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Generative AI Market Report 2025-2030

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A 263-page report on the enterprise Generative AI market, incl. market sizing & forecast, competitive landscape, end user adoption, trends, challenges, and more.
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Document type: PDF, XLSX, PPTX
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Published: January 2025
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Main author: Joaquin Fernandez
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About the report

The Generative AI Market Report 2025-2030 is part of IoT Analytics’ ongoing coverage of enterprise technology markets. The information presented in this report is based on the results of secondary research and qualitative research, i.e., interviews with experts with experts in the field. The main purpose of this document is to help our readers understand the current Generative AI (GenAI) landscape and potential use cases.​

Generative AI definition understanding relationship with AI ML and DL

What is Generative AI?

GenAI is a deep-learning technique based on variational autoencoders, generative adversarial networks, and transformer-based models.

What is a Data Center GPU?

The market segment for data center GPUs refers to specialized graphics processing units designed to handle the extensive computational demands of modern data centers. These GPUs are engineered to accelerate a variety of complex workloads, including high-performance computing, DL, ML, and large-scale graphics processing tasks. The market does not include spending on CPUs, consumer-grade GPUs, or application-specific integrated circuits (ASICs). It includes GPU systems such as specialized GPU server racks. The market only includes external spending but not spending on developing own chips e.g., Google’s TPUs or AWS’ Trainium or Inferentium.

What are foundational models and model management platforms?

This market segment includes both foundational models and model management platforms.

  1. Foundational models are large-scale, pre-trained models that can be adapted to a wide variety of tasks without the need for training from
    scratch, such as language processing, image recognition, and decision-making algorithms.
  2. Model management platforms are software platforms that enable users to deploy, fine-tune, and call GenAI models. Model management
    platforms allow the use of different GenAI models and are not limited to one single model vendor.
    The market does not include chatbots and applications such as ChatGPT.

What are Gen AI services?

GenAI services represent a specialized market segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate GenAI capabilities. These services are tailored to help businesses conceptualize, develop, and execute strategies that leverage GenAI technologies for enhanced innovation, efficiency, and value creation. Services includes consulting, integration, and managed services.

Five building blocks make up the Generative AI stack

Generative AI Tech StackThe GenAI tech stack includes 5 building blocks:

  1. Applications (e.g., AI-powered software solutions)
  2. Platform tools for deployment and management
  3. Foundation models like OpenAI’s GPT 4
  4. Critical backend infrastructure such as data processing and GPUs
  5. Governance frameworks for security and compliance

The report includes a structured repository of 530 generative AI projects*

Database structure

Column name Description
Company Name of the company that implemented the project.
Industry (ISIC classification) Industry classification (ISIC code) of the customer
Project description A brief description of the project
Country Country that the project took place in
Region Region that the project took place in
Vendor Name of the vendor that has published the case study/project on their website
Year Year that the project was implemented
Link Unique identifier of each case study/project
Key department and activities that are improved by each project Each project is grouped into one or more of the follogin departments: Sales, Marketing, Operations/mfg, Maintenance/field service, Finance and account, Human resources, IT/technology, Research and development, Customer service/support, Legal and compliance, Procurement, Logistics and supply chain, Corporate strategy/business development, Facility management. A project can touch mulitple departments. Each department is broken down into key activities.

The database is suited for

  • AI strategy/business case development
  • Sector scan+Customer/vendor selection
  • Competitive analysis
  • Go-to-market/market entry strategy
  • And more

 

*As part of the team user license and enterprise premium license.

Table of Contents

Generative AI Market Report (PDF)

