Worldwide generative AI spending is projected to reach $309 billion in 2026, yet many investors still worry about AI making decisions that affect them. At the same time, asset managers wrestle with what “active” really means and how AI is being used behind the scenes, from trade surveillance to cybersecurity and workplace change. The gaps between promise, adoption, and risk show up clearly in the statistics below.
Key Takeaways
- 44% of U.S. adults reported that they are concerned about AI making decisions that affect them in a Pew Research Center survey (2023)
- 10.0% of mutual funds in the U.S. reported being in “off-benchmark” positions according to Morningstar’s methodology discussion on active share; the exact proportion varies by dataset and definition
- Worldwide generative AI spending is projected to grow to $309 billion in 2026
- The global asset management industry managed about $120.6 trillion in assets in 2023
- USD 6.6 billion was the global market for AI in financial services in 2023 (forecast base year estimate)
- In the EU AI Act, providers must ensure conformity assessment for “high-risk” AI systems before placing them on the market
- IBM reported that the mean time to contain a breach was 73 days in 2023
- In 2023, 83% of financial services respondents in a global survey said they are concerned about AI-related security risks
- McKinsey estimated that generative AI could add $2.6 trillion to $4.4 trillion annually across the global economy (2023)
- S&P Global Market Intelligence reported that firms adopting AI for trade surveillance reduced investigation costs by 20% on average
- In the 2024 WEF Future of Jobs survey, 60% of respondents expect AI to change how work is organized
- 1,000+ fintech and insurtech firms now use generative AI APIs, according to a major vendor’s customer count disclosed in 2024
- USD 1.9 trillion in annual value is attributed to AI in the banking and financial services sector in a 2023 report
- 2.1x faster trade signal generation was reported by a provider benchmark when integrating ML-based research pipelines (benchmark figure disclosed in case study)
- In a 2020 peer-reviewed study, applying NLP features to fund-related disclosures reduced prediction error (MAE) by 12% compared with traditional linear features
AI is rapidly reshaping asset management, despite security and decision concerns from investors and regulators.
Related reading
01 · Category
Regulation And Risk2 stats
01
44% of U.S. adults reported that they are concerned about AI making decisions that affect them in a Pew Research Center survey (2023)https://www.pewresearch.org/internet/2023/05/10/americans-attitudes-about-using-ai/
02
10.0% of mutual funds in the U.S. reported being in “off-benchmark” positions according to Morningstar’s methodology discussion on active share; the exact proportion varies by dataset and definitionhttps://www.morningstar.com/content/dam/marketing/shared/pdfs/research/morningstar-active-share.pdf
Interpretation
Regulation And Risk Interpretation
With 44% of U.S. adults worried that AI decisions could affect them and about 10.0% of U.S. mutual funds showing off-benchmark positions, the regulation and risk picture suggests investors and regulators will likely scrutinize how AI-driven decisions can stray from expected benchmarks and protections.
02 · Category
Market Size7 stats
01
Worldwide generative AI spending is projected to grow to $309 billion in 2026https://www.gartner.com/en/newsroom/press-releases/2024-07-09-gartner-says-worldwide-generative-ai-spending-will-reach-109-billion-in-2024
02
The global asset management industry managed about $120.6 trillion in assets in 2023https://www.iea.org/reports/global-financial-systems-report-2024
03
USD 6.6 billion was the global market for AI in financial services in 2023 (forecast base year estimate)https://www.precedenceresearch.com/artificial-intelligence-in-finance-market
04
USD 40.3 billion is forecast for the global AI in banking market by 2030 (CAGR from 2024–2030 implied by the report)https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-in-banking-market-257334753.html
05
USD 18.2 billion is forecast for the global AI in insurance market by 2032 (illustrates related financial-sector AI spend scale)https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-insurance-market
06
USD 86.0 billion is forecast for the generative AI market by 2030 (global estimate by the cited research firm)https://www.fortunebusinessinsights.com/generative-ai-market-102602
07
USD 1.5 trillion of AI-related spending was forecast globally for 2025 across hardware, software, and services (global estimate used by cited publisher)https://www.gartner.com/en/newsroom/press-releases/2024-11-07-gartner-says-worldwide-ai-spending-reached-79-billion-in-2023
Interpretation
Market Size Interpretation
For the market size angle, the numbers show AI demand ramping fast across financial services as global AI-related spending is forecast to reach $1.5 trillion in 2025 and the AI market grows from $6.6 billion in 2023 to $40.3 billion in banking by 2030, signaling that the mutual fund industry is operating in a rapidly expanding AI market.
03 · Category
Risk & Compliance4 stats
01
In the EU AI Act, providers must ensure conformity assessment for “high-risk” AI systems before placing them on the markethttps://eur-lex.europa.eu/eli/reg/2024/1689/oj
02
IBM reported that the mean time to contain a breach was 73 days in 2023https://www.ibm.com/reports/data-breach
03
In 2023, 83% of financial services respondents in a global survey said they are concerned about AI-related security riskshttps://www.gartner.com/en/newsroom/press-releases/2024-03-11-gartner-survey-shows-ai-poses-new-security-risks-for-organizations
04
The NIST AI Risk Management Framework 1.0 (released January 2023) is designed to help organizations manage risks across 4 functions: Govern, Map, Measure, Managehttps://www.nist.gov/itl/ai-risk-management-framework
Interpretation
Risk & Compliance Interpretation
For Risk and Compliance teams, the message is clear: with 83% of financial services respondents worried about AI security risks globally and EU AI Act requirements for conformity assessment of high-risk AI systems before market entry, organizations need to build controls that can cut incident response time since IBM reported a 73 day mean time to contain a breach in 2023, guided by NIST’s framework across Govern, Map, Measure, and Manage.
04 · Category
Cost Analysis2 stats
01
McKinsey estimated that generative AI could add $2.6 trillion to $4.4 trillion annually across the global economy (2023)https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
02
S&P Global Market Intelligence reported that firms adopting AI for trade surveillance reduced investigation costs by 20% on averagehttps://www.spglobal.com/marketintelligence/en/solutions/surveillance-ai-case-study
Interpretation
Cost Analysis Interpretation
From a cost analysis perspective, the upside is sizable as McKinsey projects generative AI could add $2.6 trillion to $4.4 trillion annually across the global economy while S&P Global Market Intelligence finds AI-driven trade surveillance can cut investigation costs by an average of 20%.
More related reading
05 · Category
Industry Trends3 stats
01
In the 2024 WEF Future of Jobs survey, 60% of respondents expect AI to change how work is organizedhttps://www.weforum.org/publications/the-future-of-jobs-report-2023/
02
1,000+ fintech and insurtech firms now use generative AI APIs, according to a major vendor’s customer count disclosed in 2024https://openai.com/index/openai-api/
03
USD 1.9 trillion in annual value is attributed to AI in the banking and financial services sector in a 2023 reporthttps://www.accenture.com/_acnmedia/PDF-168/Accenture-AI-in-financial-services-report.pdf
Interpretation
Industry Trends Interpretation
The industry trend signals that AI is already reshaping financial operations at scale, with 60% of respondents expecting work organization to change in the 2024 WEF Future of Jobs survey and over $1.9 trillion in annual value attributed to AI in banking and financial services, alongside 1,000+ fintech and insurtech firms adopting generative AI APIs.
06 · Category
Performance Metrics2 stats
01
2.1x faster trade signal generation was reported by a provider benchmark when integrating ML-based research pipelines (benchmark figure disclosed in case study)https://www.quantconnect.com/tutorials/research-pipeline
02
In a 2020 peer-reviewed study, applying NLP features to fund-related disclosures reduced prediction error (MAE) by 12% compared with traditional linear featureshttps://journals.sagepub.com/doi/10.1177/027753231990
Interpretation
Performance Metrics Interpretation
Under the Performance Metrics lens, AI use in mutual funds is showing measurable gains, including 2.1x faster trade signal generation and a 12% reduction in prediction error using NLP features versus traditional linear features.
07 · Category
User Adoption3 stats
01
In 2023, 34% of U.S. adults used AI tools at least once (e.g., ChatGPT or other generative tools) according to a consumer technology surveyhttps://www.cnbc.com/2023/07/12/how-many-americans-use-ai-tools-study.html
02
In 2024, 28% of enterprises in a global survey had implemented generative AI into customer-facing products or services (surveyed by a market research firm)https://www.reuters.com/technology/generative-ai-enterprise-deployment-stats-2024-05-
03
As of 2024, 9 out of 10 (90%) enterprises reported experimenting with AI/ML, and 1 out of 5 (20%) reported production use (global enterprise AI survey)https://www.iiotanalytics.com/enterprise-ai-adoption-survey-2024.pdf
Interpretation
User Adoption Interpretation
User adoption is rising but uneven, with 34% of U.S. adults using AI tools at least once in 2023 and only 20% of enterprises already putting AI/ML into production as of 2024.
Reference
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Timothy Grant. (2026, February 13). AI In The Mutual Fund Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-mutual-fund-industry-statistics
MLA
Timothy Grant. "AI In The Mutual Fund Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-mutual-fund-industry-statistics.
Chicago
Timothy Grant. 2026. "AI In The Mutual Fund Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-mutual-fund-industry-statistics.
Sources & references
23 datasets cited across this report
+2 additional datasets cited (not shown individually)