AIDE Index Methodology
AIDE Index Methodology
The AIDE Index measures what companies and their leaders demonstrably do with respect to AI, using exclusively public data and structured AI Research Agents rather than self-reported inputs.
"The question for senior leaders isn't whether to invest in AI, it's where and how fast. Research like this, grounded in observable data across rather than self-reported surveys, gives executives a credible starting point for those decisions."
Ned Calder, Managing Director, Strategy and Innovation, Innosight
Why a New Measurement Standard
The rapid integration of artificial intelligence into corporate strategy and operations has created an urgent need for objective, comparable measurement of enterprise AI maturity. While numerous AI readiness frameworks exist, most rely on self-reported surveys, proprietary questionnaires, or qualitative expert assessments, approaches that introduce subjectivity, limit comparability, and depend on voluntary disclosure by the assessed organizations.
Self-reported measurement carries well-documented distortions: organizations tend to overstate readiness when results are visible to investors or partners, and understate it when disclosure feels risky. AI assessments compound this problem, because the domain moves faster than most respondents' internal vocabulary, definitions of deployment vary widely, and the line between piloting and production use is rarely clear. The result is data that reflects organizational narrative as much as operational reality.
The AIDE Index addresses this gap. Developed by the AIDE Institute, it evaluates the degree to which organizations visibly integrate artificial intelligence across leadership commitment and operational deployment. The index measures observable adoption behavior, not AI impact: it captures what companies and their leaders demonstrably do with respect to AI (hiring, patenting, communicating, deploying), not whether those activities translate into financial returns, productivity gains, or competitive advantage.
What AIDE Stands For
The acronym AIDE (pronounced A-I-D-E) stands for AI-Driven Enterprise, reflecting the index's focus on measuring not merely AI awareness or experimentation, but the degree to which artificial intelligence is embedded into an organization's leadership posture, strategic priorities, and tangible operational capabilities across functional areas.
It's important to note that AI-driven enterprises are not exclusively AI companies or tech companies. Rather, any organization in any sector that has adopted AI.
The Four Pillars
Each pillar isolates a distinct, observable signal of enterprise AI posture.
Literacy
What Leaders Know
Advocacy
What Leaders Say
Orientation
What the Org Prioritizes
Implementation
What the Org Builds
Four Guiding Principles
The AIDE Index methodology is governed by four foundational commitments.
Independent Sources, Public Data
All inputs derive from publicly available signals: patent databases, annual reports, earnings call transcripts, job postings, corporate sites, LinkedIn, and AI Research Agent queries across news, SEC filings, industry publications, and case studies. No data point requires voluntary disclosure from the assessed company.
Comparability Across the Cohort
Every S&P 500 company is assessed with identical collection protocols, scoring rubrics, and normalization procedures. Cohort-based min-max normalization ensures scores reflect relative positioning, enabling meaningful cross-company and cross-sector comparisons.
Multi-Dimensional Measurement
AI maturity is not a single attribute. The AIDE framework separates what leaders know and communicate (Leadership) from what the organization prioritizes and builds (Company), revealing alignment, or misalignment, between strategic intent and operational reality.
Transparency and Auditability
Every score can be traced from the final AIDE Index back through normalized scores, raw dimension scores, channel-level signals, and individual data points. Multi-LLM cross-validation and human-in-the-loop review provide the audit mechanism for non-deterministic components.
Where We Pull From
Our intelligence sources span the entire public digital footprint of global enterprises.
AIDE Index
Literacy
Dimension
Board Members and Top Management Experiences, skills, formation related to AI
Data Sources
AIDE Index
Advocacy
Dimension
Board Members and Top Management message on AI
Data Sources
AIDE Index
Orientation
Dimension
Company Strategy, Partnerships & Acquisitions, Workforce, Organization Structure
Data Sources
AIDE Index
Implementation
Dimension
AI product features, AI in operations, and AI deployment evidences
Data Sources
AIDE Index
Literacy
AIDE Index
Advocacy
AIDE Index
Orientation
AIDE Index
Implementation
Dimension
Board Members and Top Management Experiences, skills, formation related to AI
Board Members and Top Management message on AI
Company Strategy, Partnerships & Acquisitions, Workforce, Organization Structure
AI product features, AI in operations, and AI deployment evidences
Data Sources
The Outside‑In View
Our foundational index is powered exclusively by Open Source Intelligence (OSINT). By scanning the public web, we provide an objective, standardized benchmark of corporate AI maturity. However, public signaling is only one piece of the puzzle. To capture your proprietary workflows, internal talent density, and private AI initiatives, we seamlessly integrate your first‑party data.
What the Full Methodology Covers
The complete methodology document describes:
- 01
The conceptual framework underpinning the AIDE Index, including its dimensions and pillars.
- 02
Definitions, data sources, and scoring logic for each of the four pillars: Literacy, Advocacy, Orientation, and Implementation.
- 03
The data collection approach, distinguishing between automated OSINT pipelines and AI Research Agents.
- 04
The normalization methodology used to produce comparable scores across the S&P 500 cohort.
- 05
The analytical frameworks derived from AIDE scores, including the AI-Driven Enterprise Matrix.
- 06
The rules governing executive handling, cross-company propagation, and data quality management.
- 07
Known limitations and the mitigations implemented to address them.
Download the Complete Methodology
Get the full technical methodology, including scoring rubrics, normalization procedures, the AI-Driven Enterprise Matrix, and known limitations.
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At a Glance
The Methodology in Five Phases
A visual walkthrough of how raw signals become a comparable AIDE score.
The Global Ingestion Engine
We ingest hundreds of millions of public data points from regulatory filings, executive social media, job markets, patent databases, and earnings calls — all without requiring any proprietary access.
10-K filings, proxy statements, and annual reports from 500+ global companies. We extract every mention of AI strategy, investment, risk factors, and governance language.
2,100+ filings analyzed annually
From ingestion of public signals to scoring, classification, and alerting — each phase is documented in detail in the full methodology report above.
FAQ