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Artificial Intelligence (AI) Market

Report Code TC 7894
Published in Apr, 2025, By MarketsandMarkets™
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Artificial Intelligence (AI) Market by Offering (Infrastructure, Software, Services), Technology (ML, NLP, Generative AI), Business Function (Operations & Supply Chain, Finance & Accounting), Enterprise Application, and End User - Global Forecast to 2032

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Overview

MarketsandMarkets: The market for artificial intelligence is slated to expand from USD 371.71 billion in 2025 to USD 2,407.02 billion by 2032, at a CAGR of 30.6% during the forecast period.

The artificial intelligence (AI) market is accelerating at an unprecedented pace, propelled by its deep integration into enterprise workflows, infrastructure modernization, and a shift toward autonomous decision-making systems. Among the multiple drivers fueling this momentum, the most transformative is the rapid democratization of AI via cloud-native AI platforms and APIs. These platforms—offered by hyperscalers like Microsoft Azure OpenAI, AWS Bedrock, and Google Vertex AI—have drastically reduced barriers to entry by abstracting the complexity of model development and deployment. Enterprises no longer need in-house data science teams to build AI solutions; instead, they can plug into pre-trained foundation models and fine-tune them on proprietary data using intuitive interfaces and scalable APIs. This ease of access has triggered a wave of use case experimentation across sectors—ranging from customer support automation and fraud detection to predictive maintenance and personalized healthcare. Crucially, this driver not only accelerates time-to-value but also enables small and mid-sized enterprises (SMEs) to adopt AI at enterprise-grade levels, reshaping competitive dynamics. In essence, cloud-delivered AI platforms have become the great equalizer—transforming AI from a niche capability into a standard business utility.

Artificial intelligence refers to the capability of a machine to imitate intelligent human behavior by performing tasks such as reasoning, learning, decision-making, and perception. It encompasses a broad range of technologies—including machine learning, natural language processing, computer vision, context-aware computing, and generative AI—that enable systems to analyze data, adapt through experience, and autonomously perform functions traditionally requiring human intelligence.

Artificial Intelligence (AI) Market

Attractive Opportunities in the Artificial Intelligence (AI) Market

ASIA PACIFIC

The artificial intelligence market in Asia Pacific is growing rapidly due to strong government-led digitalization initiatives, rising enterprise AI adoption in countries like China, India, and South Korea, and a booming startup ecosystem focused on localized AI solutions. Increasing investments in AI infrastructure and smart city projects are also accelerating regional demand.

Vendors that offer domain-specific AI with seamless integration, scalable infrastructure, and responsible AI governance will excel. Those combining proprietary data with vertical expertise will gain a decisive edge in enterprise adoption.

Emerging AI solutions will include autonomous agents, multimodal AI platforms combining text, vision, and speech, and AI copilots embedded across enterprise software. These tools will drive productivity, decision support, and intelligent automation.

The shift toward domain-specific AI models is reshaping the market, as enterprises demand tailored solutions over general-purpose outputs. This trend is accelerating vertical AI adoption and opening up competitive space beyond foundation model providers.

Generative AI is the most transformative technology impacting the market, driving new applications in content creation, coding, design, and enterprise automation. Its integration into core workflows is reshaping customer experiences.

Global Artificial Intelligence (AI) Market Dynamics

Driver: Rising affinity of enterprises toward vertically contextualized AI solutions

One of the most pivotal and high-impact growth drivers of the AI market is the integration of AI into sector-specific software suites, particularly in high-complexity domains like healthcare, legal, and manufacturing. Unlike generic AI tools, these embedded AI features are fine-tuned for industry-grade data formats, compliance requirements, and workflow nuances. For instance, in healthcare, electronic medical record (EMR) platforms like Epic and Cerner now incorporate AI to surface clinical decision support, automate medical coding, and predict patient deterioration risks using real-time vitals. In legal tech, AI embedded into contract lifecycle management (CLM) platforms such as Ironclad and LinkSquares automates clause extraction, redlining, and regulatory alignment, significantly reducing review turnaround times. Meanwhile, in manufacturing, Siemens’ MindSphere and GE’s Predix leverage AI models to detect equipment anomalies, forecast maintenance windows, and optimize energy consumption. These embedded AI integrations are not standalone tools—they are part of core enterprise systems, making adoption seamless and use case-driven. As a result, enterprises are more willing to invest in AI when it is vertically contextualized, risk-mitigated, and instantly operational within their existing software ecosystems—turning AI from an experimental cost center into a productivity-anchored investment.

Restraint: Shortage of curated, domain-specific datasets

A major restraint throttling the growth of the AI market is the shortage of domain-specific annotated datasets, particularly in regulated and high-stakes industries like healthcare, finance, and legal. While large language models thrive on web-scale data, enterprise-grade AI systems demand curated, compliant, and domain-rich datasets to deliver meaningful outcomes. In healthcare, for example, training AI to detect rare diseases or interpret radiological scans requires annotated datasets governed by HIPAA and FDA standards—yet access to such data is limited due to privacy, consent, and fragmentation issues. Similarly, in finance, fraud detection models need labeled transaction-level data that’s often siloed due to internal risk policies and external compliance mandates like GDPR. Even in legal AI, the lack of labeled clause-level contract data across jurisdictions slows the training of robust models. This data bottleneck forces vendors to either build synthetic data pipelines—which may not capture real-world variance—or partner with a handful of institutions, limiting scalability. As a result, the inability to acquire, annotate, and scale high-quality datasets remains a structural bottleneck for operationalizing AI in mission-critical domains, delaying ROI and adoption across key sectors.

