustainable competitive advantage will not belong to organizations that simply deploy the most advanced AI models, but to those that convert tokens into business value most effectively. Token-Centric Management provides the management architecture for achieving this goal and offers a practical framework for building long-term competitive advantage in the age of AI.

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(1) A token is a fundamental discrete unit through which AI models process and generate information—that is, a distinct minimum unit into which information is segmented. Tokenization is the process of converting input information into a sequence of tokens, and the tokenization method and vocabulary design can affect the number of tokens required, computational workload, memory usage, and inference costs.
(2) Deedy Das, Matt Murphy(May 26, 2026)“OpenRouter Now Processes More Than a Quadrillion Tokens a Year”
(3) Goldman Sachs(May 20, 2026) “AI Agents Forecast to Boost Tech Cash Flow as Usage Soars”
(4) Longju Bai et al. (29 Apr 2026) “How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks”
(5) Tuan Anh Do(June 15, 2026)“The Jevons Paradox in AI Infrastructure: Efficiency, Rebound, and the Limits of Cost Reduction”
(6) Madison Mills(May 28, 2026)“AI sticker shock hits corporate America”
(7) Aditi Bharade and Henry Chandonnet(July 4, 2026)“Tokenmaxxing is so over. It's all about modelmaxxing now.”
(8) Finance BigGo(July 6, 2026)“"Model Routers" Emerge as Enterprise Cost-Cutting Weapon: Palantir Slashes Inference Costs by 97%, Capital Floods In”

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(9) Fujitsu has developed AI agents not as standalone automation tools, but as a framework that learns and improves through business operations and extends those insights to other use cases. The approach aims to maximize the efficiency and value of token usage through agent routing and optimization.

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Dr. Jianmin Jin (Ph.D in International Economic Law)
Chief Digital Economist Fujitsu Ltd.
Senior Director
Marketing Promotion Office

2020 Fujitsu Ltd., Chief Digital Economist. 1998 Fujitsu Research Institute, Senior Fellow.
Dr. Jin's research mainly focuses on global economic, digital innovation/digital transformation, and Dr. Jin has published books such as ”Towards the Creation of a Japan’s Silicon Valley”(2020), etc.

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A New Management Framework for Competitive AdvantageToken-Centric Management in the Age of AI Agents

1. What Changes in the Age of AI Agents?When Tokens Become a New Strategic Resource

2. How Should Enterprises Design Management for the AI Era?The Three-Layer Enterprise AI Framework and Token-Centric Management

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3. How Far Has Token-Centric Management Progressed?Current Practice and Emerging Opportunities from Early Adopters

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4. Where Should Companies Begin?Three Recommendations for Adopting Token-Centric Management

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Related Information

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Related Information

1. What Changes in the Age of AI Agents?
When Tokens Become a New Strategic Resource

2. How Should Enterprises Design Management for the AI Era?
The Three-Layer Enterprise AI Framework and Token-Centric Management

3. How Far Has Token-Centric Management Progressed?
Current Practice and Emerging Opportunities from Early Adopters

4. Where Should Companies Begin?
Three Recommendations for Adopting Token-Centric Management