Generative AI adoption in analytics is already mainstream, with 53% of organizations reporting use of some form in 2024. That momentum sits alongside broader AI rollout, including 28.6% using AI in at least one business function in 2023 and 52% adopting at least one AI technology in 2022. Global spending on AI software is forecast to reach $291.7 billion by 2026, even as data readiness and governance continue to determine whether models reach production.
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)
Generative AI adoption is surging, boosting analytics value, but data readiness and governance remain critical.
Related reading
01 · Category
Industry Trends3 stats
01
28.6% of organizations reported using AI in at least one business function in 2023
02
52% of organizations used at least one AI technology in 2022 (OECD Digital Economy Outlook reporting on survey evidence)
03
53% of organizations reported that generative AI is being used in some form in 2024 (McKinsey Global Survey)
Interpretation
Industry Trends Interpretation
In Industry Trends, AI adoption is accelerating quickly with 53% of organizations reporting some form of generative AI use in 2024, building on the broader momentum of 28.6% using AI in at least one business function in 2023 and 52% adopting at least one AI technology in 2022.
02 · Category
User Adoption5 stats
01
27% of organizations reported using generative AI tools in 2023 (Gartner press release)
02
63% of banks reported using AI/ML for fraud detection in 2022 (Juniper Research summary in Banking Technology)
03
49% of companies use AI for customer interaction analytics (Salesforce State of Service survey)
04
72% of organizations use some form of predictive analytics (Birst/Reseller survey reported by Birst)
05
44% of organizations have adopted an analytics platform (cloud or on-prem) that supports AI-assisted features (G2 Grid report)
Interpretation
User Adoption Interpretation
User adoption of AI in analytics is already mainstream, with 72% of organizations using predictive analytics and 44% adopting AI-enabled analytics platforms, even as generative AI adoption remains at 27% in 2023.
03 · Category
Market Size9 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)
02
The global AI in analytics market is projected to grow from $22.7 billion in 2024 to $66.9 billion by 2030 (MarketsandMarkets)
03
The global analytics and BI market is expected to reach $274.3 billion by 2026 (MarketsandMarkets)
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)
05
Use of machine learning for fraud detection increased from 2019 to 2022, reaching 64% adoption among banks (Juniper Research summary in Banking Technology)
06
The global machine learning in healthcare market is projected to grow to $17.3 billion by 2026 (MarketsandMarkets)
07
The global AI software market is expected to reach $154.0 billion by 2024 (IDC forecast, reported by IDC press release)
08
The global AI chip market is forecast to reach $47.6 billion by 2027 (Counterpoint Research)
09
The global natural language processing (NLP) market is projected to reach $26.9 billion by 2026 (Allied Market Research)
Interpretation
Market Size Interpretation
The market size data shows rapid expansion for AI-driven analytics, with the global AI in analytics market growing from $22.7 billion in 2024 to $66.9 billion by 2030, signaling strong momentum in the broader analytics and data software landscape.
More related reading
04 · Category
Performance Metrics3 stats
01
31% of respondents reported that AI improved decision-making speed in 2024 (Gartner survey reported by Gartner)
02
15% increase in campaign ROI was reported in marketing organizations using AI-driven analytics (Salesforce State of Marketing survey)
03
33% of data scientists said model performance improved after adopting MLOps practices (Gartner survey results reported by Gartner)
Interpretation
Performance Metrics Interpretation
Performance metrics show measurable gains from AI in analytics, with 31% of respondents citing faster decision making in 2024, a 15% lift in campaign ROI for marketing teams using AI-driven analytics, and 33% of data scientists reporting improved model performance after adopting 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)
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)
03
Global spending on AI software is forecast to reach $291.7 billion in 2026 (Gartner forecast)
Interpretation
Cost Analysis Interpretation
Cost analysis is showing real momentum as organizations cut analytics and BI costs by a median 14% in 2023 with automation and AI augmentation, while MLOps can reduce machine learning deployment costs by up to 30% and global AI software spending is projected to reach $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)
02
47% of AI projects fail due to lack of data readiness according to a 2020 Gartner-derived industry analysis cited by IBM
03
EU AI Act requires certain high-risk AI systems to undergo conformity assessments before placing them on the market (high-risk compliance trigger)
04
The GDPR introduced fines up to €20 million or 4% of global annual turnover for certain infringements (legal maximum)
05
The NIST AI Risk Management Framework (AI RMF 1.0) was released in 2023 (NIST official release year)
06
The ISO/IEC 42001 standard specifies requirements for an AI management system (published in 2023)
Interpretation
Risk And Governance Interpretation
With AI governance tightening across jurisdictions, the risk is stark: 4.45 million was the average global cost of a data breach in 2023 and 47% of AI projects still fail due to poor data readiness, underscoring why frameworks and standards like the 2023 NIST AI RMF 1.0 and the ISO/IEC 42001 AI management system matter for managing compliance and exposure.
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
29 datasets cited across this report · attribution is report-level
+9 additional datasets cited (not shown individually)