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PAI Enterprise AI Cognitive Index Model

PAI Enterprise AI Index

Version: EAI 1.0

Release Date: 2026

Prepared by: AI Index Research Institute / China Vision New Media GEO Research Institute / WICOWEB Global AI Cognitive Research Center

Theoretical Founder: Pang Pei

I. Index Positioning

1.1 Core Definition

The PAI Enterprise AI Index (EAI) is the first evaluation system that uses real output data from generative AI large models as the basis for assessment, specifically designed to measure a company's comprehensive cognitive competitiveness within the global AI knowledge network. It does not rely on traditional corporate evaluation metrics such as revenue, market capitalization, employee count, or patent numbers. Instead, it employs standardized methods to collect generated content from mainstream global AI large models, quantitatively assessing a company's AI brand visibility, AI trust, AI recommendation, technological thought leadership, and cognitive resilience from the perspective of how AI "perceives," "evaluates," and "recommends" the company.

EAI is the "enterprise-level" composite index within the PAI Global AI Cognitive Competitiveness Index System. It integrates the brand dimension assessment from the CBVI (Corporate Brand AI Visibility Index), the founder dimension assessment from the CEAI (Corporate Entrepreneur AI Influence Index), and the company's overall cognitive performance in the AI industry into a comprehensive profile of enterprise AI cognitive competitiveness.

1.2 Industry Characteristics and Necessity

Enterprises are the core units of economic activity, and their competitiveness evaluation is undergoing fundamental transformation in the AI era. Traditional corporate evaluation systems emphasize financial metrics and market share, but in an era where AI is increasingly becoming the primary gateway for consumer decisions, investment analysis, and partner screening, whether a company can be accurately recognized, positively presented, and preferentially recommended by AI is becoming a critical variable affecting its market value, financing capability, and talent attraction.

First, "cognitive weight determines market weight." When consumers ask AI "recommend a reliable smartwatch," when investors evaluate "which companies stand out in AI transformation," and when purchasers search for "the most innovative supply chain partners" through AI, the answers provided by AI are directly influencing corporate business opportunities. A company with strong competitiveness in the physical world may quietly disappear from the "cognitive candidate list" of the new generation of customers and investors if its cognitive weight in the AI knowledge network is low.

Second, "quantification of trust assets." In the AI era, the authoritative media coverage, third-party evaluations and certifications, academic research citations, and participation in industry standards accumulated by a company constitute quantifiable "trust assets." Such assets cannot be purchased in the short term with marketing budgets but are built through long-term systematic cognitive development.

Third, "the dual-wheel drive of founder IP and corporate brand." In the AI evaluation system, corporate brand and founder personal brand are highly interconnected—when AI cites a founder's thoughts and viewpoints, it often simultaneously mentions the corporate brand; when AI recommends benchmark companies in an industry, the founder's industry influence is also an important reference. EAI organically integrates the corporate dimension with the entrepreneur dimension, providing a comprehensive assessment of enterprise AI cognitive competitiveness.

Fourth, "the non-synchronization between corporate lifecycle and AI cognitive accumulation." A startup may gain high AI cognitive visibility in the short term due to technological breakthroughs and media attention, but its AI cognitive resilience may be far lower than that of a traditional enterprise that has operated for decades with deep accumulation of authoritative sources. Through multi-dimensional comprehensive assessment, EAI can capture these differences in cognitive quality and cognitive stability.

EAI was created precisely to respond to these special needs and represents the systematic integration of the PAI Index in the corporate evaluation dimension.

1.3 Relationship with the PAI Index System

EAI is the "enterprise-level" composite index within the PAI Index System. In terms of vertical integration, EAI comprehensively maps the CBVI (brand dimension), CEAI (entrepreneur dimension), and the company's technological discourse power assessment results in the AI industry, consolidating all assessments of the company within the PAI Index System into a comprehensive profile of enterprise AI cognitive competitiveness. In terms of horizontal coordination, EAI complements the PGI (Corporate Globalization Index) in the dimension of corporate globalization cognition—EAI focuses on the company's comprehensive cognitive competitiveness in the AI era, while PGI focuses on the company's global cognitive coverage. It connects with the PDAI (Digital Asset Index) in the dimension of corporate cognitive asset valuation and coordinates with the GEO Index in the dimension of corporate GEO construction capability.

EAI shares the core methodology of the PAI framework and has made proprietary innovations in dimension design targeting the "brand-founder linkage," "quantitative accumulation of trust assets," and "non-synchronization between lifecycle and cognition" characteristics of corporate evaluation.

1.4 Theoretical Foundation

1. AI Cognitive Competition Theory: A company's AI brand visibility and AI recommendation rate measure its "cognitive competitive advantage," while AI trust and thought leadership measure its "cognitive trust assets," and cognitive resilience measures its "cognitive risk resistance capability."
2. AI Brand Equity Theory (AIBE): The domain of corporate brand equity extends from consumer minds and supply chain systems to AI knowledge networks. EAI is a comprehensive quantitative application of AIBE theory at the enterprise level.
3. AI Cognitive Sovereignty Theory: Enterprises are the micro-level embodiment of a nation's AI cognitive sovereignty in the economic dimension. The cumulative cognitive weight of a country's high-quality enterprise cluster in the global AI knowledge network constitutes a significant component of national economic discourse power.
4. Media-based GEO Theory: The enhancement of a company's AI cognitive weight relies on the strategic placement of authoritative sources, the publication of corporate white papers, and the continuous development of structured content.