 

Opportunity: Emergence of multilingual large language models with cultural nuance understanding

A highly underexploited yet commercially significant opportunity in the AI market lies in AI model localization for non-English and low-resource languages, particularly across emerging markets in Asia, Africa, and Latin America. While most foundation models are trained on predominantly English internet data, enterprises in regions like India, Indonesia, Nigeria, and Brazil operate in linguistically diverse ecosystems—with customer support, legal compliance, and public service delivery requiring fluency in local dialects. This creates a compelling gap for AI vendors to fine-tune language models on vernacular data, incorporating cultural nuances, syntax variation, and code-switching behavior. Companies like AI4Bharat and Cohere for AI are already prototyping language-specific models that outperform GPT-class models on local language tasks. In sectors like fintech, local language voice bots can onboard unbanked users; in education, AI tutors in regional dialects can scale learning access; and in governance, multilingual document processing can streamline citizen services. The commercial upside is immense, as by unlocking localized AI, vendors can achieve first-mover advantage in multi-billion-dollar regional markets currently underserved by global LLM providers. This positions language localization not as a peripheral feature, but as a core strategic lever for market expansion and differentiated product-market fit in emerging economies.

Challenge: Rising computational power costs for training large-scale AI models

A growing challenge in the AI market is the escalating cost and operational complexity of training and fine-tuning large-scale models, which increasingly limits innovation to a small circle of hyperscalers and elite AI labs. Training a frontier model like GPT-4 or Gemini Ultra requires upwards of 25,000 GPUs, sustained over weeks, with compute costs exceeding USD 50 million. Even fine-tuning smaller models (like LLaMA 2 or Mistral) for domain-specific tasks isn’t trivial—it demands curated datasets, high memory infrastructure, and optimization frameworks like DeepSpeed or LoRA. For most enterprises, especially those in cost-sensitive sectors or emerging markets, this presents a structural barrier to adopting customized AI. Moreover, post-deployment retraining and inference optimization introduce further cost layers, especially as models must stay current with evolving regulatory and business contexts. This creates a dependency on foundation model APIs, which, while easy to integrate, often lack the domain specificity or control that regulated industries require. As a result, a growing number of enterprises face a trade-off between customization and affordability, slowing vertical-specific innovation and reinforcing centralization of AI capabilities in a few dominant players. This cost-performance asymmetry is increasingly emerging as a bottleneck to equitable AI adoption across the global enterprise landscape.

Global Artificial Intelligence (AI) Market Ecosystem Analysis

The artificial intelligence market ecosystem comprises AI hardware providers, AI software providers, and AI service providers. AI hardware providers form the foundational infrastructure for AI workloads by delivering the computational power, storage, and connectivity required for training and inference. AI software providers offer platforms, models, applications, and frameworks essential for developing, deploying, and managing AI systems. AI service providers facilitate the practical deployment, customization, and scaling of AI technologies across industries through core data and integrated services.

Top Companies in Artificial Intelligence (AI) Market

Note: The above diagram only shows the representation of the Artificial Intelligence (AI) Market ecosystem; it is not limited to the companies represented above.
Source: Secondary Research and MarketsandMarkets Analysis

 

By offering, AI infrastructure segment to hold largest share in 2025

AI infrastructure is slated to become the largest segment in 2025 as it forms the foundational backbone of every AI workload, from training massive foundation models to deploying inference at scale across edge and cloud environments. The surge in model complexity—from GPT-2’s 1.5 billion parameters to GPT-4’s estimated trillion+ scale—has driven exponential demand for advanced GPUs, TPUs, and custom AI accelerators, with vendors like NVIDIA, AMD, Intel, and Cerebras at the epicenter. For instance, NVIDIA’s H100 and AMD’s MI300X chips are now priced as high as USD 30,000 per unit due to their ability to parallelize AI tasks across thousands of cores with minimal latency. Memory bandwidth becomes a critical bottleneck in model training, prompting the adoption of high-bandwidth memory (HBM3) and tiered storage systems to manage massive datasets in real-time. Storage requirements are no longer measured in terabytes but in petabytes, especially in verticals like autonomous vehicles and life sciences, where data ingestion and retrieval must be instantaneous. Moreover, ultra-low latency networking fabrics like InfiniBand and Ethernet with RDMA are now essential to prevent throughput losses in multi-node training environments. As AI shifts from experimentation to production, the infrastructure stack scales accordingly—not just in raw power but in architectural sophistication—making it the most monetized and capital-intensive layer in the entire AI value chain.

By end user, healthcare & life sciences to account for highest growth rate during forecast period

Healthcare & life sciences is poised to register the highest growth rate in the AI market during the forecast period, owing to its unique convergence of data abundance, regulatory urgency, and high-value decision environments that demand precision and scalability. Unlike most industries, healthcare generates structured (EHRs, lab data) and unstructured (clinical notes, medical images) data at scale, creating fertile ground for AI-powered diagnostics, treatment optimization, and patient engagement. For instance, radiology AI platforms like Aidoc and Gleamer are FDA-approved and integrated directly into PACS workflows to flag critical findings in real-time—reducing diagnostic delays. In genomics, AI models from companies like Deep Genomics and Tempus analyze massive omics datasets to identify novel biomarkers and therapeutic targets faster than traditional methods. Meanwhile, clinical trial operations, often bottlenecked by recruitment and protocol deviations, are being optimized by AI tools from vendors like Unlearn.AI and Medable that simulate patient cohorts and predict trial outcomes. Regulatory agencies such as the FDA and EMA are accelerating AI validation pathways through adaptive approval frameworks, incentivizing faster deployment of AI in regulated care settings. Furthermore, the shift toward value-based care has created economic pressure to improve outcomes per dollar spent—making AI indispensable for population health analytics, personalized care pathways, and hospital resource optimization. These systemic enablers make healthcare not just data-rich, but AI-reliant, fueling its unmatched growth trajectory in the AI landscape.

North America to emerge as largest region by market share in 2025

North America is projected to lead the AI market in 2025, due to its deep capital concentration, hyperscaler footprint, and enterprise-grade AI adoption across high-value verticals. The US alone accounts for the majority of global AI investment, fueled by VCs, private equity, and corporate venture arms, enabling aggressive R&D, model training, and M&A activity. It is home to the world’s most influential AI hyperscalers—Microsoft (Azure OpenAI), Google (Vertex AI), and Amazon (AWS Bedrock)—which not only commercialize foundation models but also offer AI-as-a-service infrastructure that underpins global deployments. Moreover, North America leads in enterprise AI maturity, especially in regulated and high-margin sectors like finance, healthcare, defense, and manufacturing. JPMorgan, Mayo Clinic, and Lockheed Martin are embedding AI not just for automation, but for core operational transformation—ranging from fraud detection and diagnostics to autonomous defense systems. The region also benefits from a tightly integrated startup–enterprise–research ecosystem: academic institutions like MIT, Stanford, and Carnegie Mellon continuously feed talent and IP into the commercial pipeline. Meanwhile, policy frameworks like the US Executive Order on AI and NIST’s AI Risk Management Framework support scalable, responsible deployment. Unlike other regions still piloting use cases, North America has moved decisively into production-scale AI—anchoring its leadership with infrastructure depth, funding velocity, and industry adoption.

LARGEST REGION IN 2025
CANADA FASTEST GROWING MARKET IN THE REGION
Artificial Intelligence (AI) Market by region

Recent Developments of Artificial Intelligence (AI) Market

  • In April 2025, Oracle launched the Oracle Cloud Infrastructure (OCI) File Storage, a fully managed service designed for AI/ML training and inference, and high-performance computing. OCI automates deployment, scaling, and maintenance, allowing users to concentrate on applications instead of infrastructure management. OCI deploys and maintains all Lustre (a type of parallel distributed file system) server components, including metadata, management, and storage servers.
  • In April 2025, Amazon Lex V2 was updated with generative AI capabilities, including support for Bedrock Knowledge Base, Guardrails, Anthropic Claude 3 Haiku, and Sonnet models. These enhancements are integrated within the QnA built-in slot. Furthermore, Lex V2 now also supports the QinConnect built-in intent to connect bots with Amazon Connect.
  • In March 2025, OpenAI integrated an advanced image generator into GPT-4o, designed to produce both beautiful and practical images with precise text rendering and adherence to prompts. GPT-4o excels at generating accurate text within images, following detailed instructions, and leveraging its knowledge base, allowing for effective visual communication through logos, diagrams, and infographics. It supports multi-turn generation, enabling users to refine images through natural conversation while maintaining consistency.
  • In March 2025, Microsoft deepened its partnership with the Government of Kuwait to accelerate AI transformation in line with Kuwait's Vision 2035. A key aspect of this collaboration is the intent to establish an AI-powered Azure Region in Kuwait to boost local AI capabilities and drive economic growth. The partnership includes initiatives in cloud and AI transformation, cybersecurity enhancements through the Cybersphere initiative, and AI literacy programs with a Microsoft Technology Innovation Hub. The Kuwaiti government will also integrate Microsoft 365 Copilot to enhance efficiency and productivity
  • In March 2025, NVIDIA launched the NVIDIA AI Data Platform, a customizable reference design for a new class of enterprise AI infrastructure aimed at demanding AI inference workloads. Leading storage providers collaborate with NVIDIA to build customized AI data platforms using NVIDIA Blackwell GPUs, BlueField DPUs, Spectrum-X networking, and the NVIDIA Dynamo open-source inference library. The platform brings accelerated computing and AI to enterprise storage, enabling AI query agents to generate insights from data in near real time.
  • In March 2025, IBM introduced several capabilities in its Watsonx AI Assistant. Conversational search now supports more languages, including French, Spanish, German, and Brazilian Portuguese, expanding its global reach. Additionally, there's improved accuracy in understanding free-text responses, reducing errors and enhancing data gathering. These updates demonstrate a focus on leveraging advanced AI models to improve user experience and broaden language support.

Key Market Players

List of Top Artificial Intelligence (AI) Market Companies

The Artificial Intelligence (AI) Market is dominated by a few major players that have a wide regional presence. The major players in the Artificial Intelligence (AI) Market are

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Scope of the Report

Report Attribute Details
Market size available for years 2020–2032
Base year considered 2024
Forecast period 2025–2032
Forecast units USD (Million)
Segments Covered Offering, Business Function, Technology, Enterprise Application, End User, and Region
Regions covered North America, Europe, Asia Pacific, Middle East & Africa, and Latin America

Key Questions Addressed by the Report

What is artificial intelligence (AI)?
Artificial intelligence (AI) refers to algorithms and computational models that enable machines to perform cognitive functions typically associated with human intelligence. These functions include, but are not limited to, natural language processing (NLP), machine learning (ML), computer vision, and decision-making. AI systems leverage advanced techniques such as deep learning, reinforcement learning, and probabilistic reasoning to process data, recognize patterns, and make autonomous decisions or provide predictive analytics. These systems are designed to improve over time through iterative training and adaptation, often utilizing large-scale data and high-performance computing infrastructure to optimize performance and accuracy.
What is the total CAGR expected to be recorded for the artificial intelligence market during 2025–2032?
The artificial intelligence market is expected to record a CAGR of 30.6% from 2025 to 2032.
How is the generative AI market shaping the broader artificial intelligence industry?
The generative AI market is profoundly shaping the broader artificial intelligence industry by driving advancements in machine learning, natural language processing, and creative applications. Technologies like OpenAI's recent GPT-4o have demonstrated significant progress, capable of producing coherent and contextually relevant text, which has spurred innovation across various sectors. The increasing capability and application of generative AI highlight its role as a catalyst for broader AI adoption, pushing the boundaries of what machines can autonomously create and enabling new business models and efficiencies across industries.
Which are the key drivers supporting the growth of the artificial intelligence market?
The key factors driving the growth of the artificial intelligence market include the growth in adoption of autonomous artificial intelligence, the rise of deep learning and machine learning technologies, and advancements in AI-native infrastructure, enhancing scalability and performance.
Which are the top 3 verticals prevailing in the artificial intelligence market?
Software & technology providers top the AI market as they develop and embed AI into core platforms, APIs, and enterprise tools—powering adoption across industries. BFSI follows closely, using AI for fraud detection, credit scoring, and personalized financial services, where real-time data processing is critical. Healthcare & life sciences ranks third, driven by AI applications in diagnostics, drug discovery, and patient care optimization. These three sectors lead due to their massive data volumes, complex workflows, and strong economic incentives for automation and precision—making AI both a strategic asset and operational necessity.
Who are the key vendors in the artificial intelligence market?
Some major players in the AI market include Google (US), Microsoft (US), IBM (US), Oracle (US), AWS (US), Intel (US), Salesforce (US), SAP (Germany), AMD (US), Qualcomm (US), Cisco (US), Meta (US), HPE (US), Siemens (Germany), NVIDIA (US), Baidu (China), SAS Institute (US), OpenAI (US), Huawei (China), Alibaba Cloud (China), Centific (US), Fractal Analytics (US), Tiger Analytics (US), Quantiphi (US), Databricks (US), iMerit (US), Telus International (Canada), Innodata (US), Sama (US), C3 AI (US), HQE Systems (US), Appier (Taiwan), Adept (US), H20.AI (US), Spot AI (US), Anthropic (US), Cohere (Canada), Inbenta (US), Character.ai (US), DeepL (Germany), Inflection AI (US), Arrow AI (US), Observe.ai (US), Anyscale (US), AI21 Labs (Israel), Persado (US), Dialpad (US), Graphcore (UK), Shield AI (US), Gamaya (Switzerland), Arthur AI (US), ADA (Canada), Mostly AI (Austria), Metropolis Technologies (US), Cerebras (US), Jasper (US), Soundful (US), Writesonic (US), One AI (Israel), Synthesia (UK), Snorkel (US), Labelbox (US), Appen (US), and Cogito Tech (US).

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Table of Contents

Exclusive indicates content/data unique to MarketsandMarkets and not available with any competitors.

TITLE
PAGE NO
INTRODUCTION
1
  • 1.1 OBJECTIVES OF THE STUDY
  • 1.2 MARKET DEFINITION
    INCLUSIONS AND EXCLUSIONS
  • 1.3 MARKET SCOPE
    MARKET SEGMENTATION
    REGIONS COVERED
    YEARS CONSIDERED FOR THE STUDY
  • 1.4 CURRENCY CONSIDERED
  • 1.5 STAKEHOLDERS
  • 1.6 SUMMARY OF CHANGES
RESEARCH METHODOLOGY
2
  • 2.1 RESEARCH DATA
    SECONDARY DATA
    PRIMARY DATA
    BREAKUP OF PRIMARY PROFILES
    KEY INDUSTRY INSIGHTS
  • 2.2 MARKET BREAKUP AND DATA TRIANGULATION
  • 2.3 MARKET SIZE ESTIMATION
    TOP-DOWN APPROACH
    BOTTOM-UP APPROACH
  • 2.4 MARKET FORECAST
  • 2.5 ASSUMPTIONS FOR THE STUDY
  • 2.6 LIMITATIONS OF THE STUDY
EXECUTIVE SUMMARY
3
PREMIUM INSIGHTS
4
  • 4.1 ATTRACTIVE OPPORTUNITIES IN THE GLOBAL ARTIFICIAL INTELLIGENCE MARKET
  • 4.2 ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING, 2025 VS. 2032
  • 4.3 ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION, 2025 VS. 2032
  • 4.4 ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY, 2025 VS. 2032
  • 4.5 ARTIFICIAL INTELLIGENCE MARKET, BY ENTERPRISE APPLICATIONS, 2025 VS. 2032
  • 4.6 ARTIFICIAL INTELLIGENCE MARKET, BY END USER, 2025 VS. 2032
  • 4.7 ARTIFICIAL INTELLIGENCE MARKET, BY REGION
MARKET OVERVIEW AND INDUSTRY TRENDS
5
  • 5.1 INTRODUCTION
  • 5.2 MARKET DYNAMICS
    DRIVERS
    RESTRAINTS
    OPPORTUNITIES
    CHALLENGES
  • 5.3 EVOLUTION OF ARTIFICIAL INTELLIGENCE
  • 5.4 SUPPLY CHAIN ANALYSIS
  • 5.5 ECOSYSTEM ANALYSIS
  • 5.6 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
  • 5.7 CASE STUDY ANALYSIS
    CASE STUDY 1
    CASE STUDY 2
    CASE STUDY 3
  • 5.8 TECHNOLOGY ANALYSIS
    KEY TECHNOLOGIES
    - GENERATIVE AI
    - AUTONOMOUS AI & AUTONOMOUS AGENTS
    - AUTOML
    - CAUSAL AI
    - MLOPS
    COMPLEMENTARY TECHNOLOGIES
    - BLOCKCHAIN
    - EDGE COMPUTING
    - SENSORS AND ROBOTICS
    - CYBERSECURITY
    ADJACENT TECHNOLOGIES
    - PREDICIVE ANALYTICS
    - IOT
    - BIG DATA
    - AR/VR
  • 5.9 TARIFF AND REGULATORY LANDSCAPE
    TARIFF RELATED TO AI PROCESSORS & CONTROLLERS (HSN: 854231)
    REGULATORY BODIES, GOVERNMENT AGENCIES AND OTHER ORGANIZATIONS
    KEY REGULATIONS
    - NORTH AMERICA
    - EUROPE
    - ASIA PACIFIC
    - MIDDLE EAST AND AFRICA
    - LATIN AMERICA
    TRADE ANALYSIS
    - EXPORT SCENARIO OF AI PROCESSORS AND CONTROLLERS
    - IMPORT SCENARIO OF AI PROCESSORS AND CONTROLLERS
    PATENT ANALYSIS
    - METHODOLOGY
    - PATENTS FILED, BY DOCUMENT TYPE, 2016–2025
    - INNOVATION AND PATENT APPLICATIONS
    PRICING ANALYSIS
    - AVERAGE SELLING PRICE OF OFFERING, BY KEY PLAYERS, 2025
    - AVERAGE SELLING PRICE, BY APPLICATION, 2025
    KEY CONFERENCES AND EVENTS, 2025-2026
    PORTER’S FIVE FORCES’ ANALYSIS
    - THREAT FROM NEW ENTRANTS
    - THREAT OF SUBSTITUTES
    - BARGAINING POWER OF SUPPLIERS
    - BARGAINING POWER OF BUYERS
    - INTENSITY OF COMPETITION RIVALRY
    TRENDS/DISRUPTIONS IMPACTING BUYER/CLIENTS OF AI MARKET
    KEY STAKEHOLDERS AND BUYING CRITERIA
    - KEY STAKEHOLDERS IN BUYING PROCESS
    - BUYING CRITERIA
ARTIFICIAL INTELLIGENCE MARKET, BY OFFERING
6
  • 6.1 INTRODUCTION
    OFFERING: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
  • 6.2 INFRASTRUCTURE, BY TYPE
    COMPUTE
    - GRAPHICS PROCESSING UNIT (GPU)
    - CENTRAL PROCESSING UNIT (CPU)
    - FIELD-PROGRAMMABLE GATE ARRAY (FPGA)
    MEMORY
    - DOUBLE DATA RATE (DDR)
    - HIGH BANDWIDTH MEMORY (HBM)
