자연어처리(NLP) : 시장 점유율 분석, 업계 동향과 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the natural language processing market size is projected to expand from USD 39.37 billion in 2025 and USD 47.37 billion in 2026 to USD 117.57 billion by 2031, registering a CAGR of 19.94% between 2026 to 2031.

This report is Segmented by Deployment (On-Premise, and Cloud), Organization Size (Large Enterprises, and Small and Medium Enterprises), Component (Hardware, Software, and Services), Processing Type (Text, Speech or Voice, and Image or Vision), End-User Industry (BFSI, Healthcare and Life Sciences, IT and Telecom, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Natural Language Processing Market Trends and Insights

Generative-AI-Powered Model Accuracy Gains

Foundation releases in 2025 posted 18-23 percentage-point jumps on specialist benchmarks when fine-tuned on fewer than 10,000 labels, slashing annotation budgets and opening high-liability workflows such as legal clause extraction and ICD-10 coding. Google's Gemini 2.0 Flash trimmed inference latency by 40%, enabling conversational and summarization tasks that once demanded batch processing. Mixture-of-experts routing keeps only 10-15% of a trillion parameters active per query, lowering energy draw without hurting precision. OpenAI's o3 model logged 87.5% on ARC-AGI, signaling that multi-step reasoning is entering automated scope. Together these advances fueled board-level confidence that the natural language processing market can underpin mission-critical operations.

Surge in Conversational AI Adoption in Customer Support

Twelve-to-eighteen-percent wage inflation for tier-1 agents pushed contact centers toward automation, and retrieval-augmented generation raised first-contact resolution to 75-85% by mid-2025.Salesforce's Agentforce 2.0 orchestrates CRM, billing, and inventory flows without hand-offs, cutting average handle time by up to 40%. Cloud platforms bundled chat APIs into existing enterprise agreements, shrinking pilot cycles from quarters to weeks. The EU AI Act tagged most customer-service bots as limited-risk, sparing them conformity reviews and accelerating adoption across the trading bloc.

Shortage of High-Quality, Bias-Free Training Data

Audits showed 70-80% of corpora remain English, leading to 15-25-point drops in other languages and exposing enterprises to compliance risk in multilingual regions. Synthetic generators fill gaps yet risk mode collapse after repeated fine-tunes. Health records are siloed behind HIPAA, while PCI-DSS locks down transaction logs, fragmenting training pools. Without standardized bias tests, firms invent bespoke audits that slow procurement. The restraint weighs heavily on national markets seeking sovereign AI autonomy.

Other drivers and restraints analyzed in the detailed report include:

1. Integration of NLP in Embedded or Edge Devices
2. Proliferation of Domain-Specific LLMs for Regulated Industries
3. Escalating Inference Costs for Large Models

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

Segment Analysis

Cloud retained 64.31% share of the natural language processing market in 2025 as enterprises favored elastic scaling and bundled AI services. Through 2031 the segment grows at 20.01% as hyperscalers lock in workloads by embedding proprietary models into broader contracts. On-premise clusters persist inside banks and hospitals that must audit every data flow, even when this choice raises cost by up to 50%. AWS Bedrock and Azure confidential enclaves now blur the line, letting clients keep sensitive payloads inside virtual private clouds while still relying on managed orchestration.

Edge adoption surges as smartphone penetration tops 70% in Asia-Pacific and automakers demand deterministic voice control. Google's AI Edge SDK compresses Gemini Nano to under 2 GB, proving high-grade NLP can live on mid-tier handsets. Mercedes-Benz and BMW show 20-point gains in voice intent accuracy after localizing inference. Processing data in the device satisfies China's Personal Information Protection Law with no architecture changes, and similar dynamics play out under India's Digital Personal Data Protection Act. The dual-track evolution means the natural language processing market now values cloud and edge parity rather than single-venue supremacy.

Large enterprises held 73.13% of the natural language processing market share in 2025, leveraging petabytes of data and dedicated MLOps teams. Yet SMEs are projected to outpace at a 19.98% CAGR because no-code agents and pay-per-token models eliminate capital barriers. Hugging Face hubs and consumption-based cloud pricing push experimentation costs below USD 20,000, affordable even to seed-stage startups. Fast decision cycles let SMEs pilot voice commerce bots or contract analyzers in weeks, often beating slow-moving incumbents to niche opportunities.

Corporate titans keep an edge in multi-system integrations that tap ERP, CRM and supply chain feeds concurrently. They also shoulder heavier EU AI Act audits that can extend deployment by up to a year, an overhead the smallest firms avoid when their use cases fall under limited-risk classifications. Over the forecast horizon convergence is likely, with mature tooling erasing technical gaps and forcing both cohorts to differentiate on workflow intimacy rather than raw compute scale.

Complete Report Scope:

• By Deployment
  • On-Premise
  • Cloud
• By Organization Size
  • Large Enterprises
  • Small and Medium Enterprises (SMEs)
• By Component
  • Hardware
  • Software
  • Services
• By Processing Type
  • Text
  • Speech or Voice
  • Image or Vision
• By End-User Industry
  • BFSI
  • Healthcare and Life Sciences
  • IT and Telecom
  • Retail and E-Commerce
  • Manufacturing
  • Media and Entertainment
  • Education
  • Others End-User Industry
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • South Korea
    • India
    • Australia
    • New Zealand
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • United Arab Emirates
      • Saudi Arabia
      • Turkey
      • Rest of Middle East
    • Africa
      • South Africa
      • Nigeria
      • Kenya
      • Rest of Africa

Geography Analysis

North America kept 37.92% share in 2025, anchored by hyperscaler infrastructure and risk-tolerant early adopters. Enterprises tap generous venture funding, and regulatory sandboxes let banks trial generative models under supervisory guidance. Yet saturation and rising compliance overhead temper growth to high teens.

