How We Score Enterprise AI Adoption
Scoring logic, source list, confidence framework, and update cadence. Transparency is a core product requirement.
Scoring Logic
The AI Execution Score synthesizes enterprise AI adoption data across six weighted dimensions. Each dimension is sourced from multiple independent primary research studies conducted with enterprise-scale organizations (200+ employees). We do not conduct original surveys: we synthesize and validate existing research.
Metrics are expressed in the most common form from primary sources (percentages, ratios, dollar values). When sources disagree, we report the range and cite both. We do not cherry-pick favorable numbers. Our editorial standard: the metric must tell a story that is operationally useful to a C-suite leader, not just statistically interesting.
Adoption Rate
Percentage of enterprises with AI deployed in at least one production function. Primary sources: McKinsey Global AI Survey, Gartner.
Pilot-to-Production Gap
Percentage of enterprises that have moved AI agent pilots to production scale. This dimension captures execution quality, not intent. Primary source: Digital Applied (n=650).
Governance Maturity
Percentage of enterprises with a formal AI governance framework (not just a usage policy). Sources: Knostic/Pacific AI, AuditBoard, Gartner.
Workforce Readiness
Training investment effectiveness, measured by ROI differential between organizations with and without structured AI programs. Sources: BCG, DataCamp/CFO Dive.
ROI Achievement
Percentage of enterprises reporting positive AI ROI at different thresholds (any / significant / substantial). Sources: BCG (n=1,250), Gartner.
Investment Trajectory
Global enterprise AI spending growth rate and direction. Sources: IDC, Gartner.
Confidence Framework
Every metric displayed on AI for Orgs carries a confidence indicator. This reflects source quality, sample size, corroboration across studies, and recency.
Metric corroborated by 3+ independent primary sources with sample sizes of 500+. Direction and magnitude consistent across sources.
Metric supported by 2 independent sources or 1 high-quality source (sample size 1,000+). Minor discrepancies between sources noted.
Metric from a single source, smaller sample size, or significant variance across sources. Treat as directional only.
Update Cadence
Quarterly
Full dimension review and score updates. Conducted after major research releases from McKinsey, BCG, Gartner, Deloitte, and IDC.
As-needed
Regulatory tracker updated when significant legislative events occur: new enforcement dates, court decisions, or major EO/legislation.
Weekly
C-Suite Brief subscribers receive weekly data highlights and trend analysis outside the full quarterly update.
Data Collection
AI for Orgs does not conduct original research. All data is sourced from publicly available primary research published by independent analyst firms, academic institutions, and industry associations. We do not accept data from vendors about their own products.
Selection criteria for included studies: (1) publicly available methodology, (2) minimum sample size of 200 enterprise respondents, (3) respondent base must be enterprise organizations (not individual employees), (4) published within the last 24 months, (5) no conflict of interest from the publisher's core business.
When a study does not meet all five criteria, it may still be cited as a secondary source with appropriate caveats. All primary data sources are listed below with the date of most recent use.
Primary Sources
All data cited on AI for Orgs is drawn from the following primary research sources.