Paloren compiles public AI statistics from cited sources such as McKinsey, Stanford HAI, WEF, NIST and OECD.
Paloren compiles AI statistics from cited public sources including McKinsey, Stanford HAI, World Economic Forum, NIST and OECD. Each row links to the original source and explains what the figure means.
For researchers and teams needing cited AI statistics.
The most useful AI statistics come from cited public sources such as McKinsey State of AI, Stanford AI Index, WEF Future of Jobs, OECD.AI and NIST. Each figure should be read with its population, period and definition.
Figures from McKinsey, Stanford HAI, WEF, OECD and NIST.
Keep these in view
McKinsey State of AI
Stanford AI Index
WEF Future of Jobs
Check the population, period, definition and source.
Keep these in view
Population and period
Definition used
Original citation
| Source | What it covers | Link |
|---|---|---|
| McKinsey State of AI | Enterprise AI use, scaling, agents and financial impact | https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai |
| Stanford AI Index | AI research, technology, economy and policy data | https://aiindex.stanford.edu/report/ |
| WEF Future of Jobs | Employer expectations about jobs and skills | https://www.weforum.org/publications/the-future-of-jobs-report-2025/ |
| OECD.AI | Public AI policies and initiatives from jurisdictions and organisations | https://oecd.ai/en/dashboards |
| NIST AI RMF | Voluntary AI risk-management framework and resources | https://www.nist.gov/itl/ai-risk-management-framework |
Use the original page to confirm method and population.
Source: Public source pages linked below.
Paloren organises widely cited AI statistics from McKinsey, Stanford HAI, the World Economic Forum, NIST and OECD.AI, keeping each figure tied to its original report.
| Stage | What changes |
|---|---|
| Signal | List the recurring figures teams request most: McKinsey adoption rates, Stanford HAI AI Index trends, WEF Future of Jobs employer expectations and OECD.AI policy counts. |
| Synthesis | Build a citation table recording source, publication period, surveyed population and definition for each statistic, so secondary paraphrases never replace the original report. |
| System | Link every row to the original publisher page, label whether the figure covers enterprises, respondents or countries, and flag figures that lack a stated population. |
| Scale | Assign a researcher to refresh rows when McKinsey or Stanford HAI publish new editions, tracking citation currency and the share of rows with verified primary links. |
Suppose a research team needs an adoption figure for a briefing. Signal logs the McKinsey enterprise survey and the Stanford HAI AI Index edition it came from. Synthesis records that the statistic covers surveyed large enterprises in one period, not all businesses.
System links the row to the publisher's original page and marks the population. Scale checks the row when the next survey appears, replacing the outdated figure while keeping the older citation archived for comparison.
Written teaching example with hypothetical inputs, not a client result.
Operating metric: Share of statistic rows with a verified primary-source link.
What you take forward
Plan evidence-led AI
Tell Paloren the workflow and the evidence you need.
Use the original publication.
Confirm population and period.
Do not apply one segment to all businesses.
Track changes from the same source.
| Source | What it covers | Link |
|---|---|---|
| McKinsey State of AI | Enterprise AI use, scaling, agents and financial impact | https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai |
| Stanford AI Index | AI research, technology, economy and policy data | https://aiindex.stanford.edu/report/ |
| WEF Future of Jobs | Employer expectations about jobs and skills | https://www.weforum.org/publications/the-future-of-jobs-report-2025/ |
| OECD.AI | Public AI policies and initiatives from jurisdictions and organisations | https://oecd.ai/en/dashboards |
| NIST AI RMF | Voluntary AI risk-management framework and resources | https://www.nist.gov/itl/ai-risk-management-framework |
Use the original page to confirm method and population.
Public source pages linked below.
Cited statistic table
Source notes
Reading checklist
Research brief
Update route
Owner
| Step | Stage | What it changes |
|---|---|---|
| 01 | Signal | List the recurring figures teams request most: McKinsey adoption rates, Stanford HAI AI Index trends, WEF Future of Jobs employer expectations and OECD.AI policy counts. |
| 02 | Synthesis | Build a citation table recording source, publication period, surveyed population and definition for each statistic, so secondary paraphrases never replace the original report. |
| 03 | System | Link every row to the original publisher page, label whether the figure covers enterprises, respondents or countries, and flag figures that lack a stated population. |
| 04 | Scale | Assign a researcher to refresh rows when McKinsey or Stanford HAI publish new editions, tracking citation currency and the share of rows with verified primary links. |
Widely cited AI statistics come from public reports rather than private vendor claims. McKinsey surveys enterprise AI adoption and financial impact. Stanford HAI publishes the AI Index.
The World Economic Forum reports employer expectations in Future of Jobs. OECD.AI tracks policy initiatives. NIST publishes the AI Risk Management Framework. Paloren cites the original pages rather than paraphrasing secondary summaries.
A statistic becomes misleading when the population or definition is omitted. For example, a figure about large enterprises does not describe every small business. A survey about respondents does not equal a census of all companies.
Paloren labels each row with the source, period and population so the evidence can be checked before use.
No single source covers all AI use. Use survey, index and policy sources together, such as McKinsey, Stanford HAI, WEF, OECD and NIST.
Not always. Check the population in the source before applying a figure to your business.
OECD.AI provides a public policy navigator covering initiatives from more than 80 jurisdictions and organisations.
NIST publishes the AI Risk Management Framework and related resources.
Which data matters to your decision?
Plan evidence-led AI