Asia-Pacific records the fastest climb at 22.13% CAGR as China's USD 50 billion sovereign-AI push, India's public digital stack, and Japan's aging-population pressures converge. Chinese state-owned firms mandate domestic model deployment, boosting Baidu and Alibaba adoption. India's Unified Payments Interface feeds billions of multilingual records into fraud and credit models. Japanese hospitals enjoy tax breaks when installing ambient documentation, spurring clinical NLP rollouts.

Europe benefits from the EU AI Act's clarity, though conformity reviews add 6-12 months to high-risk launches. Germany's automakers embed local voice assistants to satisfy GDPR. The United Kingdom encourages KYC automation to trim compliance costs. South America adopts customer-service bots tuned to regional dialects, while the Middle East funds sovereign AI as economic-diversification pillars. Africa's uptake clusters in Nigeria and Kenya where mobile-first NLP supports fintech and ag-extension messaging. Despite disparate starting points, every region positions the natural language processing market as core digital infrastructure by decade's end.

1. Microsoft Corp.
2. Google LLC
3. SAS Institute Inc.
4. IBM Corp.
5. NVIDIA Corp.
6. OpenAI LP
7. Meta Platforms Inc.
8. SAP SE
9. Oracle Corp.
10. Baidu Inc.
11. Intel Corp.
12. Qualcomm Inc.
13. Amazon Web Services
14. Adobe Inc.
15. Salesforce Inc.
16. Apple Inc.
17. Verint Systems Inc.
18. Nuance Communications
19. Cohere Inc.
20. Hugging Face
21. Grammarly Inc.

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 Generative-AI-Powered Model Accuracy Gains
  • 4.2.2 Surge in Conversational AI Adoption in Customer Support
  • 4.2.3 Integration of NLP in Embedded or Edge Devices
  • 4.2.4 Proliferation of Domain-Specific LLMs for Regulated Industries
  • 4.2.5 Rising Demand for Real-Time Speech Recognition in Automotive and Smart Devices
  • 4.2.6 Multimodal Foundation Models Unlocking New Verticals
• 4.3 Market Restraints
  • 4.3.1 Shortage of High-Quality, Bias-Free Training Data
  • 4.3.2 Escalating Inference Costs for Large Models
  • 4.3.3 Cross-Border Data Residency Compliance Barriers
  • 4.3.4 Environmental Footprint of Large-Scale Training Compute
• 4.4 Industry Value-Chain Analysis
• 4.5 Regulatory Landscape
• 4.6 Technological Outlook
• 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

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Deployment
  • 5.1.1 On-Premise
  • 5.1.2 Cloud
• 5.2 By Organization Size
  • 5.2.1 Large Enterprises
  • 5.2.2 Small and Medium Enterprises (SMEs)
• 5.3 By Component
  • 5.3.1 Hardware
  • 5.3.2 Software
  • 5.3.3 Services
• 5.4 By Processing Type
  • 5.4.1 Text
  • 5.4.2 Speech or Voice
  • 5.4.3 Image or Vision
• 5.5 By End-User Industry
  • 5.5.1 BFSI
  • 5.5.2 Healthcare and Life Sciences
  • 5.5.3 IT and Telecom
  • 5.5.4 Retail and E-Commerce
  • 5.5.5 Manufacturing
  • 5.5.6 Media and Entertainment
  • 5.5.7 Education
  • 5.5.8 Others End-User Industry
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Italy
    • 5.6.3.5 Spain
    • 5.6.3.6 Rest of Europe
  • 5.6.4 Asia Pacific
    • 5.6.4.1 China
    • 5.6.4.2 Japan
    • 5.6.4.3 South Korea
    • 5.6.4.4 India
    • 5.6.4.5 Australia
    • 5.6.4.6 New Zealand
    • 5.6.4.7 Rest of Asia-Pacific
  • 5.6.5 Middle East and Africa
    • 5.6.5.1 Middle East
      • 5.6.5.1.1 United Arab Emirates
      • 5.6.5.1.2 Saudi Arabia
      • 5.6.5.1.3 Turkey
      • 5.6.5.1.4 Rest of Middle East
    • 5.6.5.2 Africa
      • 5.6.5.2.1 South Africa
      • 5.6.5.2.2 Nigeria
      • 5.6.5.2.3 Kenya
      • 5.6.5.2.4 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Microsoft Corp.
  • 6.4.2 Google LLC
  • 6.4.3 SAS Institute Inc.
  • 6.4.4 IBM Corp.
  • 6.4.5 NVIDIA Corp.
  • 6.4.6 OpenAI LP
  • 6.4.7 Meta Platforms Inc.
  • 6.4.8 SAP SE
  • 6.4.9 Oracle Corp.
  • 6.4.10 Baidu Inc.
  • 6.4.11 Intel Corp.
  • 6.4.12 Qualcomm Inc.
  • 6.4.13 Amazon Web Services
  • 6.4.14 Adobe Inc.
  • 6.4.15 Salesforce Inc.
  • 6.4.16 Apple Inc.
  • 6.4.17 Verint Systems Inc.
  • 6.4.18 Nuance Communications
  • 6.4.19 Cohere Inc.
  • 6.4.20 Hugging Face
  • 6.4.21 Grammarly Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment