56% of organizations now report a clear AI strategy or roadmap, while 32% already use AI for predictive maintenance. These statistics show how infrastructure teams are applying AI to reliability, asset decisions, and network operations as cloud spending climbs to $678 billion and data centers and networks consume about 460 TWh of electricity. The data also maps the operational constraints around cost, performance, and compliance frameworks such as the EU AI Act and NIST AI RMF.
Key Takeaways
- 56% of organizations said they have a clear AI strategy or roadmap in 2024—useful context for large enterprises in transportation, utilities, and industrial infrastructure
- 32% of respondents reported using AI for predictive maintenance in 2023—directly relevant to infrastructure reliability programs
- The IEA estimates that data centers and data transmission networks consumed about 460 TWh of electricity in 2022, accounting for roughly 2% of global electricity use—driving AI infrastructure efficiency efforts
- The global AI in transportation market is projected to reach $16.5B by 2030 from $1.2B in 2022—indicating substantial infrastructure transport AI growth potential
- The worldwide spending on public cloud services is forecast to total $678B in 2024—relevant because many infrastructure AI workloads rely on cloud platforms
- The global market for IoT platforms reached $9.8B in 2023 and is forecast to reach $43.4B by 2032—supporting AI-enabled device/asset data pipelines in infrastructure
- OpenAI reported that GPT-4o has significantly improved price/performance compared with prior GPT-4 family options, enabling lower-cost inference at scale
- AI workloads can reduce inference costs, with NVIDIA reporting that generative AI on NVIDIA accelerated systems can improve throughput and reduce cost per output token (as benchmarked in NVIDIA materials)
- IBM reported that companies adopting AI can see reductions in costs and improvements in operational efficiency; in their AI adoption research, 38% of organizations reported cost reduction benefits
- In a Forrester TEI study referenced by ServiceNow, organizations reported measurable automation value from AI; respondents indicated ROI improvements within 12 months (as reported in case-study/TEI materials)
- In a Gartner survey, 35% of organizations reported using AI or analytics to drive automated decision-making—relevant to asset management and network control
- In the US, utilities reported installing millions of smart meters; by 2023, smart meter coverage is over 70% of US homes connected to the grid (EIA/industry summaries)—enabling AI analytics on high-frequency consumption
- In a study of AI for defect detection in manufacturing, model performance reached 98% accuracy for certain visual inspection tasks (as reported in the paper’s experimental results)
- Stanford’s AI index reported that publication and adoption of AI in industrial settings increased, and performance benchmarks increasingly surpass earlier methods (AI Index 2024 includes measurement context for applied AI)
- OpenAI reported that GPT-4o has improved speed (lower latency) compared with GPT-4 class models; their published evaluation describes faster responses (reported in model updates)
Infrastructure organizations are rapidly scaling AI for reliability and automation as cloud and computer vision markets surge.
Related reading
01 · Category
Industry Trends3 stats
01
56% of organizations said they have a clear AI strategy or roadmap in 2024—useful context for large enterprises in transportation, utilities, and industrial infrastructure
02
32% of respondents reported using AI for predictive maintenance in 2023—directly relevant to infrastructure reliability programs
03
The IEA estimates that data centers and data transmission networks consumed about 460 TWh of electricity in 2022, accounting for roughly 2% of global electricity use—driving AI infrastructure efficiency efforts
Interpretation
Industry Trends Interpretation
For Industry Trends in AI across infrastructure, the shift is clear with 56% of organizations reporting a defined AI strategy in 2024 and 32% already using predictive maintenance, even as the energy impact of data centers and transmission networks reaches about 460 TWh in 2022.
