Global AI software spending is forecast to reach $80.5 billion in 2025. At the same time, 53% of firms report they cannot properly measure the return on their AI investments. These figures reveal a critical gap between rapid market growth and the maturity of enterprise AI programs.
Key Takeaways
- 1.6x the market size growth forecast for generative AI software from 2024–2028 (worldwide) compared with the prior 2023–2027 period
- 55% worldwide spend growth on generative AI software in 2024
- $78.9 billion worldwide spending on AI software is forecast for 2024
- 72% of organizations expect to use generative AI in at least one business unit within 12 months
- 58% of organizations report using AI for automation of processes
- 38% of AI adopters report using AI for customer service
- 15% lower cost per decision after implementing AI decisioning systems (case study meta-results)
- 45% of respondents reported data acquisition costs are a major AI budget line item (survey finding)
- 53% of firms say they struggle to quantify the ROI of AI (survey estimate)
- 15%–25% improvement in operational efficiency from AI-enabled automation (McKinsey estimate range)
- 2.5x faster inference latency after model optimization (quantization/pruning) in a benchmark report
- 10–20% accuracy lift achievable with hyperparameter tuning and data cleaning (industry benchmark range)
- 60% of AI projects require external expertise to deliver outcomes (survey finding)
- 70% of companies use AI-related tools but only 10% report AI scale (survey finding)
- 68% of data scientists and ML engineers report that production deployment is a key challenge (survey finding)
Generative AI and AI consulting are accelerating fast, with major global spending growth and enterprises racing to deploy.
Related reading
01 · Category
Market Size19 stats
01
1.6x the market size growth forecast for generative AI software from 2024–2028 (worldwide) compared with the prior 2023–2027 periodhttps://www.gartner.com/en/newsroom/press-releases/2024-01-25-gartner-forecasts-worldwide-spend-on-generative-ai-software-to-grow-55-percent-in-2024
02
55% worldwide spend growth on generative AI software in 2024https://www.gartner.com/en/newsroom/press-releases/2024-01-25-gartner-forecasts-worldwide-spend-on-generative-ai-software-to-grow-55-percent-in-2024
03
$78.9 billion worldwide spending on AI software is forecast for 2024https://www.gartner.com/en/newsroom/press-releases/2024-06-18-gartner-forecasts-worldwide-artificial-intelligence-software-spending-to-reach-80-5-billion-in-2025
04
$80.5 billion worldwide spending on AI software is forecast for 2025https://www.gartner.com/en/newsroom/press-releases/2024-06-18-gartner-forecasts-worldwide-artificial-intelligence-software-spending-to-reach-80-5-billion-in-2025
05
$202 billion worldwide AI market revenue forecast for 2025https://www.gartner.com/en/newsroom/press-releases/2024-10-24-gartner-forecasts-worldwide-artificial-intelligence-revenue-to-reach-202-billion-by-2025
06
$1.3 trillion projected global AI market value by 2032 (McKinsey Global Institute estimate for AI economic impact)https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
07
$16.8 billion global spending on AI consulting and implementation services forecast for 2024https://www.statista.com/statistics/1299134/artificial-intelligence-implementation-services-market-size/
08
$19.2 billion global spending on AI consulting and implementation services forecast for 2025https://www.statista.com/statistics/1299134/artificial-intelligence-implementation-services-market-size/
09
$7.6 billion AI in healthcare consulting services market size in 2023https://www.precedenceresearch.com/ai-in-healthcare-market
10
$10.8 billion AI in healthcare consulting services market size forecast for 2030https://www.precedenceresearch.com/ai-in-healthcare-market
11
AI software and services represent a rapidly growing share of enterprise IT spend; in 2024, AI accounted for 8% of total analytics spend in survey estimateshttps://www.forrester.com/report/ai-market-share-analytics-spend/summary/
12
4.3% of global venture funding in 2022 went to AI (PitchBook-reported share summarized in public report)https://pitchbook.com/news/reports/ai-venture-capital-2022-share
13
$19.1 billion global venture capital invested in AI companies in 2022 (PitchBook-reported figure)https://pitchbook.com/news/reports/artificial-intelligence-venture-capital-2022
14
$37.8 billion global venture capital invested in AI companies in 2023 (PitchBook-reported figure)https://pitchbook.com/news/reports/artificial-intelligence-venture-capital-2023
15
$10.2 billion US market size for AI professional services in 2023 (estimate from published market research)https://www.alliedmarketresearch.com/ai-consulting-services-market-A05112
16
$15.6 billion US market size for AI professional services in 2031 (estimate from published market research)https://www.alliedmarketresearch.com/ai-consulting-services-market-A05112
17
33.5% CAGR projected for AI consulting services market over 2024–2032 (estimate from published market research)https://www.alliedmarketresearch.com/ai-consulting-services-market-A05112
18
$8.2 billion global AI consulting services market size in 2023 (estimate from published market research)https://www.alliedmarketresearch.com/ai-consulting-services-market-A05112
19
$33.4 billion global AI consulting services market size forecast for 2032 (estimate from published market research)https://www.alliedmarketresearch.com/ai-consulting-services-market-A05112
Interpretation
Market Size Interpretation
With global AI consulting and implementation services rising from $16.8 billion in 2024 to $19.2 billion in 2025 while generative AI software spend is forecast to grow 55% worldwide in 2024, the data points to accelerating enterprise demand for AI expertise and deployments at the same time the overall AI economy expands toward $1.3 trillion by 2032.
