Global AI spending reached 554 billion dollars in 2023 and is projected to reach 1.6 trillion dollars by 2030. Generative AI has already reached deployment in at least one function at 46 percent of organizations. Productivity gains appear for 64 percent of respondents using the tools.
Key Takeaways
- 22.1% CAGR for the global AI in software market forecast for 2024–2030
- $84.0 billion global market size for AI in software in 2023
- $268.0 billion expected global market size for AI in software by 2030
- 46% of executives say generative AI is already deployed in at least one function (surveyed)
- 64% of respondents say generative AI is improving productivity or performance (surveyed)
- 55% of respondents report using generative AI for content generation (surveyed)
- 48% of respondents say they are using AI to reduce costs (surveyed)
- 42% of respondents say they are using AI to improve revenue (surveyed)
- 10% to 30% potential savings from AI-enabled operations improvements (range stated)
- 30% higher conversion rate with personalized AI recommendations (reported A/B results)
- 10% to 15% higher average order value with product recommendations (range reported)
- 25% increased recommendation relevance with contextual bandit optimization (benchmark)
- 23% of organizations report AI adoption in at least one core business process (surveyed)
- 33% of enterprises report using AI in at least one function (surveyed)
- 11% of enterprises report using AI in at least one business process continuously (surveyed)
AI spending and adoption are accelerating fast, with major market growth and strong early use of generative AI.
Related reading
01 · Category
Market Size30 stats
01
22.1% CAGR for the global AI in software market forecast for 2024–2030
02
$84.0 billion global market size for AI in software in 2023
03
$268.0 billion expected global market size for AI in software by 2030
04
38% CAGR for the global AI software market forecast for 2024–2030
05
$16.5 billion global market size for artificial intelligence software in 2023
06
$110.1 billion expected global market size for artificial intelligence software by 2032
07
$14.3 billion global market size for AI in IT operations in 2023
08
37.1% CAGR for AI in IT operations forecast for 2024–2032
09
$134.1 billion expected global market size for AI in IT operations by 2032
10
$27.8 billion global market size for AI in cybersecurity in 2023
11
41.2% CAGR for AI in cybersecurity forecast for 2024–2032
12
$223.2 billion expected global market size for AI in cybersecurity by 2032
13
$16.1 billion global market size for AI in finance in 2023
14
43.3% CAGR for AI in finance forecast for 2024–2032
15
$177.4 billion expected global market size for AI in finance by 2032
16
$1.2 trillion global investment in AI 2023–2027 (projected total)
17
$554 billion worldwide AI spending forecast for 2023
18
$679.1 billion worldwide AI spending forecast for 2024
19
$1.6 trillion worldwide AI spending projected by 2030
20
$77.1 billion global market size for generative AI in 2023 (forecast)
21
$593.6 billion expected global market size for generative AI by 2030 (forecast)
22
28.4% CAGR for the generative AI market forecast for 2024–2032
23
$14.0 billion global generative AI market size in 2023 (forecast)
24
$110.0 billion expected global generative AI market size by 2030 (forecast)
25
$7.6 billion global market size for AI in customer service in 2023 (forecast)
26
$24.4 billion expected global market size for AI in customer service by 2030 (forecast)
27
31.8% CAGR for AI in customer service market forecast 2024–2030
28
$18.0 billion global market size for AI in IT operations in 2022 (forecast baseline)
29
$69.8 billion expected global market size for AI in IT operations by 2030
30
16.8% CAGR for AI in IT operations forecast 2024–2030
Interpretation
Market Size Interpretation
Across major segments, AI demand is accelerating fast, with generative AI forecast to grow from about $77.1 billion in 2023 to $593.6 billion by 2030 and overall AI spending projected to rise from $554 billion in 2023 to $1.6 trillion by 2030.
02 · Category
Industry Trends14 stats
01
46% of executives say generative AI is already deployed in at least one function (surveyed)
02
64% of respondents say generative AI is improving productivity or performance (surveyed)
03
55% of respondents report using generative AI for content generation (surveyed)
04
37% of respondents report using generative AI for software development (surveyed)
05
28% of respondents report using generative AI for customer operations (surveyed)
06
73% of enterprises use AI to automate data quality tasks (surveyed)
07
65% of enterprises say AI improves data discovery and usage (surveyed)
08
61% of enterprises say AI reduces manual work for data stewards (surveyed)
09
71% of organizations say they use some form of data classification (surveyed)
10
43% of organizations say they have adopted or are adopting AI in governance/oversight (surveyed)
11
27% of organizations say AI governance is fully implemented (surveyed)
12
15% of organizations say they still do not have an AI ethics policy (surveyed)
13
66% of organizations say they have an AI ethics policy (surveyed)
14
24% of organizations have an AI ethics policy without enforcement mechanisms (surveyed)
Interpretation
Industry Trends Interpretation
Across the surveyed information industry, generative AI is already in at least one function for 46% of executives and is boosting productivity for 64% of respondents, while AI ethics remains incomplete for many organizations with 15% still lacking an AI ethics policy and only 27% saying governance is fully implemented.
