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Industrial AI Market Report 2025-2030

Name: Industrial AI Market Report 2025-2030
SKU: 223161
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A 400-page report on the current state of the industrial AI market, including detailed market sizing, forecasts, vendor market shares, key trends, use cases, adoption statistics, and more.

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Document type: PDF, XLSX, PPTX

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Published: July 2025

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Main author: Fernando Brügge
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Questions answered

What is industrial AI (i.e., an industrial AI definition)?
Which technologies are used for implementing industrial AI projects (including hardware and software deep-dive)?
What is the current industrial AI market size and its forecast (by sub-markets, regions, technologies, industries)?
Who are the key industrial AI vendors and what are their market shares?
What are the 50 most common industrial AI use cases?
What is the perspective of industrial AI end users? What are the factors that facilitate or limit adoption?
How are selected manufacturers adopting industrial AI and what are the details of representative case studies?
How do manufacturers adopt generative AI, edge AI and agentic AI?
What are the key trends & challenges in industrial AI space?

Executive Summary
Introduction
1. Introduction: Chapter overview and key takeaways
2. Understanding AI
3. Types of ML
4. Categories of AI
5. Types of analytics and role of AI
6. Focus of this report: Industrial AI
7. Understanding AI: Non-industrial vs. industrial AI solutions
8. General and industrial AI timeline
9. Industrial AI interest in context
10. Case in point: Industrial AI at a large automotive supplier
Technology overview
1. Technology overview: Chapter overview and key takeaways
2. The industrial AI implementation process – Process overview
3. The industrial AI implementation process – Topics overview
4. Deep dive 1: Common frameworks to determine AI business value
5. Deep Dive 2: AI system requirements – Overview
6. Deep Dive 3: AI chips
7. Deep Dive 4: Build versus buying AI solutions
8. Deep Dive 5: Data management
9. Deep Dive 6: Ingest & prepare data
10. Deep Dive 7: Develop & train models
11. Deep Dive 8: ML Ops
Market size & outlook
1. Market size and outlook: Chapter overview and key takeaways
2. General drivers and inhibitors for the industrial AI market 2025
3. Industrial AI market: What is included and what is not
4. Global industrial AI market: Overall
5. Data in perspective: What the average U.S. manufacturer spends on AI
6. Global industrial AI market: By tech stack
7. Global industrial AI market: By AI type
8. Global industrial AI market: Training by hosting type
9. Global industrial AI market: Inference by hosting type
10. Global industrial AI market: By use case
11. Global industrial AI market: By industry
12. Discrete manufacturing industrial AI market: By ISIC code
13. Hybrid manufacturing industrial AI market: By ISIC code
14. Process manufacturing industrial AI market: By ISIC code
15. Global industrial AI market: By region
16. East Asia & Pacific industrial AI market: By country
17. Europe & Central Asia industrial AI market: By country
18. North America industrial AI market: By country
19. Middle East & North Africa industrial AI market: By country
20. Latin America & Caribbean industrial AI market: By country
21. South Asia industrial AI market: By country
22. Global industrial AI market: By top 10 countries and industry (2024)
23. China industrial AI market: Overall
24. China industrial AI market: By tech stack
25. China industrial AI market: By industry
26. China industrial AI market: By use case
27. USA industrial AI market: Overall
28. USA industrial AI market: By tech stack
29. USA industrial AI market: By industry
30. USA industrial AI market: By use case
31. Germany industrial AI market: Overall
32. Germany industrial AI market: By tech stack
33. Germany industrial AI market: By industry
34. Germany industrial AI market: By use case
35. Japan industrial AI market: Overall
36. Japan industrial AI market: By tech stack
37. Japan industrial AI market: By industry
38. Japan industrial AI market: By use case
39. South Korea industrial AI market: Overall
40. South Korea industrial AI market: By tech stack
41. South Korea industrial AI market: By industry
42. South Korea industrial AI market: By use case
Competitive landscape
1. Competitive landscape: Chapter overview and key takeaways
2. Company landscape: Vendor classifications
3. Methodology: How individual companies were analyzed
4. Example: How this report accounts for Company X 2024 revenues
5. Company landscape: Company database
6. The 15 largest industrial AI vendors: Overview
7. Competitive landscape 2024: Market share overview by tech stack