  1. Executive Summary
  2. Introduction
      1. Chapter overview: Introduction
      2. Starting point: Understanding GenAI and its relationship with AI, ML, and DL
      3. The history of GenAI
      4. Interest in GenAI
      5. Investments in GenAI start-ups
      6. AI advances: (Gen)AI surpasses human capabilities in many tasks
      7. GenAI models
      8. GenAI adoption by industry
      9. GenAI adoption by business function
      10. Negative consequences of GenAI adoption
      11. GenAI model building/integration approaches
      12. Case study: AI at Thomson Reuters
      13. Beneficiaries of GenAI tech spending
    1. Technology overview
      1. Chapter overview: Technology Overview
      2. The GenAI tech stack: 5 main blocks
      3. Foundation models: The transformer architecture
      4. Foundation models: What are foundation models?
      5. Foundation models: Type – Language models
      6. Foundation models: Type – Vision models
      7. Foundation models: Type – Speech/audio models
      8. Foundation models: Type – Multimodal models
      9. Foundation models: Type – Industry-specific models
      10. Foundation models: Optimization techniques
      11. Foundation models: Comparing GenAI models
      12. Foundation models: Best-performing models
      13. Foundation models: Open models
      14. GenAI software ecosystem: The five main types of platforms
      15. GenAI software ecosystem: The foundation model value chain
      16. GenAI software ecosystem: Databases
      17. GenAI software ecosystem: IaaS/GPU-as-a-service
      18. GenAI software ecosystem: Development platforms
      19. GenAI software ecosystem: Middleware & integration tools
      20. Computing hardware: AI chips overview
      21. Computing hardware: Types of AI chips and their capabilities
      22. Computing hardware: AI chips’ power consumption
      23. Computing hardware: Training vs. Inference
      24. Computing hardware: NVIDIA vs. AMD chips
      25. Computing hardware: Emergence of new AI chips
      26. Computing hardware: GPU types in research papers
      27. Computing hardware: Data center infrastructure
    2. Market model & outlook
      1. Chapter overview: Market model & outlook
      2. GenAI enterprise market: What is included and what is not
      3. GenAI market 2022–2030
      4. 1. Data center GPU market: Overview
      5. 1. Data center GPU market: By customer group
      6. 2. Foundation models & model mgmt. platforms market: Overview
      7. 2. Foundation models & model mgmt. platforms market: By vertical
      8. 2. Foundation models & model mgmt. platforms market: By region
      9. 2. Foundation models & model mgmt. platforms market: By country
      10. 3. GenAI services market: Overview
      11. 3. GenAI services market: By vertical
      12. 3. GenAI services market: By region
      13. 3. GenAI services market: By country
      14. Perspective: GenAI spending in relation to global software and services spending
    3. Competitive landscape
      1. Chapter overview: Competitive landscape
      2. Competitive landscape 2024: Market Share Overview
      3. Data center GPUs: Competitive landscape (revenue)
      4. Data center GPUs: Competitive landscape (market share)
      5. Data center GPUs: NVIDIA
      6. Data center GPUs: AMD
      7. Data Center GPUs: Intel
      8. Data Center GPUs: Cerebras
      9. Data center GPUs: Groq
      10. Foundation models & model mgmt. platforms: Competitive landscape
      11. Foundation models & model mgmt. platforms (market share)
      12. Foundation models & model mgmt. platforms: Best LLMs
      13. Foundation models & model mgmt. platforms: Leading open models
      14. Foundation models & model mgmt. platforms: Microsoft
      15. Foundation models & model mgmt. platforms: AWS
      16. Foundation models & model mgmt. platforms: Google
      17. Foundation models & model mgmt. platforms: OpenAI
      18. Foundation models & model mgmt. platforms: Hugging Face
      19. Foundation models & model mgmt. platforms: Mistral AI
      20. Overview of key software platforms for GenAI: 1. Development Platforms
      21. Overview of key software platforms for GenAI: 2. Data Management Tools
      22. Overview of key software platforms for GenAI: 3. AI IaaS, GPU-as-a-Service
      23. Overview of key software platforms for GenAI: 4. Middleware & Integration
      24. Overview of key software platforms for GenAI: 5. MLOps
      25. How CEOs discuss selected LLMs and LLM providers
      26. GenAI services: Competitive landscape
      27. GenAI services: Competitive landscape (market share)
      28. GenAI services: Accenture
      29. GenAI services: Deloitte
      30. GenAI services: Capgemini
      31. GenAI services: IBM
    4. End user adoption
      1. Chapter overview: End user adoption
      2. Analysis of 530 GenAI projects: Overview
      3. Analysis of 530 GenAI projects: By department
      4. Analysis of 530 GenAI projects: By department and activity
      5. Analysis of 530 GenAI projects: By industry
      6. Analysis of 530 GenAI projects: By industry and department
      7. Analysis of 530 GenAI projects: Crossing the chasm
      8. Key case studies: Example – Klarna
      9. Key case studies: Example – Westnet
      10. Key case studies: Example – Covered California
      11. Manufacturing deep dive: Overview of 20 GenAI solutions at HMI 24
      12. Manufacturing deep dive: GenAI solutions highlighted at HMI 2024
      13. Manufacturing deep dive: Case study – Siemens
      14. Manufacturing deep dive: Survey stats – Top AI use cases in manufacturing
      15. Tech & TelCo deep dive: GenAI solutions highlighted at MWC 2024
      16. Tech & TelCo deep dive: Case study 1 – Vodafone
      17. Tech & TelCo deep dive: Case study 2 – Soracom
      18. Tech & TelCo deep dive: Case study 3 – SAP
    5. GenAI applications landscape & business model considerations
      1. Chapter overview: GenAI application landscape & business model considerations
      2. GenAI applications landscape 2024
      3. Considerations on GenAI business models
      4. Consideration 1
      5. Consideration 2
      6. Consideration 3
      7. Consideration 4
      8. Consideration 5
      9. Consideration 6
      10. Consideration 7
    6. Trends & challenges
      1. Chapter overview: Trends & challenges
      2. Trend 1
      3. Trend 2
      4. Trend 3
      5. Trend 4
      6. Trend 5
      7. Trend 6
      8. Trend 7
      9. Trend 8
      10. Trend 9
      11. Challenge 1
      12. Challenge 2
      13. Challenge 3
      14. Challenge 4
      15. Challenge 5
      16. Challenge 6
      17. Challenge 7: Other challenges
    7. Methodology
    8. About IoT Analytics

Authors

Joaquin Fernandez, Knud Lasse Lueth, Philipp Wegner

Questions answered

  • What is GenAI, and what are its technological components?
  • Which GenAI use cases and applications are being prioritized by enterprises right now?
  • What is the current market size for GenAI, and what are the market shares of key players?
  • Who is leading the market for GenAI models and platforms?
  • Which companies offer AI accelerators beyond NVIDIA?
  • Which consulting and professional services companies are selling the most GenAI projects?
  • How do the leading GenAI models compare?
  • What are some of the important implementation considerations for GenAI?​
  • What are the current and next trends and challenges around GenAI?​

Related reading

Companies mentioned

A selection of companies mentioned in the report.

AMD

AWS

Accenture

Alibaba

Anthropic

Baidu

Capgemini

Cerebras

Cognizant

Cohere

Google

Groq

Huawei

Hugging Face

IBM

Infosys

Microsoft

Nvidia

OpenAI

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