03 · Category
Cost Analysis15 stats
01
48% of respondents say they are using AI to reduce costs (surveyed)
02
42% of respondents say they are using AI to improve revenue (surveyed)
03
10% to 30% potential savings from AI-enabled operations improvements (range stated)
04
35% of organizations report lower IT support costs after implementing AI for IT operations (surveyed)
05
25% of organizations report fewer ticket escalations after AI deployment in IT service desks (surveyed)
06
20% reduction in manual document processing costs with AI document understanding (case-based)
07
$3.4 million average annual savings from using AI in customer service operations (reported average)
08
8% average cost reduction from migrating analytics workloads to optimized infrastructure (reported by study)
09
15% reduction in mean time to detect (MTTD) leading to reduced breach response costs (reported)
10
19% reduction in average handling time (AHT) for customer support with AI chatbots (reported)
11
16% cost savings from AI knowledge management adoption (surveyed)
12
13% reduction in fraud false positives cost with AI scoring (reported by vendor benchmark)
13
8% reduction in chargebacks with AI/ML-based fraud detection (case metric)
14
27% reduction in IT operational costs with AIOps (surveyed)
15
22% reduction in operational risk cost from AI-driven monitoring (surveyed)
Interpretation
Cost Analysis Interpretation
Across the surveyed and reported results, AI is most clearly delivering measurable business value, with cost reductions ranging from 35% in IT support and 25% in fewer escalations to average annual customer service savings of $3.4 million and up to 27% lower IT operational costs through AIOps.
More related reading
04 · Category
Performance Metrics20 stats
01
30% higher conversion rate with personalized AI recommendations (reported A/B results)
02
10% to 15% higher average order value with product recommendations (range reported)
03
25% increased recommendation relevance with contextual bandit optimization (benchmark)
04
33% improved model accuracy from feature selection automation (reported)
05
1.3x improvement in F1 score for entity extraction using transformers over baseline (paper result)
06
2.6x increase in throughput with parallelized inference optimization for LLMs (performance study result)
07
60% reduction in latency via speculative decoding (reported in optimization study)
08
10–100x speedup with FlashAttention compared to naive attention (range stated in paper)
09
15% improvement in translation quality (BLEU) using AI post-editing in workflow (study result)
10
20% improvement in customer ticket resolution times with AI routing (reported)
11
0.8% relative improvement in language model perplexity after domain adaptation (paper result)
12
2.2x higher recall for malware detection using deep learning vs. traditional signatures (study result)
13
5% of enterprises report AI improving decision speed by more than 50% (surveyed)
14
1.6x faster time-to-market for teams using AI-assisted coding tools (developer survey)
15
24% reduction in software defect density after AI-assisted testing adoption (reported)
16
29% improvement in data labeling throughput using AI-assisted annotation (reported)
17
75% of analysts report reduced time searching for relevant information with AI search tools (surveyed)
18
2.0x faster customer identity verification with AI document processing (reported)
19
95% document extraction accuracy reported in production for AI OCR (case metric)
20
80%+ accuracy targets for AI OCR in languages supported by platform (documented threshold)
Interpretation
Performance Metrics Interpretation
Across the board, AI is driving measurable performance gains, including a 60% latency reduction from speculative decoding and a 30% higher conversion rate from personalized recommendations, signaling that practical optimizations are delivering near-term impact in information industry workflows.
05 · Category
User Adoption22 stats
01
23% of organizations report AI adoption in at least one core business process (surveyed)
02
33% of enterprises report using AI in at least one function (surveyed)
03
11% of enterprises report using AI in at least one business process continuously (surveyed)
04
14% of enterprises using AI provide AI-based decision support (surveyed share)
05
10% of enterprises use AI for marketing or advertising (surveyed)
06
5% of enterprises use AI for sales and supply chain (surveyed)
07
22% of enterprises in the EU used AI in 2022 (share)
08
17% of enterprises in the EU used cloud computing for AI in 2022 (share)
09
29% of organizations use AI for data preparation (surveyed)
10
31% of organizations use AI for analytics insights (surveyed)
11
27% of organizations use AI for predictive modeling (surveyed)
12
23% of organizations use AI for automated reporting (surveyed)
13
16% of organizations have adopted generative AI in at least one business unit (surveyed)
14
11% of organizations have deployed generative AI in production (surveyed)
15
31% of organizations plan to deploy generative AI within 12 months (surveyed)
16
7% of organizations report having policies for generative AI usage (surveyed)
17
19% of organizations have launched generative AI training for employees (surveyed)
18
35% of organizations using AI have deployed it for document processing (surveyed)
19
18% of organizations use AI for identity verification in regulated environments (surveyed)
20
24% of organizations use AI for call-center transcription and summarization (surveyed)
21
31% of organizations use AI for cybersecurity alert triage (surveyed)
22
9% of global organizations used AI in 2022 (surveyed) - low adoption baseline
Interpretation
User Adoption Interpretation
Even though AI is used by at least one core process in 23% of organizations, only 11% use it continuously while generative AI adoption is still nascent at 16% in at least one business unit and just 11% in production, even as 31% plan to deploy it within 12 months.
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
Elif Demirci. (2026, February 13). AI In The Information Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-information-industry-statistics
MLA
Elif Demirci. "AI In The Information Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-information-industry-statistics.
Chicago
Elif Demirci. 2026. "AI In The Information Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-information-industry-statistics.
Sources & references
51 datasets cited across this report · attribution is report-level
+24 additional datasets cited (not shown individually)