기업용 AI 시장 : 시장 점유율 분석, 산업 동향, 통계, 성장 예측(2025-2030년)

The enterprise AI market size is estimated at USD 97.2 billion in 2025 and is forecast to reach USD 229.3 billion by 2030, registering an 18.9% CAGR.

Expansion is propelled by rapid adoption of generative AI, agentic systems that automate multi-step tasks, and rising demand for specialised silicon that cuts inference times. Enterprises increasingly view AI as a route to cost optimisation, with Microsoft's AI portfolio alone running at a USD 13 billion annualised rate in fiscal 2025, a 175% year-on-year jump. Hardware suppliers mirror this momentum; NVIDIA posted USD 44.1 billion in Q1 FY2026 revenue despite export controls, underlining resilient demand for high-end GPUs. Cloud remains the primary deployment path, yet hybrid and edge rollouts are climbing fast as firms juggle data-sovereignty mandates with real-time use cases. Investment patterns hint at a maturing competitive environment: venture capital funding topped USD 100 billion in 2024, but deals are concentrating around fewer late-stage players, signalling future consolidation.

Global Enterprise AI Market Trends and Insights

Surging Demand for Automation & AI-Based Solutions

Corporate automation has moved beyond rule-based RPA toward cognitive agents spanning supply chain, finance, and customer operations. Organisations that embed agentic AI in logistics report 61% higher revenue growth than peers, while manufacturers such as Unilever lifted overall equipment effectiveness by 85% through AI-driven optimisation. Decision cycles that once took days now shrink to minutes, delivering not just cost control but faster market response. Coupling generative AI with workflow engines is spawning adaptive process automation that refines itself without human scripting.

Need to Analyse Exponentially Growing Enterprise Data Sets

Data growth outpaces traditional analytics tooling, creating pent-up demand for large-language-model interfaces that let business users query multi-petabyte stores in natural language. Financial firms deploy GPT-scale models to combine transactions, chat transcripts, and market feeds for in-flight risk scoring, while healthcare providers synthesise imaging and EHR records to support diagnostics. Automated data-discovery features in modern AI stacks now cut data-prep effort from months to days, unlocking value faster.

Cultural & Skills Gap Slowing Enterprise Adoption

Shortfalls in AI talent rank ahead of cybersecurity and cloud skills, with 71% of firms citing expertise gaps as the chief bottleneck. Compensation premiums of up to 20% for generative-AI roles widen disparities, especially in legacy sectors where only 21% of companies have re-engineered workflows for AI. Emerging roles such as LLMOps engineers compound the challenge, forcing enterprises to ramp training initiatives or pursue managed-service partners.

Other drivers and restraints analyzed in the detailed report include:

1. Rise of Cloud-Based AI-as-a-Service Platforms
2. Restraint % Impact on CAGR Forecast Geographic Relevance Impact Timeline Cultural & skills gap slowing enterprise adoption -2.8% Global, acute in traditional industries Medium term (2-4 years) Data-sovereignty and privacy-regulation hurdles -2.1% EU & North America, expanding to APAC Long term (>= 4 years)
3. Data-Sovereignty and Privacy-Regulation Hurdles

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software and platforms accounted for 48% of the enterprise AI market in 2024, underscoring enterprise demand for pre-integrated capabilities. Hardware accelerators, however, are growing the fastest at 23.11% CAGR, indicating a pivot toward performance-centric infrastructure investments. The enterprise AI market size for hardware is projected to climb sharply as organisations run larger foundation models on-premises for privacy. NVIDIA's data-center revenue hit USD 26.3 billion in Q2 FY2025, a 154% rise that highlights sustained capital allocation into GPUs.

Uptake of custom ASICs illustrates a structural shift from general CPUs to matrix-optimised processors. Cloud vendors integrate these accelerators into hosted stacks, giving enterprises rapid scale-out without bearing depreciation. At the edge, power-efficient SoCs enable local inference for industrial vision and IoT gateways, broadening the enterprise AI market beyond core data centers.

Large enterprises continue to dominate absolute spending, yet SMEs now access advanced AI through templated models and SaaS billing. Industry-specific foundation models slash the expertise threshold, enabling a cafe chain or boutique insurer to launch AI chatbots and demand forecasting with minimal coding. Consequently, the enterprise AI market records rising contribution from companies under 1,000 staff, aligning with venture-capital flows into SME-focused AI platforms.

Cloud marketplaces package drag-and-drop pipelines while managed-service firms bundle data prep, fine-tuning, and monitoring. As AI agents automate back-office tasks, smaller firms capture productivity benefits previously reserved for global corporations, extending the enterprise AI industry's reach into long-tail sectors.

Enterprise AI Market is Segmented by Component (Software / Platform, Services and Hardware Accelerators), Deployment Model (On-Premise, Cloud and Hybrid / Edge), Organization Size (Large Enterprise (>=1 000 Employees), Mid-Market (100-999) and Small Enterprise (<100)), Functional Area, Technology, End-User Industry (BFSI, Manufacturing and More), Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

North America controlled 41.50% of 2024 enterprise AI market revenue, buoyed by hyperscaler capex exceeding USD 75 billion and a deep venture ecosystem. US policy now scrutinises cloud-AI partnerships for anticompetitive lock-ins, yet the innovation engine remains robust. Canada pursues a balanced governance blueprint that preserves research flexibility while safeguarding ethics, and Mexico leverages near-shoring to channel AI investment into manufacturing corridors.

Europe adopts a platform of trust anchored by the EU AI Act, shaping solutions that foreground explainability. Germany's strong industrial base fuels demand for AI-powered automation, while the UK positions itself as an AI testing sandbox under a pro-innovation stance. French and Italian initiatives combine ethical frameworks with incentives for startup creation, though compliance overhead can lengthen go-to-market cycles.

