The short answer
Paloren compiles AI statistics from cited public sources so teams can use evidence without confusing vendor marketing with survey or government data.
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.
What this can change for your team
- Cited evidence
- Workflow plan
- Governance controls
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What are the most cited AI statistics?
Figures from McKinsey, Stanford HAI, WEF, OECD and NIST.
How we make this work
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.
- McKinsey State of AI
- Stanford AI Index
- WEF Future of Jobs
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How should you read an AI statistic?
Check the population, period, definition and source.
How we make this work
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.
- Population and period
- Definition used
- Original citation
Public AI evidence sources
Use the original page to confirm method and population.
| 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 |
Source: Public source pages linked below.
Make the next decision
What to do with this
Cited statistic table
Source notes
Reading checklist
Research brief
Update route
Owner
- 01
Check source
Use the original publication.
- 02
Read method
Confirm population and period.
- 03
Avoid generalisation
Do not apply one segment to all businesses.
- 04
Update regularly
Track changes from the same source.
| Stage | What it changes |
|---|---|
| Check source | Use the original publication. |
| Read method | Confirm population and period. |
| Avoid generalisation | Do not apply one segment to all businesses. |
| Update regularly | Track changes from the same source. |
Which data matters to your decision?
Tell Paloren the workflow and the evidence you need.
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Before we begin
Questions we get asked, answered with numbers
What is the best source for AI statistics?
No single source covers all AI use. Use survey, index and policy sources together, such as McKinsey, Stanford HAI, WEF, OECD and NIST.
Do these statistics apply to small businesses?
Not always. Check the population in the source before applying a figure to your business.
Where can I find AI policy statistics?
OECD.AI provides a public policy navigator covering initiatives from more than 80 jurisdictions and organisations.
Where can I find AI risk guidance?
NIST publishes the AI Risk Management Framework and related resources.
Which data matters to your decision?