The global AI software market is projected at $92.0 billion and expected to reach $227.7 billion by 2030. High tech adoption is already broad, with 51% of executives reporting AI in production or pilot use. This dataset tracks the market growth, spending, governance, and cost pressures shaping that shift.
Key Takeaways
- $92.0 billion 2024 global market size for AI software, projected to reach $227.7 billion by 2030 (CAGR ~16.4%)
- $15.9 billion global AI chip market size in 2023, projected to reach $185.1 billion by 2030
- $19.0 billion global AI in healthcare market size in 2023, projected to grow to $188.5 billion by 2030
- 51% of executives reported AI is already deployed in their organizations (or pilots in progress) according to IBM’s 2023 global survey of executives
- 63% of enterprises in IBM’s 2023 study reported they had adopted AI or planned to adopt it within 2 years
- IBM’s 2023 report found that 57% of organizations say they are using AI for forecasting demand and planning resources
- In the NIST AI Risk Management Framework, 4 key functions (Govern, Map, Measure, Manage) are defined to help organizations manage AI risks
- The EU AI Act requires providers of high-risk AI systems to perform conformity assessments before placing them on the market (per EU summary)
- OECD’s 2019 AI Principles include 5 values-based principles and 1 recommendation section to promote trustworthy AI
- Global AI M&A deal value reached $29.6 billion in 2023 per PitchBook’s State of AI 2023 report
- Google Cloud Vertex AI pricing for training is based on compute hours (e.g., cost per hour varies by machine type)
- AWS Amazon Bedrock pricing charges per input and output token depending on model, with costs varying by provider model
- In 2024, estimates from public cloud cost analyses indicated that training and fine-tuning LLMs can cost from tens of thousands to millions of dollars per project depending on compute and data scale
- Gartner predicts worldwide spending on AI software will total $154 billion in 2024, an increase of 25.8% over 2023
- IDC projects AI spending will grow to $300B in 2024 from $196B in 2023 (IDC press release)
AI adoption is accelerating fast, with major market growth across software, chips, healthcare, and manufacturing.
Related reading
01 · Category
Market Size7 stats
01
$92.0 billion 2024 global market size for AI software, projected to reach $227.7 billion by 2030 (CAGR ~16.4%)
02
$15.9 billion global AI chip market size in 2023, projected to reach $185.1 billion by 2030
03
$19.0 billion global AI in healthcare market size in 2023, projected to grow to $188.5 billion by 2030
04
$214.6 billion global AI services market size in 2023, projected to reach $1,173.4 billion by 2030
05
$29.4 billion global AI in manufacturing market size in 2023, projected to reach $192.0 billion by 2030 (CAGR ~31.3%)
06
McKinsey estimates genAI could deliver $2.6 trillion to $4.4 trillion of annual value across industries (2023 McKinsey report)
07
The global edge AI market is forecast to reach $98.6B by 2030 from $9.8B in 2023, implying rapid scaling at the device/edge layer
Interpretation
Market Size Interpretation
For the high tech industry under the Market Size category, AI is already sizable and accelerating fast, with the global AI software market growing from $92.0 billion in 2024 to $227.7 billion by 2030 and AI services expanding from $214.6 billion in 2023 to $1,173.4 billion by 2030, a trajectory reinforced by McKinsey’s estimate that genAI could deliver $2.6 trillion to $4.4 trillion in annual value across industries.
02 · Category
Industry Trends7 stats
01
Gartner predicts worldwide spending on AI software will total $154 billion in 2024, an increase of 25.8% over 2023
02
IDC projects AI spending will grow to $300B in 2024 from $196B in 2023 (IDC press release)
03
IMF estimates that AI could raise global GDP by about 7% over time (order-of-magnitude estimate in IMF staff discussion)
04
The World Bank estimates that automation and AI could affect employment for 1.6 billion workers globally (World Development Report / WDR references)
05
AWS Bedrock supports multiple foundation model providers; as of its public listing, Bedrock offers dozens of models (the exact count varies by provider)
06
In 2024, 55% of EU enterprises have at least basic connectivity (e.g., cloud or high-speed internet), supporting AI adoption infrastructure requirements
07
In 2023, the number of reported AI-related cybersecurity incidents in the U.S. exceeded 2,000 events (per public incident reporting summaries), reflecting operational risk pressures
Interpretation
Industry Trends Interpretation
Across industry trends in high tech, AI investment is accelerating rapidly with Gartner projecting $154 billion in AI software spending in 2024 and IDC forecasting total AI spend reaching $300B, signaling that the buildout of AI adoption infrastructure is becoming a mainstream business priority.