    NETWORKING HARDWARE
    - NIC/NETWORK ADAPTERS
    - INTERCONNECTS
    STORAGE
  • 6.3 INFRASTRUCTURE, BY FUNCTION
    TRAINING
    INFERENCE
  • 6.4 SOFTWARE
    DIGITAL ASSISTANTS & BOTS
    MACHINE LEARNING FRAMEWORKS
    NO-CODE/LOW-CODE ML TOOLS
    COMPUTER VISION PLATFORMS
    DATA PRE-PROCESSING TOOLS
    BUSINESS INTELLIGENCE & ANALYTICS PLATFORMS
    DEVELOPER PLATFORMS
    OTHERS
  • 6.5 SERVICES
    CORE DATA SERVICES
    - DATA COLLECTION & INGESTION
    - DATA PROCESSING & TRANSFORMATION
    - DATA STORAGE & MANAGEMENT
    - DATA SECURITY & PRIVACY
    - DATA GOVERNANCE & QUALITY MANAGEMENT
    - DATA INTEGRATION & INTEROPERABILITY
    - DATA ANNOTATION & TRAINING DATA SERVICES
    INTEGRATED SERVICES
    - AI MODEL DEVELOPMENT & DEPLOYMENT
    - AI MODEL OPTIMIZATION & FINE-TUNING
    - AI SECURITY & COMPLIANCE SERVICES
    - AI SOFTWARE DEVELOPMENT SERVICES
    - SUPPORT & MAINTENANCE SERVICES
ARTIFICIAL INTELLIGENCE MARKET, BY TECHNOLOGY
7
  • 7.1 INTRODUCTION
    TECHNOLOGY: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
  • 7.2 MACHINE LEARNING
    SUPERVISED LEARNING
    UNSUPERVISED LEARNING
    REINFORCEMENT LEARNING
  • 7.3 NATURAL LANGUAGE PROCESSING
    NATURAL LANGUAGE UNDERSTANDING (NLU)
    NATURAL LANGUAGE GENERATION (NLG)
  • 7.4 COMPUTER VISION AI
    OBJECT DETECTION
    IMAGE CLASSIFICATION
    SEMANTIC SEGMENTATION
    FACIAL RECOGNITION
    OTHERS
  • 7.5 CONTEXT-AWARE ARTIFICIAL INTELLIGENCE (CAAI)
    CONTEXT-AWARE RECOMMENDATION SYSTEMS
    MULTI-MODAL AI
    CONTEXT-AWARE VIRTUAL ASSISTANTS
  • 7.6 GENERATIVE AI
ARTIFICIAL INTELLIGENCE MARKET, BY BUSINESS FUNCTION
8
  • 8.1 INTRODUCTION
    BUSINESS FUNCTION: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
  • 8.2 MARKETING AND SALES
    SENTIMENT ANALYSIS
    PREDICTIVE FORECASTING
    CONTENT GENERATION & MARKETING
    AUDIENCE SEGMENTATION & PERSONALIZATION
    CUSTOMER EXPERIENCE MANAGEMENT
    OTHERS
  • 8.3 HUMAN RESOURCES
    ONBOARDING AUTOMATION
    CANDIDATE SCREENING & RECRUITMENT
    PERFORMANCE MANAGEMENT
    WORKFORCE MANAGEMENT
    EMPLOYEE FEEDBACK ANALYSIS
    OTHERS
  • 8.4 FINANCE AND ACCOUNTING
    FINANCIAL PLANNING & FORECASTING
    AUTOMATED BOOK KEEPING & RECONCILIATION
    PROCUREMENT & SUPPLY CHAIN FINANCE
    REVENUE CYCLE MANAGEMENT
    FINANCIAL COMPLIANCE & REGULATORY REPORTING
    OTHERS
  • 8.5 OPERATIONS & SUPPLY CHAIN
    AIOPS
    IT SERVICE MANAGEMENT
    DEMAND PLANNING & FORECASTING
    PROCUREMENT & SOURCING
    WAREHOUSE & INVENTORY MANAGEMENT
    PRODUCTION PLANNING & SCHEDULING
    OTHERS
  • 8.6 OTHERS (CYBERSECURITY, R&D)
ARTIFICIAL INTELLIGENCE MARKET, BY ENTERPRISE APPLICATIONS
9
  • 9.1 INTRODUCTION
    ENTERPRISE APPLICATIONS: MARKET DRIVERS
  • 9.2 BFSI
    FRAUD DETECTION AND PREVENTION
    RISK ASSESSMENT AND MANAGEMENT
    ALGORITHMIC TRADING
    CREDIT SCORING AND UNDERWRITING
    CUSTOMER SERVICE AUTOMATION
    PERSONALIZED FINANCIAL RECOMMENDATIONS
    INVESTMENT PORTFOLIO MANAGEMENT
    REGULATORY COMPLIANCE MONITORING
    OTHERS
  • 9.3 RETAIL & E-COMMERCE
    PERSONALIZED PRODUCT RECOMMENDATION
    CUSTOMER RELATIONSHIP MANAGEMENT
    VISUAL SEARCH
    VIRTUAL CUSTOMER ASSISTANT
    PRICE OPTIMIZATION
    SUPPLY CHAIN MANAGEMENT & DEMAND PLANNING
    VIRTUAL STORES
    OTHERS
  • 9.4 TRANSPORTATION AND LOGISTICS
    ROUTE OPTIMIZATION
    INTELLIGENT TRAFFIC MANAGEMENT
    SMART LOGISTICS AND WAREHOUSING
    SUPPLY CHAIN VISIBILITY AND TRACKING
    FLEET MANAGEMENT
    OTHERS
  • 9.5 GOVERNMENT & DEFENSE
