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Statistics

Current trends in AI, in numbers

Current trends in AI, backed by published surveys and research. Every figure carries its source and year, plus a plain reading for a company of 10 to 500 people.

Updated 6 min read

The numbers

Key numbers, with sources

What the adoption numbers say

McKinsey survey

  • 78% use AI in a function
  • 71% use generative AI

Federal data on all firms

  • About 18% adopted AI
  • 3.8% use AI in production

The latest trends in AI start with use. McKinsey's survey shows 78% of organizations using AI in at least one function, and generative AI use jumped from 33% to 71% in one year.

Federal data is far lower: about 18% of firms by the end of 2025.

The Census Bureau measured 3.8% using AI to produce goods and services. These counts differ because they ask different questions of different groups.

Money in, profit uncertain

  • Investment

    $109.1 billion in the U.S. in 2024.

  • Profit effect

    37% report a positive EBIT effect.

  • High performers

    Only 6% credit AI with 5% or more of EBIT.

Investment keeps climbing. Stanford's AI Index puts U.S. private AI investment at $109.1 billion in 2024, and generative AI drew $33.9 billion worldwide.

Profit is the harder part. About 37% of McKinsey's respondents report a positive EBIT effect, and only 6% are high performers, crediting AI with 5% or more of EBIT. Spending is outrunning measured returns.

Workers, jobs and sectors

  • Finance

    63% report work-related generative AI use.

  • Professional services

    62% report the same.

  • Workforce outlook

    32% expect cuts, 43% no change, 13% growth.

The Federal Reserve finds work-related generative AI use highest in financial services at 63% and professional services at 62%. Adoption follows the type of work, not company size alone.

On jobs, expectations are split: 32% of McKinsey respondents expect a smaller workforce, 43% no change and 13% a larger one.

For AI trends in healthcare (the AI in healthcare guide has context), law or logistics, look for sector-specific studies rather than averages.

Where projects stall

  1. 01

    Name the process

    Pick one that costs real hours.

  2. 02

    Measure it

    Record time and cost today.

  3. 03

    Build and track

    Compare against the baseline.

The barrier named most often in the PEX report is lack of internal expertise, at 49%. Tools are easy to buy, but few teams know where AI fits their own processes.

That also explains the gap between wide use and thin profit. Spreading AI across many small experiments rarely moves a P&L; fixing one costly process does.

A short AI readiness survey of your own processes is a fair first step.

The AI adoption guide shows how to pick that first process.

How to read AI statistics

Check before you cite

  • Who was surveyed
  • Year of the data
  • Who paid for it

Avoid

  • Mixing populations
  • Rounding up
  • Undated figures

Check who was asked. A survey of executives, one of workers and a census of all firms measure different things, so their percentages cannot be set side by side.

Check the date and the sponsor. AI moves fast, so a figure from two years ago may be stale, and a vendor's own survey leans toward its product.

Prefer government data, universities and large research firms, and quote the year every time. For agent figures, see AI agent statistics.

Be careful with claims about AI market share by company. Counts of firms using AI say little about which company holds what share of AI revenue.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI consulting. They named 95 companies, and 81% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named342560
Sites cited mostslalom.com (4 of 10)effectivesoft.com (6 of 10)phosailabs.com and wiserbrand.com (3 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 6 companies appeared in all three. Google showed an AI Overview for 7 of the 10 questions.

Methodology

How we chose these numbers

  1. 01Only figures from published surveys and research reports are included.
  2. 02Each figure keeps the wording of its source, with publisher and year.
  3. 03Figures from different populations are never merged into one number.
  4. 04Where sources disagree, both appear and the difference is explained.
  5. 05The page is updated when a newer study replaces a figure.

FAQ

AI trends questions

  • Wider use, heavy investment and uneven returns. McKinsey reports 78% of organizations using AI, Stanford counts $109.1 billion of U.S. private investment in 2024, and only 6% of respondents are high performers.

  • Generative AI use grew from 33% to 71% of surveyed organizations in a year, and a growing share of firms say AI supports innovation. Profit impact still lags, so firms increasingly start with one measured process.

  • Counts depend on the survey. McKinsey finds 78% of surveyed organizations, the Federal Reserve about 18% of firms at the end of 2025, and the Census Bureau 3.8% using AI in production.

  • In Federal Reserve data, financial services lead work-related generative AI use at 63%, followed by professional services at 62%. Many other sectors sit well below.

  • Expectations are split. In McKinsey's survey, 32% expect their workforce to shrink, 43% expect no change and 13% expect it to grow.

  • Lack of internal expertise tops one report, at 49% of respondents. Unclear processes and weak measurement make the gap worse, which is why a small first project helps.

Related

Keep reading

Final step

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