8. Industrial AI hardware: Processors – Market share
9. Industrial AI hardware: Processors – Company X
10. Industrial AI hardware: Computing systems – Market share
11. Industrial AI software: How to think about the comp. landscape
12. Industrial AI software: Platforms – Market share
13. Industrial AI software: Platforms – Company X
14. Industrial AI software: Platforms – Company X
15. Industrial AI software: Platforms – Upcoming companies
16. Industrial AI software: AI-native Applications
17. Industrial AI services: Market share
18. Industrial AI services: Company X
19. Industrial AI services: Company X – XX showcase
20. Industrial AI services: Company X
21. AI Libraries
Use Cases
1. Use cases: Chapter overview and key takeaways
2. Main industrial AI use cases: Share of industrial AI market 2024
3. Main industrial AI use cases: Definitions
4. Use case 1
5. Use case 1 – Case study
6. Use case 2
7. Use case 2 – Case study
8. Use case 3
9. Use case 3 – Case study
10. Use case 4
11. Use case 5
12. Use case 6
13. Use case 6 – Case study
14. Use case 7
15. Use case 8
16. Use case 9
17. Use case 9 – Case study
18. Use case 10
19. Use case 10 – Case study
20. Other notable case studies
21. Other notable case studies: Focus – Generative AI
Deep dive: Generative AI & agentic AI
1. Generative AI & agentic AI: Chapter overview and key takeaways
2. This chapter looks at GenAI & agentic AI through 5 lenses
3. Analysis of 530 GenAI projects: Overview
4. Analysis of 530 GenAI projects: By Department
5. Analysis of 530 GenAI projects: By department and activity
6. Analysis of 530 GenAI projects: By industry
7. Analysis of 530 GenAI projects: By industry and department
8. Analysis of 530 GenAI projects: Crossing the chasm
9. How to monetize GenAI applications
10. Industrial agentic AI: Overview
11. Industrial agentic AI: Model context protocol (MCP) – Overview
12. Industrial agentic AI: Model context protocol (MCP) – Adoption
13. Industrial agentic AI: MCP – Example
14. Industrial agentic AI: Future vision – 1. Company X
15. Industrial agentic AI: Future vision – 2. Company X
16. Industrial agentic AI: Future vision – 3. Company X
17. Industrial agentic AI: Agentic workflow – Example
18. Industrial GenAI & agentic AI trend 1
19. Industrial GenAI & agentic AI trend 2
20. Industrial GenAI & agentic AI trend 3
21. Industrial GenAI/agentic AI solutions – Overview
22. Industrial GenAI/agentic AI solutions – Solution A
23. Industrial GenAI/agentic AI solutions – Solution B
24. Industrial GenAI/agentic AI solutions – Solution C
25. Industrial GenAI/agentic AI solutions – Solution D
Deep dive: Edge AI
1. Edge AI: Chapter overview and key takeaways
2. Edge AI: Overview
3. What is edge AI?
4. Why edge AI matters?: Reasons for AI coming to the edge
5. Edge AI architectures: Overview
6. Edge AI architectures: Example
7. Edge AI architectures: Stages of edge AI processing
8. Key edge AI technologies: Overview
9. Key edge AI technologies: 1. Technology A
10. Key edge AI technologies: 2. Technology B
11. Key edge AI technologies: 3. Technology C
12. Key edge AI technologies: 4. Technology D
13. Key edge AI technologies: 5. Technology E
14. Key edge AI technologies: 6. Technology F
15. Key edge AI technologies: 7. Technology G
16. Industrial Edge AI Trend 1
17. Industrial Edge AI Trend 2
18. Industrial Edge AI Trend 3
Deep dive: AI in robotics
1. AI in robotics: Chapter overview and key takeaways
2. Company X and Company Y are making robotics the next big thing
3. XX bring generalization and autonomy to robots
4. Overview: Key AI topics for industrial robot OEMs
5. Key robot AI use cases
6. AI setup of leading robot OEMs
7. Trend 1
8. Trend 2
9. Trend 3
Deep dive: AI strategies of select manufacturers
1. AI adoption strategies: Overview
2. Company A
3. Company B
4. Company C
Deep dive: End user insights
1. End-user insights: Chapter overview and key takeaways
2. End-user insights: Overview of the 4 surveys
3. Industrial AI Survey #1: Key Insights
4. Adoption status of AI in key industrial applications
5. Importance of various industrial AI use cases going forward
6. Future training and execution (inference) locations for industrial AI
7. Industrial AI Survey #2: Key Insights
8. Value of AI for troubleshooting/maintenance: Overview
9. Value of AI for troubleshooting/maintenance: By industry
10. Industrial AI Survey #3: Key Insights
11. Industrial AI adoption and plans to expand its use
12. Benefits of industrial AI
13. Benefits of industrial AI: Benefits for workers
14. Non-AI compatible equipment
15. AI challenges and corresponding mitigation actions
16. Industrial AI Survey #4: Key Insights
17. Industrial AI copilots vs. AI agents
18. Adoption of industrial AI by application area
19. Type of industrial AI deployed
20. Barriers for industrial AI adoption
21. Plans to address the industrial AI skills gap
22. Investments plans for industrial AI by application area
Drivers, trends & challenges
Methodology & market definitions
About IoT Analytics