    SURVEILLANCE AND SITUATIONAL AWARENESS
    LAW ENFORCEMENT
    INTELLIGENCE ANALYSIS AND DATA PROCESSING
    SIMULATION AND TRAINING
    COMMAND AND CONTROL
    DISASTER RESPONSE AND RECOVERY ASSISTANCE
    E-GOVERNANCE AND DIGITAL CITY SERVICES
    OTHERS
  • 9.6 HEALTHCARE & LIFE SCIENCES
    PATIENT DATA AND RISK ANALYSIS
    LIFESTYLE MANAGEMENT AND MONITORING
    PRECISION MEDICINE
    INPATIENT CARE AND HOSPITAL MANAGEMENT
    MEDICAL IMAGING AND DIAGNOSTICS
    DRUG DISCOVERY
    AI-ASSISTED MEDICAL SERVICES
    MEDICAL RESEARCH
    OTHERS
  • 9.7 TELECOMMUNICATIONS
    NETWORK OPTIMIZATION
    NETWORK SECURITY
    CUSTOMER SERVICE AND SUPPORT
    NETWORK PLANNING AND OPTIMIZATION
    NETWORK ANALYTICS
    INTELLIGENT CALL ROUTING
    NETWORK FAULT PREDICTION
    VIRTUAL NETWORK ASSISTANTS
    VOICE AND SPEECH RECOGNITION
    - OTHERS
  • 9.8 ENERGY & UTILITIES
    ENERGY DEMAND FORECASTING
    GRID OPTIMIZATION AND MANAGEMENT
    ENERGY CONSUMPTION ANALYTICS
    SMART METERING AND ENERGY DATA MANAGEMENT
    ENERGY STORAGE OPTIMIZATION
    REAL-TIME ENERGY MONITORING AND CONTROL
    POWER QUALITY MONITORING AND MANAGEMENT
    ENERGY TRADING AND MARKET FORECASTING
    INTELLIGENT ENERGY MANAGEMENT SYSTEMS
    - OTHERS
  • 9.9 MANUFACTURING
    MATERIAL MOVEMENT MANAGEMENT
    PREDICTIVE MAINTENANCE AND MACHINERY INSPECTION
    PRODUCTION PLANNING
    RECYCLABLE MATERIAL RECLAMATION
    PRODUCTION LINE OPTIMIZATION
    INTELLIGENT INVENTORY MANAGEMENT
    OTHERS
    AGRICULTURE
    - CROP MONITORING AND YIELD PREDICTION
    - PRECISION FARMING
    - SOIL ANALYSIS AND NUTRIENT MANAGEMENT
    - PEST AND DISEASE DETECTION
    - IRRIGATION OPTIMIZATION AND WATER MANAGEMENT
    - AUTOMATED HARVESTING AND SORTING
    - WEED DETECTION AND MANAGEMENT
    - WEATHER AND CLIMATE MONITORING
    - LIVESTOCK MONITORING AND HEALTH MANAGEMENT
    - OTHERS
    SOFTWARE & TECHNOLOGY PROVIDERS
    - CODE GENERATION & AUTO-COMPLETION
    - BUG DETECTION & FIXING
    - AUTOMATED SOFTWARE TESTING & QA
    - AI-POWERED CYBERSECURITY & THREAT DETECTION
    - AUTOMATED DEVOPS & CI/CD OPTIMIZATION
    - OTHERS
    MEDIA AND ENTERTAINMENT
    - CONTENT RECOMMENDATION SYSTEMS
    - CONTENT CREATION AND GENERATION
    - CONTENT COPYRIGHT PROTECTION
    - AUDIENCE ANALYTICS AND SEGMENTATION
    - PERSONALIZED ADVERTISING
    - OTHERS
    OTHER ENTERPRISE APPLICATIONS
    ARTIFICIAL INTELLIGENCE MARKET, BY END USER
ARTIFICIAL INTELLIGENCE MARKET, BY END USER
10
  • 10.1 INTRODUCTION
    END USER: MARKET DRIVERS
  • 10.2 CONSUMERS
  • 10.3 ENTERPRISES
    BFSI
    - BANKING
    - FINANCIAL SERVICES
    - INSURANCE
    RETAIL & E-COMMERCE
    - INDUSTRIAL GOODS
    - CONSUMER GOODS
    TRANSPORTATION AND LOGISTICS
    - RAIL
    - ROAD
    - MARINE
    - AIR
    GOVERNMENT & DEFENSE
    - FEDERAL GOVERNMENT
    - STATE & LOCAL GOVERNMENT
    - MILITARY & DEFENSE
    - PUBLIC SERVICE AGENCIES
    HEALTHCARE & LIFE SCIENCES
    - HEALTHCARE PROVIDERS
    - PHARMACEUTICALS & BIOTECH SECTOR
    - MEDTECH
    TELECOMMUNICATIONS
    - NETWORK OPERATORS
    - TELECOM EQUIPMENT PROVIDERS
    - COMMUNICATION SERVICE PROVIDERS
    - DATA & CLOUD CONNECTIVITY PROVIDERS
    ENERGY & UTILITIES
    - OIL AND GAS
    - POWER GENERATION
    - UTILITIES
    MANUFACTURING
    - DISCRETE MANUFACTURING
    - PROCESS MANUFACTURING
    SOFTWARE & TECHNOLOGY PROVIDERS
    - CLOUD HYPERSCALERS
    - FOUNDATION MODEL/LLM PROVIDERS
    - AI TECHNOLOGY PROVIDERS
    - IT & IT-ENABLED SERVICE PROVIDERS
    - MEDIA AND ENTERTAINMENT
    - OTHER ENTERPRISE END USERS
