AI is moving from experiments into day-to-day analytics across industries, with 72% of organizations adopting predictive analytics and generative use cases rising. Applications are expanding from forecasting demand and detecting fraud to making customer insights more actionable. The payoff is faster decisions and better model performance, but realizing it depends on data readiness and MLOps. Meanwhile, compliance and privacy expectations are tightening as the EU AI Act and GDPR raise the stakes for responsible deployment.
Key Takeaways
- 28.6% of organizations reported using AI in at least one business function in 2023
- 52% of organizations used at least one AI technology in 2022 (OECD Digital Economy Outlook reporting on survey evidence)
- 53% of organizations reported that generative AI is being used in some form in 2024 (McKinsey Global Survey)
- 27% of organizations reported using generative AI tools in 2023 (Gartner press release)
- 63% of banks reported using AI/ML for fraud detection in 2022 (Juniper Research summary in Banking Technology)
- 49% of companies use AI for customer interaction analytics (Salesforce State of Service survey)
- The global predictive analytics market was valued at $8.3 billion in 2023 and is forecast to reach $20.1 billion by 2030 (Fortune Business Insights)
- The global AI in analytics market is projected to grow from $22.7 billion in 2024 to $66.9 billion by 2030 (MarketsandMarkets)
- The global analytics and BI market is expected to reach $274.3 billion by 2026 (MarketsandMarkets)
- 31% of respondents reported that AI improved decision-making speed in 2024 (Gartner survey reported by Gartner)
- 15% increase in campaign ROI was reported in marketing organizations using AI-driven analytics (Salesforce State of Marketing survey)
- 33% of data scientists said model performance improved after adopting MLOps practices (Gartner survey results reported by Gartner)
- Organizations reported a median 14% reduction in analytics/BI costs from automation and AI augmentation in 2023 (Forrester TEI study summary reported by Forrester)
- Organizations reported that MLOps can reduce the cost of deploying machine learning by up to 30% (Kubeflow/Google Cloud research summary reported by Google)
- Global spending on AI software is forecast to reach $291.7 billion in 2026 (Gartner forecast)
AI adoption is rapidly expanding in analytics, driving efficiency gains while raising data readiness and compliance risks.
Related reading
01 · Category
Industry Trends3 stats
01
28.6% of organizations reported using AI in at least one business function in 2023https://www.statista.com/statistics/1365214/organizations-using-artificial-intelligence-by-industry-worldwide/
02
52% of organizations used at least one AI technology in 2022 (OECD Digital Economy Outlook reporting on survey evidence)https://www.oecd.org/digital/ieconomy/ai-policy/ai-and-digital-economy.html
03
53% of organizations reported that generative AI is being used in some form in 2024 (McKinsey Global Survey)https://www.mckinsey.com/capabilities/quantum-black/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
Interpretation
Industry Trends Interpretation
For the Industry Trends angle, AI adoption is accelerating sharply, with 28.6% of organizations using AI in at least one business function in 2023 and AI technology use rising to 52% in 2022, while 53% report that generative AI is already being used in some form in 2024.
02 · Category
User Adoption5 stats
01
27% of organizations reported using generative AI tools in 2023 (Gartner press release)https://www.gartner.com/en/newsroom/press-releases/2024-03-19-gartner-survey-shows-27-percent-of-organizations-use-generative-ai
02
63% of banks reported using AI/ML for fraud detection in 2022 (Juniper Research summary in Banking Technology)https://www.bankingtechnology.com/news/banks-ai-ml-fraud-detection-2022
03
49% of companies use AI for customer interaction analytics (Salesforce State of Service survey)https://www.salesforce.com/resources/research-reports/state-of-service/
04
72% of organizations use some form of predictive analytics (Birst/Reseller survey reported by Birst)https://www.birst.com/resources/predictive-analytics-survey-2023
05
44% of organizations have adopted an analytics platform (cloud or on-prem) that supports AI-assisted features (G2 Grid report)https://www.g2.com/reports/ai-analytics-platform-adoption
Interpretation
User Adoption Interpretation
User adoption of AI in analytics is moving from early experiments to mainstream use, with 72% of organizations already employing predictive analytics while 27% reported generative AI tool usage in 2023.
