AI Maturity Index
How effectively organizations turn AI capabilities — people, processes, platforms, data, and leadership — into business results, scored 0-100 across 45 industries, functions, regions, and business sizes.
- IndustryTechnology and Software51
- FunctionEngineering / R&D43
- RegionNorth America38
- SizeLarge Enterprise38
Most of the 28 tracked capabilities are still experimental, not operational.
Firmest foundation — 6 pts above the composite; build on it.
Caps the composite — fix first; 9 pts below the strongest pillar.
Three ways to put the index to work
The index is an external, defensible baseline for where AI actually creates value today — use it to orient your own AI strategy, not just to observe the market.
Compare your industry, function, region, or size against the global reading to see whether you are ahead of or behind the field — and by how much.
The five pillars and 28 signals expose which capabilities hold maturity back. Start where the gap is widest, not where the noise is loudest.
Use the scores and phase bands as a citable, third-party baseline to justify where AI spend and attention should go next quarter.
Every pillar, broken into its signals
Each pillar score is a holistic read across its scored sub-signals. Platforms leads at 38; Processes is the binding constraint at 29.
- Roles and org design38Experimenting
- AI champions32Experimenting
- AI literacy30Experimenting
- Change readiness and mindset29Experimenting
- Skills and training28Experimenting
- Automation maturity32Experimenting
- Process measurement30Experimenting
- Workflow documentation and standardization28Experimenting
- Handoffs and integration points27Experimenting
- Process adaptability25Experimenting
- Current tool landscape42Operationalized
- Infrastructure readiness41Operationalized
- AI/ML-specific tooling40Operationalized
- Vendor strategy38Experimenting
- Integration and interoperability35Experimenting
- Tool adoption and utilization34Experimenting
- Analytics maturity35Experimenting
- Data strategy33Experimenting
- Data accessibility30Experimenting
- Data quality28Experimenting
- Data governance27Experimenting
- Feedback loops25Experimenting
- Executive vision40Operationalized
- Investment approach38Experimenting
- Change management32Experimenting
- Risk tolerance and experimentation30Experimenting
- Measurement and accountability27Experimenting
- AI governance24Experimenting
28 signals, ranked
The granular truth beneath the five pillars: every scored sub-signal for the global reading, strongest to weakest. This is the specific capability picture the pillar averages hide.
- 01Current tool landscapePlatformsOperationalized42
- 02Infrastructure readinessPlatformsOperationalized41
- 03AI/ML-specific toolingPlatformsOperationalized40
- 04Executive visionLeadershipOperationalized40
- 05Investment approachLeadershipExperimenting38
- 06Roles and org designPeopleExperimenting38
- 07Vendor strategyPlatformsExperimenting38
- 08Analytics maturityDataExperimenting35
- 09Integration and interoperabilityPlatformsExperimenting35
- 10Tool adoption and utilizationPlatformsExperimenting34
- 11Data strategyDataExperimenting33
- 12AI championsPeopleExperimenting32
- 13Automation maturityProcessesExperimenting32
- 14Change managementLeadershipExperimenting32
- 15AI literacyPeopleExperimenting30
- 16Data accessibilityDataExperimenting30
- 17Process measurementProcessesExperimenting30
- 18Risk tolerance and experimentationLeadershipExperimenting30
- 19Change readiness and mindsetPeopleExperimenting29
- 20Data qualityDataExperimenting28
- 21Skills and trainingPeopleExperimenting28
- 22Workflow documentation and standardizationProcessesExperimenting28
- 23Data governanceDataExperimenting27
- 24Handoffs and integration pointsProcessesExperimenting27
- 25Measurement and accountabilityLeadershipExperimenting27
- 26Feedback loopsDataExperimenting25
- 27Process adaptabilityProcessesExperimenting25
- 28AI governanceLeadershipExperimenting24
Rankings and pillar breakdowns
Switch dimensions to see the full ranked ladder and a pillar-by-pillar heatmap. Sort the matrix by any pillar, and click any cell to drill into that scope's individual signals.
| Idx | ||||||
|---|---|---|---|---|---|---|
| Technology and Software | 51 | |||||
| Data Analytics and Research | 48 | |||||
| Banking and Financial Services | 46 | |||||
| Telecommunications | 46 | |||||
| Professional Services and Consulting | 40 | |||||
| Insurance | 39 | |||||
| Pharmaceuticals and Life Sciences | 39 | |||||
| Automotive and Mobility | 38 | |||||
| Media and Entertainment | 37 | |||||
| Retail and E-Commerce | 37 | |||||
| Aerospace and Defense | 36 | |||||
| Healthcare Providers and Systems | 33 | |||||
| Transportation and Logistics | 32 | |||||
| Manufacturing and Industrial Operations | 31 | |||||
| General Business and Diversified | 29 | |||||
| Energy and Utilities | 28 | |||||
| Hospitality and Travel | 25 | |||||
| Education and EdTech | 23 | |||||
| Real Estate and Construction | 22 | |||||
| Government and Public Sector | 20 | |||||
| Agriculture and Food | 18 | |||||
| Mining and Natural Resources | 18 | |||||
| Nonprofit and Social Impact | 16 |
Which capabilities are universal, and which split the field
For every signal, the range across all scored scopes: the bar runs from the lowest to the highest reading, the tick marks the median, the diamond marks the global baseline. A wide bar means the capability separates leaders from laggards.
How the world clusters
Where the 45 scored scopes fall across the five maturity phases.
The editorial read: verdict, pillar analysis, stakeholder perspectives, risks, and the opportunity layer.
See where AI is in your specific market in under a minute — enter an industry, company URL, or function.
Turn the index into a plan for your organization — know exactly where AI can create value.