ARTIFICIAL INTELLIGENCE MARKET, BY REGION
11
  • 11.1 INTRODUCTION
  • 11.2 NORTH AMERICA
    NORTH AMERICA: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR NORTH AMERICA
    UNITED STATES
    CANADA
  • 11.3 EUROPE
    EUROPE: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR EUROPE
    UK
    GERMANY
    FRANCE
    ITALY
    SPAIN
    NORDIC
    BENELUX
    - REST OF EUROPE
  • 11.4 ASIA PACIFIC
    ASIA PACIFIC: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR ASIA PACIFIC
    CHINA
    INDIA
    JAPAN
    SOUTH KOREA
    AUSTRALIA & NEW ZEALAND
    ASEAN
    REST OF ASIA PACIFIC
  • 11.5 MDDLE EAST AND AFRICA
    MDDLE EAST AND AFRICA: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR MIDDLE EAST AND AFRICA
    SAUDI ARABIA
    UAE
    SOUTH AFRICA
    TURKEY
    QATAR
    ISRAEL
    REST OF MDDLE EAST AND AFRICA
  • 11.6 LATIN AMERICA
    LATIN AMERICA: ARTIFICIAL INTELLIGENCE MARKET DRIVERS
    MACROECONOMIC OUTLOOK FOR LATIN AMERICA
    BRAZIL
    MEXICO
    ARGENTINA
    CHILE
    REST OF LATIN AMERICA
COMPETITIVE LANDSCAPE
12
  • 12.1 OVERVIEW
  • 12.2 STRATEGIES ADOPTED BY KEY PLAYERS
  • 12.3 REVENUE ANALYSIS FOR KEY PLAYERS, 2020 - 2024
    MARKET SPECIFIC REVENUE ANALYSIS
  • 12.4 MARKET SHARE ANALYSIS, 2024
    MARKET RANKING ANALYSIS
  • 12.5 PRODUCT COMPARATIVE ANALYSIS
  • 12.6 VALUATION AND FINANCIAL METRICS OF KEY AI VENDORS
  • 12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
    STARS
    EMERGING LEADERS
    PERVASIVE PLAYERS
    PARTICIPANTS
    COMPANY FOOTPRINT: KEY PLAYERS, 2024
    - COMPANY FOOTPRINT
    - REGION FOOTPRINT
    - OFFERING FOOTPRINT
    - TECHNOLOGY FOOTPRINT
    - BUSINESS FUNCTION FOOTPRINT
    - END USER FOOTPRINT
  • 12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
    PROGRESSIVE COMPANIES
    RESPONSIVE COMPANIES
    DYNAMIC COMPANIES
    STARTING BLOCKS
    COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    - DETAILED LIST OF KEY STARTUPS/SMES
    - COMPETITIVE BENCHMARKING OF KEY STARTUPS/SMES
  • 12.9 COMPETITIVE SCENARIO
    PRODUCT LAUNCHES AND ENHANCEMENTS
    DEALS
    OTHERS
COMPANY PROFILES
13
  • 13.1 INTRODUCTION
  • 13.2 KEY PLAYERS
    GOOGLE
    MICROSOFT
    IBM
    ORACLE
    AWS
    INTEL
    SALESFORCE
    SAP
    AMD
    - QUALCOMM
    - CISCO
    - META
    - HPE
    - SIEMENS
    - NVIDIA
    - BAIDU
    - SAS INSTITUTE
    - OPEN AI
    - HUAWEI
    - ALIBABA CLOUD
    - CENTIFIC
    - FRACTAL ANALYTICS
    - TIGER ANALYTICS
    - QUANTIPHI
    - DATABRICKS
    - IMERIT
    - TELUS INTERNATIONAL
    - INNODATA
    - SAMA
  • 13.3 SMES/START-UPS
    C3.AI
    HQE SYSTEMS
    APPIER
    ADEPT
    H2O.AI
    SPOT AI
    ANTHROPIC
    COHERE
    INBENTA
    - CHARACTER.AI
    - DEEPL
    - INFLECTION AI
    - ARROW AI
    - OBSERVE.AI
    - ANYSCALE
    - AI21 LABS
    - PERSADO
    - DIALPAD
    - GRAPHCORE
    - SHIELD AI
    - SCALE AI
    - GAMAYA
    - ARTHUR
    - ADA
    - MOSTLY AI
    - METROPOLIS
    - CEREBRAS
    - JASPER
    - SOUNDFUL
    - WRITESONIC
    - ONE AI
    - SYNTHESIA
    - SNORKEL
    - LABELBOX
    - APPEN
    - COGITO TECH
    - CHETU
ADJACENT AND RELATED MARKETS
14
  • 14.1 INTRODUCTION
  • 14.2 GENERATIVE AI MARKET – GLOBAL FORECAST TO 2030
    MARKET DEFINITION
    MARKET OVERVIEW
  • 14.3 BIG DATA MARKET – GLOBAL FORECAST TO 2028
    MARKET DEFINITION
    MARKET OVERVIEW
APPENDIX
15
  • 15.1 DISCUSSION GUIDE
  • 15.2 KNOWLEDGE STORE: MARKETANDMARKETS’ SUBSCRIPTION PORTAL
  • 15.3 AVAILABLE CUSTOMIZATIONS
  • 15.4 RELATED REPORTS
  • 15.5 AUTHOR DETAILS