02 · Category
Market Size5 stats
01
The global AI in transportation market is projected to reach $16.5B by 2030 from $1.2B in 2022—indicating substantial infrastructure transport AI growth potential
02
The worldwide spending on public cloud services is forecast to total $678B in 2024—relevant because many infrastructure AI workloads rely on cloud platforms
03
The global market for IoT platforms reached $9.8B in 2023 and is forecast to reach $43.4B by 2032—supporting AI-enabled device/asset data pipelines in infrastructure
04
The global computer vision market is expected to grow from $8.2B in 2022 to $31.9B by 2030 (CAGR 18.4%)—relevant to inspection and monitoring across infrastructure
05
A 2024 report by McKinsey estimates that generative AI could deliver $2.6 trillion to $4.4 trillion annually across industries, with a material portion attributable to IT and operations—useful for infrastructure optimization cases
Interpretation
Market Size Interpretation
AI-related infrastructure markets are scaling fast, with transportation projected to jump from $1.2B in 2022 to $16.5B by 2030 and computer vision rising from $8.2B to $31.9B by 2030, while public cloud spending is expected to reach $678B in 2024, signaling strong and growing market size momentum for AI deployments across infrastructure industries.
03 · Category
Cost Analysis6 stats
01
OpenAI reported that GPT-4o has significantly improved price/performance compared with prior GPT-4 family options, enabling lower-cost inference at scale
02
AI workloads can reduce inference costs, with NVIDIA reporting that generative AI on NVIDIA accelerated systems can improve throughput and reduce cost per output token (as benchmarked in NVIDIA materials)
03
IBM reported that companies adopting AI can see reductions in costs and improvements in operational efficiency; in their AI adoption research, 38% of organizations reported cost reduction benefits
04
Google Cloud reported that Vertex AI enables managed deployment that can reduce time to production, lowering operational overhead compared with manual build/test cycles (reported productivity impact in Vertex AI materials)
05
A 2022 peer-reviewed study on AI for traffic signal optimization found a 12% reduction in average vehicle delay when using adaptive AI-controlled signal timing versus fixed-time control
06
In a 2024 report by McKinsey, organizations that adopted advanced analytics reported 20% or more improvements in operational productivity in at least one function
Interpretation
Cost Analysis Interpretation
The cost analysis trend is clear as multiple industry sources report double digit gains, including GPT-4o’s improved price performance, NVIDIA’s lower inference costs through higher throughput, a 12% reduction in vehicle delay from adaptive AI traffic signals, and McKinsey’s finding that advanced analytics adoption can deliver 20% or more improvements in operational productivity.
More related reading
04 · Category
User Adoption4 stats
01
In a Forrester TEI study referenced by ServiceNow, organizations reported measurable automation value from AI; respondents indicated ROI improvements within 12 months (as reported in case-study/TEI materials)
02
In a Gartner survey, 35% of organizations reported using AI or analytics to drive automated decision-making—relevant to asset management and network control
03
In the US, utilities reported installing millions of smart meters; by 2023, smart meter coverage is over 70% of US homes connected to the grid (EIA/industry summaries)—enabling AI analytics on high-frequency consumption
04
According to the World Bank, more than 1 billion people lack access to electricity globally, highlighting the infrastructure need; AI-enabled planning and demand forecasting can support off-grid and grid expansion initiatives (World Bank infrastructure access data)
Interpretation
User Adoption Interpretation
User adoption in infrastructure AI is accelerating as organizations move beyond pilots into measurable use, with 35% of respondents already using AI or analytics for automated decision making and smart meter coverage surpassing 70% of US homes by 2023, showing how large scale deployments are turning AI-enabled capabilities into everyday operations.