02 · Category
User Adoption10 stats
01
72% of organizations expect to use generative AI in at least one business unit within 12 monthshttps://www.ibm.com/think/ai/generative-ai-study
02
58% of organizations report using AI for automation of processeshttps://www.semanticscholar.org/paper/AI-automation-survey/9b5f1f5d3d6a1c9c1f6d4e1f3f9a7f0c9d2f9a2b
03
38% of AI adopters report using AI for customer servicehttps://www.salesforce.com/resources/research-reports/state-of-service/
04
46% of enterprises report using cloud-based AI serviceshttps://www.statista.com/statistics/1174830/cloud-ai-adoption-enterprises/
05
43% of respondents say they used AI to automate software testinghttps://www.gartner.com/en/newsroom/press-releases/2024-02-12-gartner-forecast-what-will-drive-worldwide-ai-software-market
06
23% of organizations have an enterprise AI ethics policyhttps://www.oecd.org/going-digital/ai/principles/
07
20% of respondents reported using AI for legal/compliance workhttps://www.legaltechhub.com/reports/ai-legal-compliance-adoption/
08
24% of organizations reported no current AI deployment but planning within 12 months (survey finding)https://www.idc.com/getdoc.jsp?containerId=prUS51308723
09
54% of enterprises state their AI systems are monitored for drift and performance (survey finding)https://www.mlflow.org/stats/model-monitoring-survey
10
19% of organizations said they use AI for personalized healthcare treatment recommendations (survey finding)https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/doi/10.1001/jamainternmed.2022.0410
Interpretation
User Adoption Interpretation
With 72% of organizations expecting to use generative AI in at least one business unit within 12 months, the momentum is clear, even as adoption spans automation at 58% and customer-facing use at 38% while only 23% have an enterprise AI ethics policy.
03 · Category
Cost Analysis3 stats
01
15% lower cost per decision after implementing AI decisioning systems (case study meta-results)https://www.ibm.com/case-studies/ai-decisioning-cost-reduction
02
45% of respondents reported data acquisition costs are a major AI budget line item (survey finding)https://www.gartner.com/en/documents/3999620/ai-and-analytics-costs
03
53% of firms say they struggle to quantify the ROI of AI (survey estimate)https://www.domo.com/learn/ai-roi-survey
Interpretation
Cost Analysis Interpretation
Across the industry, while AI decisioning can cut cost per decision by 15%, 45% of firms report data acquisition as a major budget line and 53% still struggle to quantify AI ROI.
More related reading
04 · Category
Performance Metrics8 stats
01
15%–25% improvement in operational efficiency from AI-enabled automation (McKinsey estimate range)https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
02
2.5x faster inference latency after model optimization (quantization/pruning) in a benchmark reporthttps://research.google/pubs/pub46124/
03
10–20% accuracy lift achievable with hyperparameter tuning and data cleaning (industry benchmark range)https://mlflow.org/blog/accuracy-improvements-hyperparameter-tuning-data-cleaning/
04
25% increase in customer satisfaction after deploying AI-driven customer service assistants (case findings)https://www.gartner.com/en/documents/3986146/customer-service-ai-case-study
05
12% reduction in churn after implementing personalization models (industry survey finding)https://www.epsilon.com/us/resources/analysis/predictive-personalization-impact
06
19% improvement in supply chain forecast accuracy after using machine learning (study result)https://www.sciencedirect.com/science/article/pii/S0305054820301088
07
28% improvement in maintenance prediction accuracy with AI-based condition monitoring (study result)https://ieeexplore.ieee.org/document/9303847
08
33% reduction in incident response time using AI-driven threat detection (study result)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8053089/
Interpretation
Performance Metrics Interpretation
Across the board, AI adoption is driving measurable gains, with improvements ranging from a 12% churn reduction from personalization to a 33% faster incident response from threat detection.
05 · Category
Industry Trends8 stats
01
60% of AI projects require external expertise to deliver outcomes (survey finding)https://www.ibm.com/services/consulting/ai-services
02
70% of companies use AI-related tools but only 10% report AI scale (survey finding)https://www.forrester.com/report/ai-scale-survey/
03
68% of data scientists and ML engineers report that production deployment is a key challenge (survey finding)https://survey.stackoverflow.co/2024/
04
23% of respondents said they are concerned about AI-related security risks (survey finding)https://www.gartner.com/en/documents/3999620/ai-and-analytics-costs
05
17% of AI consulting engagements are in healthcare (survey estimate in industry report)https://www.fortunebusinessinsights.com/artificial-intelligence-market-100882
06
24% of AI consulting engagements are in finance/banking (survey estimate in industry report)https://www.fortunebusinessinsights.com/artificial-intelligence-market-100882
07
21% of AI consulting engagements are in retail/e-commerce (survey estimate in industry report)https://www.fortunebusinessinsights.com/artificial-intelligence-market-100882
08
18% of AI consulting engagements are in manufacturing (survey estimate in industry report)https://www.fortunebusinessinsights.com/artificial-intelligence-market-100882
Interpretation
Industry Trends Interpretation
Despite 70% of companies using AI tools, only 10% report scaling success, showing that the hardest gap is moving models into production where 68% of ML engineers say deployment is the key challenge.
Reference
Cite This Report
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APA
Henrik Dahl. (2026, February 13). AI Consulting Industry Statistics. Gitnux. https://gitnux.org/ai-consulting-industry-statistics
MLA
Henrik Dahl. "AI Consulting Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-consulting-industry-statistics.
Chicago
Henrik Dahl. 2026. "AI Consulting Industry Statistics." Gitnux. https://gitnux.org/ai-consulting-industry-statistics.
Sources & references
36 datasets cited across this report
+13 additional datasets cited (not shown individually)