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 period
02
55% worldwide spend growth on generative AI software in 2024
03
$78.9 billion worldwide spending on AI software is forecast for 2024
04
$80.5 billion worldwide spending on AI software is forecast for 2025
05
$202 billion worldwide AI market revenue forecast for 2025
06
$1.3 trillion projected global AI market value by 2032 (McKinsey Global Institute estimate for AI economic impact)
07
$16.8 billion global spending on AI consulting and implementation services forecast for 2024
08
$19.2 billion global spending on AI consulting and implementation services forecast for 2025
09
$7.6 billion AI in healthcare consulting services market size in 2023
10
$10.8 billion AI in healthcare consulting services market size forecast for 2030
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 estimates
12
4.3% of global venture funding in 2022 went to AI (PitchBook-reported share summarized in public report)
13
$19.1 billion global venture capital invested in AI companies in 2022 (PitchBook-reported figure)
14
$37.8 billion global venture capital invested in AI companies in 2023 (PitchBook-reported figure)
15
$10.2 billion US market size for AI professional services in 2023 (estimate from published market research)
16
$15.6 billion US market size for AI professional services in 2031 (estimate from published market research)
17
33.5% CAGR projected for AI consulting services market over 2024–2032 (estimate from published market research)
18
$8.2 billion global AI consulting services market size in 2023 (estimate from published market research)
19
$33.4 billion global AI consulting services market size forecast for 2032 (estimate from published market research)
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 months
02
58% of organizations report using AI for automation of processes
03
38% of AI adopters report using AI for customer service
04
46% of enterprises report using cloud-based AI services
05
43% of respondents say they used AI to automate software testing
06
23% of organizations have an enterprise AI ethics policy
07
20% of respondents reported using AI for legal/compliance work
08
24% of organizations reported no current AI deployment but planning within 12 months (survey finding)
09
54% of enterprises state their AI systems are monitored for drift and performance (survey finding)
10
19% of organizations said they use AI for personalized healthcare treatment recommendations (survey finding)
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)
02
45% of respondents reported data acquisition costs are a major AI budget line item (survey finding)
03
53% of firms say they struggle to quantify the ROI of AI (survey estimate)
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)
02
2.5x faster inference latency after model optimization (quantization/pruning) in a benchmark report
03
10–20% accuracy lift achievable with hyperparameter tuning and data cleaning (industry benchmark range)
04
25% increase in customer satisfaction after deploying AI-driven customer service assistants (case findings)
05
12% reduction in churn after implementing personalization models (industry survey finding)
06
19% improvement in supply chain forecast accuracy after using machine learning (study result)
07
28% improvement in maintenance prediction accuracy with AI-based condition monitoring (study result)
08
33% reduction in incident response time using AI-driven threat detection (study result)
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)
02
70% of companies use AI-related tools but only 10% report AI scale (survey finding)
03
68% of data scientists and ML engineers report that production deployment is a key challenge (survey finding)
04
23% of respondents said they are concerned about AI-related security risks (survey finding)
05
17% of AI consulting engagements are in healthcare (survey estimate in industry report)
06
24% of AI consulting engagements are in finance/banking (survey estimate in industry report)
07
21% of AI consulting engagements are in retail/e-commerce (survey estimate in industry report)
08
18% of AI consulting engagements are in manufacturing (survey estimate in industry report)
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 · attribution is report-level
+13 additional datasets cited (not shown individually)