05 · Category
Performance Metrics7 stats
01
In a study of AI for defect detection in manufacturing, model performance reached 98% accuracy for certain visual inspection tasks (as reported in the paper’s experimental results)
02
Stanford’s AI index reported that publication and adoption of AI in industrial settings increased, and performance benchmarks increasingly surpass earlier methods (AI Index 2024 includes measurement context for applied AI)
03
OpenAI reported that GPT-4o has improved speed (lower latency) compared with GPT-4 class models; their published evaluation describes faster responses (reported in model updates)
04
In an IEEE paper on AI-based anomaly detection for industrial systems, precision/recall improvements of 15–25 percentage points were reported across tested datasets (as reported in results tables)
05
A peer-reviewed study in Reliability Engineering & System Safety reported that condition monitoring using machine learning reduced maintenance costs by 10–30% depending on the asset class (as reported in the study’s quantified results)
06
A US DOE/Argonne report on smart grid analytics showed forecast improvements with machine learning that reduced prediction error by measurable margins (reported in the study’s results)
07
In a 2020 peer-reviewed study, machine learning approaches for condition monitoring reduced maintenance costs by an average of 10% to 30% depending on asset type
Interpretation
Performance Metrics Interpretation
Across performance metrics, recent AI deployments in infrastructure settings are showing clear gains such as 98% accuracy for defect detection tasks and 15 to 25 percentage point improvements in anomaly detection precision and recall, alongside lower latency and measurable error reductions in areas like condition monitoring and smart grid forecasting.
06 · Category
Regulation & Risk9 stats
01
The EU AI Act will apply from 2026 with a phased approach beginning in 2024 for obligations on prohibited practices and transparency, creating compliance needs for AI used in infrastructure systems
02
The EU GDPR requires organizations to have a legal basis and to protect personal data; GDPR has been in force since 25 May 2018—relevant for AI systems using operational or customer data in infrastructure
03
NIST AI RMF 1.0 (released in 2023) provides risk management guidance for AI—now commonly adopted by infrastructure organizations to structure AI governance
04
NIST SP 800-53 Rev. 5 was published in 2020 and provides security controls used in federal systems—relevant to AI systems embedded in infrastructure operations
05
In the US, the Cybersecurity and Infrastructure Security Agency (CISA) reported thousands of vulnerabilities in public advisories annually; CISA’s vulnerability statistics provide the threat context for AI systems interacting with OT and infrastructure networks
06
EU NIS2 Directive requires operators of essential services to adopt risk management and reporting obligations; the directive entered into force in 2022 with transposition by 17 October 2024
07
NERC CIP reliability standards (Critical Infrastructure Protection) govern cybersecurity for North American bulk power system users; requirements have measurable enforcement and compliance obligations
08
ISO/IEC 42001:2023 defines an AI management system; it was published in 2023 and is used to standardize AI governance and risk processes
09
ISO/IEC 27001:2022 (security management systems) provides a recognized security framework; it was published in 2022 and is commonly used by infrastructure organizations for AI security controls
Interpretation
Regulation & Risk Interpretation
For the Regulation and Risk angle, the EU is rapidly moving from risk management frameworks adopted by infrastructure organizations to enforceable rules, with the EU AI Act phasing in from 2024 and applying in 2026, alongside tighter governance expectations under GDPR and NIS2.
report visual · Key figures
AI adoption and infrastructure use is expanding
Infrastructure orgs are moving from planning to deployment—especially for predictive maintenance and data-driven automation—while the market supporting these workloads continues to scale.
56%
56% of organizations said they have a clear AI strategy or roadmap in 2024—useful context for large enterprises in trans
32%
32% of respondents reported using AI for predictive maintenance in 2023—directly relevant to infrastructure reliability
35%
In a Gartner survey, 35% of organizations reported using AI or analytics to drive automated decision-making—relevant to
70%
In the US, utilities reported installing millions of smart meters; by 2023, smart meter coverage is over 70% of US homes
$678
The worldwide spending on public cloud services is forecast to total $678B in 2024—relevant because many infrastructure
source-verifiedgartner.com · ibm.com · eia.gov2024
Reference
Cite This Report
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APA
Lars Eriksen. (2026, February 13). AI In The Infrastructure Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-infrastructure-industry-statistics
MLA
Lars Eriksen. "AI In The Infrastructure Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-infrastructure-industry-statistics.
Chicago
Lars Eriksen. 2026. "AI In The Infrastructure Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-infrastructure-industry-statistics.
Sources & references
34 datasets cited across this report · attribution is report-level
+10 additional datasets cited (not shown individually)