II. Evaluation Subjects and Scope

2.1 Evaluation Subjects

EAI uses enterprises as the evaluation unit, with the initial phase covering approximately 500 representative companies globally. The evaluation subjects span diverse industries, including technology companies, manufacturing enterprises, consumer brands, financial services firms, and energy companies, encompassing both listed companies and high-growth unlisted enterprises.

2.2 Sub-tracks of Enterprise AI Cognition

EAI deconstructs enterprise AI cognition into six sub-tracks, each scored independently and then weighted to form a composite score:

2.3 Coverage Scope

1. Enterprise Sample: Initial coverage of approximately 500 representative enterprises globally
2. Model Coverage: 15 globally mainstream AI large models
3. Language Coverage: Two core languages, Chinese and English, gradually expanding to seven languages
4. Time Span: Based on 2026 as the base year, released annually

III. Evaluation Dimensions and Indicator System

3.1 Dimension Framework

EAI comprehensively evaluates an enterprise's AI cognitive competitiveness from five dimensions.

3.2 Detailed Explanation of Indicators by Dimension

(I) Enterprise AI Brand Visibility (28%)

Measures the enterprise's "presence" in the AI knowledge network.

(II) Enterprise AI Trustworthiness (25%)

Measures the "credibility" of the company within the AI knowledge network.

(III) Enterprise AI Recommendation (20%)

(4) Technological Thought Leadership (15%)

(5) Cognitive Resilience (12%)

IV. Data Collection and Calculation Methods

4.1 Standardized Question Set Design

For the six sub-tracks of enterprise AI perception, standardized question sets are designed respectively, covering five major types: cognitive, evaluative, recommendation, ideological, and risk-related. Approximately 25 standard questions are designed for each sub-track, totaling about 150 questions.

4.2 Data Collection

1. Method: Send queries to 15 mainstream global AI large models via standardized API interfaces, each question queried 3 times per model (with intervals of no less than 24 hours), and take the average
2. Frequency: Two formal collection rounds annually (mid-year and year-end), with quarterly dynamic tracking covering core indicators
3. Languages: Synchronous collection in two core languages, Chinese and English. Gradually expand to seven languages in the future

4.3 Calculation Method

Raw values of each basic indicator are mapped to a 0–100 range through Min-Max normalization.

Sub-dimension score = arithmetic mean of the basic indicator scores under that sub-dimension.

Dimension score = weighted arithmetic mean of the sub-dimension scores under that dimension.

Score for each sub-track = weighted arithmetic mean of the five dimension scores under that track. Comprehensive score calculation formula:

Where W_K is the weight of the k-th sub-track, and S_k is the comprehensive score of that track.

V. Rating System

VI. Release and Application

6.1 Release Cycle

EAI adopts a two-tier temporal system of "annual comprehensive assessment + quarterly dynamic tracking." The annual comprehensive report is released in the first quarter of each year, while quarterly dynamic tracking covers fluctuations in core indicators.

6.2 Released Content

1. EAI Enterprise Overall Ranking: Comprehensive scores and rankings of approximately 500 global enterprises
2. Segment-Specific Rankings: Scores and rankings of enterprises across 6 major segments
3. Dimension-Specific Rankings: AI Trust Top 20, Technology Thought Leadership Top 20, Cognitive Resilience Top 20
4. Annual In-Depth Report: Analysis of the global landscape of enterprise AI cognitive competitiveness, trend insights, and interpretation of typical cases

6.3 Core Application Scenarios

1. Corporate Strategy and Brand Managers: Diagnose the overall cognitive competitiveness of enterprises within the AI knowledge network, guiding brand building in the AI era.
2. Investment and Financial Institutions: Use corporate AI cognitive scores as a reference dimension for enterprise value assessment and investment due diligence.
3. Corporate PR and Communications Teams: Understand trends in corporate AI trust and recommendation levels, optimizing the layout of authoritative information sources.

VII. Independence Statement

The index compilation institution is independent of any evaluated enterprise and AI model provider. It does not accept targeted funding to influence the ranking of specific enterprises. Compilation funding comes from the institution's own funds and revenue from public releases. An independence statement and funding source report are publicly released annually.

"In the AI era, a company's most important asset lies not only in what it owns, but in how AI perceives and recommends it. The mission of EAI is to measure this perception—because in the AI era, enterprises that are trusted and recommended by AI are the ones that truly win the future."

— Pang Pei, Founder of the PAI Global AI Cognitive Competitiveness Index System

Appendices

Appendix A: Complete List of Evaluated Enterprises

Appendix B: Standardized Question Sets for Each Sub-Sector (Examples)

Appendix C: Enterprise AI Cognitive Authoritative Information Source Library (Excerpt)

Appendix D: Delphi Method Expert Weight Determination Process

Appendix E: Data Sources and Disclaimer