Gitnux/Report 2026

AI in the Information Industry Statistics

Generative AI is already deployed by 46% of executives—find out how that translates into real productivity gains for 64% of teams.
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July 21, 2026Updated
AI in the Information Industry Statistics
Elif DemirciWritten by Elif Demirci·Edited by Gabrielle Fontaine·Fact-checked by Astrid Bergmann
Published ·Updated
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AI in the information industry is moving beyond experiments and reshaping how work gets done across business processes. Executives and teams report tangible impact, including productivity or performance improvements (64%) and faster creation workflows such as content generation (55%). In the pages ahead, you’ll also see where AI is applied—like software development (37%), cost reduction (48%), and revenue growth (42%)—and what results organizations measure to scale benefits responsibly.

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)

Generative AI adoption is accelerating, with major productivity gains and major growth for AI in software through 2030.

Related reading

01 · Category

Market Size30 stats

01
22.1% CAGR for the global AI in software market forecast for 2024–2030[1]
02
$84.0 billion global market size for AI in software in 2023[1]
03
$268.0 billion expected global market size for AI in software by 2030[1]
04
38% CAGR for the global AI software market forecast for 2024–2030[2]
05
$16.5 billion global market size for artificial intelligence software in 2023[2]
06
$110.1 billion expected global market size for artificial intelligence software by 2032[2]
07
$14.3 billion global market size for AI in IT operations in 2023[2]
08
37.1% CAGR for AI in IT operations forecast for 2024–2032[2]
09
$134.1 billion expected global market size for AI in IT operations by 2032[2]
10
$27.8 billion global market size for AI in cybersecurity in 2023[2]
11
41.2% CAGR for AI in cybersecurity forecast for 2024–2032[2]
12
$223.2 billion expected global market size for AI in cybersecurity by 2032[2]
13
$16.1 billion global market size for AI in finance in 2023[2]
14
43.3% CAGR for AI in finance forecast for 2024–2032[2]
15
$177.4 billion expected global market size for AI in finance by 2032[2]
16
$1.2 trillion global investment in AI 2023–2027 (projected total)[3]
17
$554 billion worldwide AI spending forecast for 2023[3]
18
$679.1 billion worldwide AI spending forecast for 2024[3]
19
$1.6 trillion worldwide AI spending projected by 2030[3]
20
$77.1 billion global market size for generative AI in 2023 (forecast)[4]
21
$593.6 billion expected global market size for generative AI by 2030 (forecast)[4]
22
28.4% CAGR for the generative AI market forecast for 2024–2032[1]
23
$14.0 billion global generative AI market size in 2023 (forecast)[1]
24
$110.0 billion expected global generative AI market size by 2030 (forecast)[1]
25
$7.6 billion global market size for AI in customer service in 2023 (forecast)[5]
26
$24.4 billion expected global market size for AI in customer service by 2030 (forecast)[5]
27
31.8% CAGR for AI in customer service market forecast 2024–2030[5]
28
$18.0 billion global market size for AI in IT operations in 2022 (forecast baseline)[1]
29
$69.8 billion expected global market size for AI in IT operations by 2030[1]
30
16.8% CAGR for AI in IT operations forecast 2024–2030[1]
Interpretation

Market Size Interpretation

The market size data shows AI software is on a steep growth trajectory, rising from $84.0 billion in 2023 to an expected $268.0 billion by 2030 with a 22.1% CAGR, which strongly signals rapid expansion in the information industry’s AI spending.

03 · Category

Cost Analysis15 stats

01
48% of respondents say they are using AI to reduce costs (surveyed)[8]
02
42% of respondents say they are using AI to improve revenue (surveyed)[8]
03
10% to 30% potential savings from AI-enabled operations improvements (range stated)[6]
04
35% of organizations report lower IT support costs after implementing AI for IT operations (surveyed)[3]
05
25% of organizations report fewer ticket escalations after AI deployment in IT service desks (surveyed)[3]
06
20% reduction in manual document processing costs with AI document understanding (case-based)[9]
07
$3.4 million average annual savings from using AI in customer service operations (reported average)[9]
08
8% average cost reduction from migrating analytics workloads to optimized infrastructure (reported by study)[10]
09
15% reduction in mean time to detect (MTTD) leading to reduced breach response costs (reported)[9]
10
19% reduction in average handling time (AHT) for customer support with AI chatbots (reported)[11]
11
16% cost savings from AI knowledge management adoption (surveyed)[12]
12
13% reduction in fraud false positives cost with AI scoring (reported by vendor benchmark)[13]
13
8% reduction in chargebacks with AI/ML-based fraud detection (case metric)[13]
14
27% reduction in IT operational costs with AIOps (surveyed)[3]
15
22% reduction in operational risk cost from AI-driven monitoring (surveyed)[3]
Interpretation

Cost Analysis Interpretation

For the cost analysis angle, the data shows a clear theme of measurable savings, with 48% of respondents using AI to reduce costs and additional reported reductions such as 35% lower IT support costs and 20% lower manual document processing costs.