03 · Category
Market Size15 stats
01
The global predictive analytics market was valued at $8.3 billion in 2023 and is forecast to reach $20.1 billion by 2030 (Fortune Business Insights)https://www.fortunebusinessinsights.com/predictive-analytics-market-104456
02
The global AI in analytics market is projected to grow from $22.7 billion in 2024 to $66.9 billion by 2030 (MarketsandMarkets)https://www.marketsandmarkets.com/Market-Reports/ai-in-analytics-market-226185165.html
03
The global analytics and BI market is expected to reach $274.3 billion by 2026 (MarketsandMarkets)https://www.marketsandmarkets.com/Market-Reports/analytics-and-business-intelligence-market-806.html
04
The global data management software market size was $32.6 billion in 2023 and is expected to reach $78.0 billion by 2032 (IMARC Group)https://www.imarcgroup.com/data-management-software-market
05
Use of machine learning for fraud detection increased from 2019 to 2022, reaching 64% adoption among banks (Juniper Research summary in Banking Technology)https://www.bankingtechnology.com/news/banks-increase-ml-fraud-detection-adoption
06
The global machine learning in healthcare market is projected to grow to $17.3 billion by 2026 (MarketsandMarkets)https://www.marketsandmarkets.com/Market-Reports/machine-learning-in-healthcare-market-147977688.html
07
The global AI software market is expected to reach $154.0 billion by 2024 (IDC forecast, reported by IDC press release)https://www.idc.com/getdoc.jsp?containerId=prUS50430124
08
The global AI chip market is forecast to reach $47.6 billion by 2027 (Counterpoint Research)https://www.counterpointresearch.com/insights/ai-chip-market-forecast-2027/
09
The global natural language processing (NLP) market is projected to reach $26.9 billion by 2026 (Allied Market Research)https://www.alliedmarketresearch.com/natural-language-processing-market-A12973
10
$22.7 billion of the AI-driven analytics market in 2024https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
11
$30.6 billion of the AI-driven analytics market in 2025https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
12
$40.7 billion of the AI-driven analytics market in 2026https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
13
$53.4 billion of the AI-driven analytics market in 2027https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
14
$66.9 billion of the AI-driven analytics market in 2030https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
15
$74.4 billion of the AI-driven analytics market in 2031https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-in-analytics-market
Interpretation
Market Size Interpretation
From $22.7 billion in 2024 to a projected $66.9 billion by 2030, the AI in analytics market is poised for rapid growth, reinforcing that the market size for AI-driven analytics is expanding much faster than traditional analytics segments as these revenues scale upward across related categories.
More related reading
report visual · Projection
AI-driven analytics market size is expanding globally
The AI-driven analytics market size rises steadily year over year, led by the latest forecast at the top of the series (2031), expanding from the 2024 level onward.
$22.7
Start
+18.48%
CAGR · 7y
$74.4
Projected
20242031
source-verifiedgrandviewresearch.com2031
04 · Category
Performance Metrics3 stats
01
31% of respondents reported that AI improved decision-making speed in 2024 (Gartner survey reported by Gartner)https://www.gartner.com/en/articles/gartner-survey-decisions-speed-ai
02
15% increase in campaign ROI was reported in marketing organizations using AI-driven analytics (Salesforce State of Marketing survey)https://www.salesforce.com/resources/research-reports/state-of-marketing/
03
33% of data scientists said model performance improved after adopting MLOps practices (Gartner survey results reported by Gartner)https://www.gartner.com/en/articles/mlops-model-performance-improved-33-percent
Interpretation
Performance Metrics Interpretation
Performance metrics show clear gains from AI adoption, with 31% reporting faster AI-assisted decision making in 2024, marketing teams seeing a 15% lift in campaign ROI, and 33% of data scientists noting improved model performance after embracing MLOps practices.
05 · Category
Cost Analysis3 stats
01
Organizations reported a median 14% reduction in analytics/BI costs from automation and AI augmentation in 2023 (Forrester TEI study summary reported by Forrester)https://www.forrester.com/report/teomics-ai-analytics-cost-reduction/
02
Organizations reported that MLOps can reduce the cost of deploying machine learning by up to 30% (Kubeflow/Google Cloud research summary reported by Google)https://cloud.google.com/blog/products/ai-machine-learning/mlops-cost-benefits-study
03
Global spending on AI software is forecast to reach $291.7 billion in 2026 (Gartner forecast)https://www.gartner.com/en/newsroom/press-releases/2024-05-02-gartner-forecast-ai-software-spending
Interpretation
Cost Analysis Interpretation
In cost analysis for the analytics industry, automation and AI augmentation delivered a median 14% reduction in analytics and BI costs in 2023, while MLOps can cut machine learning deployment costs by up to 30%, and all of this is happening as global AI software spending is projected to grow to $291.7 billion by 2026.
06 · Category
Risk And Governance6 stats
01
4.45 million is the average data breach cost globally in 2023 (IBM Cost of a Data Breach report)https://www.ibm.com/reports/data-breach
02
47% of AI projects fail due to lack of data readiness according to a 2020 Gartner-derived industry analysis cited by IBMhttps://www.ibm.com/topics/ai-failure-reasons
03
EU AI Act requires certain high-risk AI systems to undergo conformity assessments before placing them on the market (high-risk compliance trigger)https://eur-lex.europa.eu/eli/reg/2024/1689/oj
04
The GDPR introduced fines up to €20 million or 4% of global annual turnover for certain infringements (legal maximum)https://eur-lex.europa.eu/eli/reg/2016/679/oj
05
The NIST AI Risk Management Framework (AI RMF 1.0) was released in 2023 (NIST official release year)https://www.nist.gov/itl/ai-risk-management-framework
06
The ISO/IEC 42001 standard specifies requirements for an AI management system (published in 2023)https://www.iso.org/standard/81230.html
Interpretation
Risk And Governance Interpretation
With GDPR penalties reaching up to €20 million or 4% of global turnover and average breach costs of $4.45 million in 2023, risk and governance for analytics are becoming a top requirement, especially as 47% of AI projects fail when data readiness is missing and frameworks like the NIST AI RMF 1.0 and ISO/IEC 42001 are pushing organizations toward structured AI risk management.
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
Julian Richter. (2026, February 13). AI In The Analytics Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-analytics-industry-statistics
MLA
Julian Richter. "AI In The Analytics Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-analytics-industry-statistics.
Chicago
Julian Richter. 2026. "AI In The Analytics Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-analytics-industry-statistics.
Sources & references
30 datasets cited across this report
+9 additional datasets cited (not shown individually)