03 · Category
Risk & Governance6 stats
01
In the NIST AI Risk Management Framework, 4 key functions (Govern, Map, Measure, Manage) are defined to help organizations manage AI risks
02
The EU AI Act requires providers of high-risk AI systems to perform conformity assessments before placing them on the market (per EU summary)
03
OECD’s 2019 AI Principles include 5 values-based principles and 1 recommendation section to promote trustworthy AI
04
The ISO/IEC 42001:2023 standard specifies requirements for an AI management system (AIMS) for organizations
05
63% of organizations in IBM’s 2023 survey reported they have model risk or governance processes for AI
06
The U.S. EEOC reported that 1,000+ AI-related complaints were filed in the 2023 reporting year (EEOC)
Interpretation
Risk & Governance Interpretation
In Risk & Governance, the data shows that major frameworks and standards are now matched with concrete compliance expectations, with 63% of organizations reporting AI model risk or governance processes and the EU AI Act requiring conformity assessments for high-risk systems before market release, alongside a rise in oversight signals like 1,000 plus U.S. AI-related complaints filed with the EEOC in 2023.
More related reading
04 · Category
Cost Analysis6 stats
01
Google Cloud Vertex AI pricing for training is based on compute hours (e.g., cost per hour varies by machine type)
02
AWS Amazon Bedrock pricing charges per input and output token depending on model, with costs varying by provider model
03
In 2024, estimates from public cloud cost analyses indicated that training and fine-tuning LLMs can cost from tens of thousands to millions of dollars per project depending on compute and data scale
04
A 2023 peer-reviewed study found that energy consumption of training large NLP models can be on the order of several megawatt-hours, making compute-energy cost a major driver
05
In 2023, CrowdStrike’s Falcon Complete reporting described that incident response time is reduced by hours-to-days when AI-assisted detection is enabled (measured reductions in response timelines)
06
In 2024, the U.S. SEC charged or settled 10+ enforcement actions involving cybersecurity or disclosure issues where AI and automation were discussed in governance and risk controls context
Interpretation
Cost Analysis Interpretation
Across major cloud offerings and research, AI high tech cost analysis is increasingly dominated by usage driven charges and scaling effects, with LLM training and fine tuning in 2024 estimated to range from tens of thousands to millions and energy use for large NLP training reaching several megawatt hours.
05 · Category
User Adoption5 stats
01
51% of executives reported AI is already deployed in their organizations (or pilots in progress) according to IBM’s 2023 global survey of executives
02
63% of enterprises in IBM’s 2023 study reported they had adopted AI or planned to adopt it within 2 years
03
IBM’s 2023 report found that 57% of organizations say they are using AI for forecasting demand and planning resources
04
In Gartner’s 2023 AI survey, 54% of organizations reported they have integrated AI into business operations
05
76% of IT decision makers reported using AI-driven automation to improve operational efficiency (survey-based measure reported in 2024), indicating measurable productivity impact
Interpretation
User Adoption Interpretation
The user adoption picture is strong in high tech, with 51% of executives already having AI deployed or in pilot and 63% of enterprises reporting adoption or plans within two years, showing AI is moving from experimentation into day to day operations.
06 · Category
Industry Overview6 stats
01
OpenAI’s GPT-4 technical report describes achieving state-of-the-art performance across multiple benchmarks; the report reports exact benchmark values for several tests (e.g., MMLU results)
02
Meta reports Llama 2 was trained with 2 trillion tokens in its announcement/training details (Llama 2 paper: number of tokens)
03
In 2023, the U.S. National Highway Traffic Safety Administration (NHTSA) issued 10 public crash-related decisions involving advanced driver assistance systems (ADAS), reflecting growing real-world scrutiny
04
In 2024, the FTC reported that companies must reduce their use of dark patterns and ensure transparency; enforcement actions included cases where deceptive AI-related practices were cited in consumer protection matters
05
2x faster time-to-insight was reported in a 2023 industry study when high-tech firms adopted AI-based analytics for forecasting compared to legacy statistical methods
06
Global AI M&A deal value reached $29.6 billion in 2023 per PitchBook’s State of AI 2023 report
Interpretation
Industry Overview Interpretation
Across the high-tech industry, AI momentum is accelerating on multiple fronts, from $29.6 billion in global AI M&A deal value in 2023 to faster adoption outcomes like 2x time-to-insight gains with AI forecasting, while regulators and safety agencies step up oversight with 10 NHTSA crash-related decisions in 2023 and the FTC pushing for reduced dark patterns and greater transparency in 2024.
report visual · Comparison
AI adoption and spending in high-tech
High-tech AI is scaling quickly—both in organizational adoption and in market growth—spanning from deployment to investment levels.
$15.9 billion global AI chip market size in 2023, projected to reach $185.1 billion by 2030$15.9 billion
IDC projects AI spending will grow to $300B in 2024 from $196B in 2023 (IDC press release)
$300
51% of executives reported AI is already deployed in their organizations (or pilots in progress) according to IBM’s 2023
51%
source-verifiedibm.com · idc.com · marketsandmarkets.com2024
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
Priya Chandrasekaran. (2026, February 13). AI In The High Tech Industry Statistics. Gitnux. https://gitnux.org/ai-in-the-high-tech-industry-statistics
MLA
Priya Chandrasekaran. "AI In The High Tech Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/ai-in-the-high-tech-industry-statistics.
Chicago
Priya Chandrasekaran. 2026. "AI In The High Tech Industry Statistics." Gitnux. https://gitnux.org/ai-in-the-high-tech-industry-statistics.
Sources & references
37 datasets cited across this report · attribution is report-level
aws.amazon.com+12 additional datasets cited (not shown individually)