More related reading

04 · Category

Performance Metrics20 stats

01
30% higher conversion rate with personalized AI recommendations (reported A/B results)[14]
02
10% to 15% higher average order value with product recommendations (range reported)[14]
03
25% increased recommendation relevance with contextual bandit optimization (benchmark)[15]
04
33% improved model accuracy from feature selection automation (reported)[9]
05
1.3x improvement in F1 score for entity extraction using transformers over baseline (paper result)[16]
06
2.6x increase in throughput with parallelized inference optimization for LLMs (performance study result)[16]
07
60% reduction in latency via speculative decoding (reported in optimization study)[16]
08
10–100x speedup with FlashAttention compared to naive attention (range stated in paper)[16]
09
15% improvement in translation quality (BLEU) using AI post-editing in workflow (study result)[17]
10
20% improvement in customer ticket resolution times with AI routing (reported)[3]
11
0.8% relative improvement in language model perplexity after domain adaptation (paper result)[16]
12
2.2x higher recall for malware detection using deep learning vs. traditional signatures (study result)[18]
13
5% of enterprises report AI improving decision speed by more than 50% (surveyed)[3]
14
1.6x faster time-to-market for teams using AI-assisted coding tools (developer survey)[19]
15
24% reduction in software defect density after AI-assisted testing adoption (reported)[16]
16
29% improvement in data labeling throughput using AI-assisted annotation (reported)[20]
17
75% of analysts report reduced time searching for relevant information with AI search tools (surveyed)[21]
18
2.0x faster customer identity verification with AI document processing (reported)[22]
19
95% document extraction accuracy reported in production for AI OCR (case metric)[10]
20
80%+ accuracy targets for AI OCR in languages supported by platform (documented threshold)[23]
Interpretation

Performance Metrics Interpretation

Across performance metrics in the information industry, AI-driven personalization and optimization are delivering measurable gains such as a 30% higher conversion rate and up to 2.6x throughput improvements, showing that these systems can reliably enhance real outcomes rather than just model quality.

05 · Category

User Adoption22 stats

01
23% of organizations report AI adoption in at least one core business process (surveyed)[24]
02
33% of enterprises report using AI in at least one function (surveyed)[24]
03
11% of enterprises report using AI in at least one business process continuously (surveyed)[24]
04
14% of enterprises using AI provide AI-based decision support (surveyed share)[24]
05
10% of enterprises use AI for marketing or advertising (surveyed)[25]
06
5% of enterprises use AI for sales and supply chain (surveyed)[25]
07
22% of enterprises in the EU used AI in 2022 (share)[25]
08
17% of enterprises in the EU used cloud computing for AI in 2022 (share)[25]
09
29% of organizations use AI for data preparation (surveyed)[26]
10
31% of organizations use AI for analytics insights (surveyed)[26]
11
27% of organizations use AI for predictive modeling (surveyed)[26]
12
23% of organizations use AI for automated reporting (surveyed)[26]
13
16% of organizations have adopted generative AI in at least one business unit (surveyed)[8]
14
11% of organizations have deployed generative AI in production (surveyed)[8]
15
31% of organizations plan to deploy generative AI within 12 months (surveyed)[8]
16
7% of organizations report having policies for generative AI usage (surveyed)[8]
17
19% of organizations have launched generative AI training for employees (surveyed)[8]
18
35% of organizations using AI have deployed it for document processing (surveyed)[22]
19
18% of organizations use AI for identity verification in regulated environments (surveyed)[22]
20
24% of organizations use AI for call-center transcription and summarization (surveyed)[3]
21
31% of organizations use AI for cybersecurity alert triage (surveyed)[3]
22
9% of global organizations used AI in 2022 (surveyed) - low adoption baseline[27]
Interpretation

User Adoption Interpretation

User adoption is still early and uneven, with only 33% of enterprises using AI in at least one function and just 11% using it continuously in a business process.
Reference

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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

27 sources cited across this report

  1. fortunebusinessinsights.com
  2. precedenceresearch.com
  3. gartner.com
  4. gminsights.com
  5. grandviewresearch.com
  6. mckinsey.com
  7. ciodive.com
  8. forrester.com
  9. ibm.com
  10. cloud.google.com
  11. salesforce.com
  12. g2.com
  13. fico.com
  14. thinkwithgoogle.com
  15. ai.googleblog.com
  16. arxiv.org
  17. aclanthology.org
  18. sciencedirect.com
  19. gitlab.com
  20. scale.com
  21. oversight.ai
  22. lexisnexis.com
  23. learn.microsoft.com
  24. oecd.org
  25. ec.europa.eu
  26. domo.com
  27. unctad.org