AI Impact Research Hub
Understanding AI's corporate impact through publicly available data
What This Research Covers
We analyze public information to understand how companies are adopting AI—from job postings and earnings calls to product documentation and partnership announcements. Our research provides objective, data-driven insights without relying on proprietary access or surveys.
Research Approach
Our methodology draws exclusively from publicly available sources, allowing for transparent and reproducible analysis. We track signals across three dimensions: what companies are building through product initiatives, who they are hiring through talent investments, and what tools they are deploying across the enterprise. This structure is designed to capture practical AI adoption rather than self-reported intent.
This approach aligns with established third-party corporate research practices. Firms such as CB Insights, PitchBook, and Gartner similarly rely on public filings, press releases, and job data to assess corporate strategy and market trends.
Data Sources
All analysis is derived from publicly available corporate information. Sources are continuously monitored and incorporated as new information becomes available.
Financial disclosures
Earnings calls, SEC filings, and investor presentations
Corporate communications
Press releases and partnership announcements
Hiring data
Job postings across major platforms
Product documentation
Product documentation and release notes
APIs and integrations
Public API and integration documentation
Research Areas
The AI Impact Research Hub is organized around three primary research areas, each designed to capture a different dimension of corporate AI adoption. All datasets are built using the same public-data methodology and are updated on a regular cadence.
AI Adoption Tracker
The AI Adoption Tracker measures AI maturity across more than 567 companies using a multi-dimensional scoring framework. Four dimensions are rated on a five-point scale based on evidence drawn from public sources. The dataset spans 12 industries, including technology, financial services, healthcare, and industrials, and is refreshed every two to three weeks.
AI Hiring Leaders
The AI Hiring Leaders dataset analyzes more than 43,000 job postings across 428 companies to assess how aggressively organizations are investing in AI talent. An AI Hiring Score, ranging from 0 to 100, is calculated using weighted signals that account for overall AI mention rate, adoption beyond technical roles, functional breadth, and data quality confidence.
AI Apps Tracker
The AI Apps Tracker is a curated database of 301 enterprise AI tools verified through public sources. Each tool is evaluated using security and compliance signals, traction indicators, and customer evidence. Tools are categorized by functional use cases such as coding, analytics, automation, and creative workflows, as well as by industry vertical and data risk profile.
Why Public Data Research Matters
Third-party research based on public data has become essential for understanding corporate AI strategy. Unlike vendor-sponsored reports or survey-based research, public-data analysis avoids response bias and vendor influence, allows methodologies and sources to be independently verified, and can be updated continuously as new information emerges. This breadth and transparency are particularly important in AI research, where stated adoption often diverges from observable implementation.