Asia-Pacific records the fastest uplift in active deployments. China shows 83% generative-AI adoption, yet US firms lead in production-grade rollouts, underscoring maturity gaps. Japan and South Korea scale domestic semiconductor fabs to secure hardware supply, and India's USD 19 billion AI funding wave accelerates startup momentum. Meanwhile, the Middle East and Africa enterprise AI market is forecast at 21.70% CAGR as sovereign wealth funds bankroll national AI hubs in Saudi Arabia and the UAE. Latin America crafts human-rights-oriented regulations, creating white spaces for responsible-AI platform vendors.

1. Microsoft Corporation
2. IBM Corporation
3. Amazon Web Services Inc.
4. Google LLC
5. Oracle Corporation
6. Hewlett Packard Enterprise
7. NVIDIA Corporation
8. SAP SE
9. Intel Corporation
10. Wipro Limited
11. NEC Corporation
12. Accenture plc
13. ServiceNow Inc.
14. DataRobot Inc.
15. UiPath Inc.
16. C3.ai Inc.
17. Palantir Technologies
18. H2O.ai Inc.
19. Sentient Technologies
20. AiCure LLC

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Surging demand for automation and AI-based solutions
  • 4.2.2 Need to analyse exponentially growing enterprise data sets
  • 4.2.3 Rise of cloud-based AI-as-a-Service platforms
  • 4.2.4 Advances in specialised computing hardware (GPU, TPU, NPU)
  • 4.2.5 Industry-specific foundation models democratising AI for SMEs
  • 4.2.6 Net-Zero pledges driving AI-enabled carbon-optimisation tools
• 4.3 Market Restraints
  • 4.3.1 Cultural and skills gap slowing enterprise adoption
  • 4.3.2 Data-sovereignty and privacy-regulation hurdles
• 4.4 Value / Supply-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
  • 4.6.1 Model-Ops and Prompt-Ops evolution
  • 4.6.2 Edge inference acceleration
• 4.7 Porter's Five Forces Analysis
  • 4.7.1 Bargaining Power of Buyers
  • 4.7.2 Bargaining Power of Suppliers
  • 4.7.3 Threat of New Entrants
  • 4.7.4 Threat of Substitutes
  • 4.7.5 Competitive Rivalry
• 4.8 Assessment of the Impact of Macroeconomic Trends on the Market

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software / Platform
  • 5.1.2 Services
  • 5.1.3 Hardware Accelerators
• 5.2 By Organisation Size
  • 5.2.1 Large Enterprise (>=1 000 Employees)
  • 5.2.2 Mid-market (100-999)
  • 5.2.3 Small Enterprise (<100)
• 5.3 By Functional Area
  • 5.3.1 Customer-facing (CX, marketing, sales)
  • 5.3.2 Operations and Supply-chain
  • 5.3.3 Finance and Risk
  • 5.3.4 HR and Talent
• 5.4 By Technology
  • 5.4.1 Machine Learning / Foundation Models
  • 5.4.2 Natural-Language Processing
  • 5.4.3 Computer Vision
  • 5.4.4 Decision Intelligence / Optimisation
• 5.5 By End-user Industry
  • 5.5.1 BFSI
  • 5.5.2 Manufacturing
  • 5.5.3 Automotive and Mobility
  • 5.5.4 IT and Telecom
  • 5.5.5 Media and Advertising
  • 5.5.6 Healthcare and Life-sciences
  • 5.5.7 Retail and e-Commerce
  • 5.5.8 Energy and Utilities
  • 5.5.9 Others
• 5.6 By Deployment Model
  • 5.6.1 On-premise
  • 5.6.2 Cloud
  • 5.6.3 Hybrid / Edge
• 5.7 By Geography
  • 5.7.1 North America
    • 5.7.1.1 United States
    • 5.7.1.2 Canada
    • 5.7.1.3 Mexico
  • 5.7.2 South America
    • 5.7.2.1 Brazil
    • 5.7.2.2 Argentina
    • 5.7.2.3 Rest of South America
  • 5.7.3 Europe
    • 5.7.3.1 United Kingdom
    • 5.7.3.2 Germany
    • 5.7.3.3 France
    • 5.7.3.4 Italy
    • 5.7.3.5 Rest of Europe
  • 5.7.4 Asia-Pacific
    • 5.7.4.1 China
    • 5.7.4.2 Japan
    • 5.7.4.3 India
    • 5.7.4.4 South Korea
    • 5.7.4.5 Rest of Asia-Pacific
  • 5.7.5 Middle East and Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration and Share
• 6.2 Strategic Developments
• 6.3 Company Profiles (includes Global-level Overview, Market-level Presence, Core Segments, Financials, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.3.1 Microsoft Corporation
  • 6.3.2 IBM Corporation
  • 6.3.3 Amazon Web Services Inc.
  • 6.3.4 Google LLC
  • 6.3.5 Oracle Corporation
  • 6.3.6 Hewlett Packard Enterprise
  • 6.3.7 NVIDIA Corporation
  • 6.3.8 SAP SE
  • 6.3.9 Intel Corporation
  • 6.3.10 Wipro Limited
  • 6.3.11 NEC Corporation
  • 6.3.12 Accenture plc
  • 6.3.13 ServiceNow Inc.
  • 6.3.14 DataRobot Inc.
  • 6.3.15 UiPath Inc.
  • 6.3.16 C3.ai Inc.
  • 6.3.17 Palantir Technologies
  • 6.3.18 H2O.ai Inc.
  • 6.3.19 Sentient Technologies
  • 6.3.20 AiCure LLC

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-space and Unmet-need Assessment