Companies mentioned

A selection from 670 companies mentioned in the report.

AMD

AWS

Accenture

Alibaba

Capgemini

Dell Technologies

Deloitte

Foxconn

Google Cloud

Infosys

Microsoft

NVIDIA

Siemens

Supermicro

TCS

About the report

The Industrial AI Market Report 2025-2030 is part of IoT Analytics’ ongoing coverage of smart manufacturing and AI topics. The information presented in this report is based on the results of multiple surveys, secondary research as well as qualitative research i.e., interviews with experts and end users in the field. The document includes definitions for industrial AI and related topics (Edge AI, AI in robotics, Generative AI), market projections, adoption drivers, competitive landscapes, key trends and developments, and case studies.

This report is the third installment of our dedicated research coverage on industrial AI and related topics, including predictive maintenance, machine vision & robotics, digital twin, and edge AI.

The main purpose of this document is to help our readers understand the current industrial AI landscape by defining, sizing and analyzing the market.

The Industrial AI Market Report 2025-2030

The global industrial AI market, a multi-billion dollar market in 2024, is forecast to experience significant double-digit growth through 2030. This report delivers market data and insights helping decisions makers navigate through the market landscape.

Report highlights:

Market sizing & forecasts: A detailed market model and forecast to 2030, segmented by tech stack (hardware, software, services), AI type, industry, region, and by top five countries.
Competitive landscape: In-depth analysis of the 15 largest vendors with market shares and 30+ upcoming companies.
Use case & adoption analysis: Deep dive into 48 key use cases across 10 categories, enriched with end-user perspectives on adoption drivers and barriers.
Strategic insights: A review of 21 key market trends and 6 challenges shaping the industrial AI space.
Technology deep dives: Dedicated chapters providing in-depth analyses of Generative AI & Agentic AI, Edge AI, and AI in Robotics.
In-depth studies: Features 6 detailed use case studies and 4 deep dives into the AI strategies of leading manufacturers.

The market report comes with the full market model data in EXCEL, a list of 670 industrial AI vendor in EXCEL, and a list of industrial AI projects (only team user and enterprise premium license).

What is industrial AI?

Definition of AI

AI (Artificial Intelligence) is defined as machine driven intelligent behavior that involves the ability to acquire and apply knowledge.

AI consists of an analytics (learning) and an outcome (action/decision/prediction) component:

Analytics corresponds to the data management processes and data science algorithms through which the device learns.
Outcome corresponds to the intelligent behavior, e.g., generating a decision, a prediction, or triggering an action.

Definition of industrial AI

Industrial AI is defined as the application of AI techniques to data generated by operational technology and engineering systems in asset-heavy sectors, optimizing industrial processes at any stage of the product and asset lifecycle.

Operational technology and engineering systems: Control, monitoring, and design platforms that generate real-time and engineering data about physical assets (e.g., PLC, SCADA networks, sensors, CAD/CAE suites, and PLM tools)
Asset-heavy sectors: Industries whose business relies on extensive physical infrastructure and equipment (e.g., discrete and process manufacturing, energy, chemicals, mining, and transportation)
Industrial processes: Technical workflows that create, move, or sustain physical goods and assets (e.g., product design, manufacturing, maintenance, logistics, field service)

Authors

Fernando Brügge, Knud Lasse Lueth, Philipp Wegner, Satyajit Sinha

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