 

In the primary research process, a diverse range of stakeholders from both the supply and demand sides of the artificial intelligence ecosystem were interviewed to gather qualitative and quantitative insights specific to this market. From the supply side, key industry experts, such as chief executive officers (CEOs), vice presidents (VPs), marketing directors, technology & innovation directors, and technical leads from vendor companies offering artificial intelligence infrastructure, software & services were consulted. Additionally, system integrators, service providers, and IT service firms that implement and support artificial intelligence were included in the study. On the demand side, input from IT decision-makers, infrastructure managers, and business heads of prominent enterprise end users was collected to understand the user perspectives and adoption challenges within targeted industries.

The primary research ensured that all crucial parameters affecting the artificial intelligence market—from technological advancements and evolving use cases (predictive maintenance, fraud detection, customer service automation, content generation, personalized recommendations, etc.) to regulatory and compliance needs (GDPR, CCPA, Europe AI Act, AIDA, etc.) were considered. Each factor was thoroughly analyzed, verified through primary research, and evaluated to obtain precise quantitative and qualitative data for this market.

Secondary Research

Once the initial phase of market engineering was completed, including detailed calculations for market statistics, segment-specific growth forecasts, and data triangulation, an additional round of primary research was undertaken. This step was crucial for refining and validating critical data points, such as AI offerings (artificial intelligence infrastructure, software & services), industry adoption trends, the competitive landscape, and key market dynamics like demand drivers (growth in adoption of autonomous artificial intelligence, rise of deep learning and machine learning technologies, advancements in computing power and availability of large databases), challenges (lack of transparency and explainability in decision-making process of AI, concerns related to bias and inaccurately generated output, integration challenges and lack of understanding of state-of-the-art systems), and opportunities (advancements in AI-native infrastructure enhancing scalability and performance, expansion of edge AI capabilities for real-time data processing and decision-making, advancements in generative AI to open new avenues for AI-powered content creation).

Primary Research

In the complete market engineering process, the top-down and bottom-up approaches and several data triangulation methods were extensively used to perform the market estimation and market forecast for the overall market segments and subsegments listed in this report. Extensive qualitative and quantitative analysis was performed on the complete market engineering process to record the critical information/insights throughout the report.

Artificial Intelligence (AI) Market Size, and Share

Note: Three tiers of companies are defined based on their total revenue as of 2024; tier 1 = revenue
more than USD 1 billion, tier 2 = revenue between USD 1 billion and 500 million, tier 3 = revenue less than USD 500 million
Source: MarketsandMarkets Analysis

To know about the assumptions considered for the study, download the pdf brochure

Market Size Estimation

To estimate and forecast the artificial intelligence market and its dependent submarkets, both top-down and bottom-up approaches were employed. This multi-layered analysis was further reinforced through data triangulation, incorporating primary and secondary research inputs. The market figures were also validated against the existing MarketsandMarkets repository for accuracy. The following research methodology has been used to estimate the market size:

Artificial Intelligence (AI) Market : Top-Down and Bottom-Up Approach

Artificial Intelligence (AI) Market Top Down and Bottom Up Approach

Data Triangulation

After arriving at the overall market size using the market size estimation processes as explained above, the market was split into several segments and subsegments. To complete the overall market engineering process and arrive at the exact statistics of each market segment and subsegment, data triangulation and market breakup procedures were employed, wherever applicable. The overall market size was then used in the top-down procedure to estimate the size of other individual markets via percentage splits of the market segmentation.

Market Definition

Many theoretical definitions of artificial intelligence center on a machine's capacity to mimic human behavior or carry out tasks that call for intelligence, but given the majority of current applications, artificial intelligence can be described as “systems that employ methods that can gather data and use it to predict, suggest, or make decisions with varying degrees of autonomy and select the best course of action to accomplish particular objectives”. AI systems leverage advanced techniques such as deep learning, reinforcement learning, and probabilistic reasoning to process data, recognize patterns, and make autonomous decisions or provide predictive analytics. These systems are designed to improve over time through iterative training and adaptation, often utilizing large-scale data and high-performance computing infrastructure to optimize performance and accuracy.

Stakeholders

  • AI software developers
  • AI infrastructure providers
  • AI-integrated service providers
  • AI training dataset providers
  • Core data service providers
  • Business analysts
  • Cloud service providers
  • Consulting service providers
  • Enterprise end users
  • Distributors and Value-added Resellers (VARs)
  • Government agencies
  • Independent Software Vendors (ISVs)
  • Managed service providers
  • Market research and consulting firms
  • Support & maintenance service providers
  • System Integrators (SIs)/Migration service providers
  • Language service providers
  • Technology providers
  • Academia & research institutions
  • Investors & venture capital firms

Report Objectives

  • To define, describe, and forecast the artificial intelligence market, by offering, business function, technology, enterprise application, and end user
  • To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
  • To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
  • To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the artificial intelligence market
  • To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
  • To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America
  • To profile the key players and comprehensively analyze their market ranking and core competencies
  • To analyze competitive developments, such as partnerships, product launches, and mergers and acquisitions, in the artificial intelligence market

Available Customizations

With the given market data, MarketsandMarkets offers customizations as per the company’s specific needs. The following customization options are available for the report:

Product Analysis

  • Product matrix provides a detailed comparison of the product portfolio of each company

Geographic Analysis as per Feasibility

  • Further breakup of the North American market for artificial intelligence
  • Further breakup of the European market for artificial intelligence
  • Further breakup of the Asia Pacific market for artificial intelligence
  • Further breakup of the Middle Eastern & African market for artificial intelligence
  • Further breakup of the Latin American market for artificial intelligence

Company Information

  • Detailed analysis and profiling of additional market players (up to five)

 

Previous Versions of this Report

Artificial Intelligence (AI) Market by Offering (Discriminative AI, Generative AI, Hardware, Services), Technology (ML, NLP, Context-aware AI, Computer Vision), Business Function (Marketing & Sales, HR), Vertical and Region - Global Forecast to 2030

Report Code TC 7894
Published in May, 2024, By MarketsandMarkets™

Artificial Intelligence (AI) Market by Offering (Hardware, Software), Technology (ML (Deep Learning (LLM, Transformers (GPT 1, 2, 3, 4)), NLP, Computer Vision), Business Function, Vertical, and Region - Global Forecast to 2030

Report Code TC 7894
Published in Jun, 2023, By MarketsandMarkets™

Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Deployment Mode, Organization Size, Business Function (Law, Security), Vertical and Region - Global forecast to 2027

Report Code TC 7894
Published in Aug, 2022, By MarketsandMarkets™

Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing), Deployment Mode, Organization Size, Business Function (Law, Security), Vertical, and Region - Global Forecast to 2026

Report Code TC 7894
Published in May, 2021, By MarketsandMarkets™

Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025

Report Code SE 4053
Published in Feb, 2018, By MarketsandMarkets™

Artificial Intelligence Market by Technology (Deep Learning, Robotics, Digital Personal Assistant, Querying Method, Natural Language Processing, Context Aware Processing), Offering, End-User Industry, and Geography - Global Forecast to 2022

Report Code SE 4053
Published in Nov, 2016, By MarketsandMarkets™

Artificial Intelligence (AI) Market by Technology (Machine Learning, Natural Language Processing (NLP), Image Processing, and Speech Recognition), Application & Geography - Global Forecast to 2020

Report Code SE 4053
Published in Feb, 2016, By MarketsandMarkets™
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