According to the research report, the global generative AI in HR market size is expected to touch USD 2,091.4 Million by 2032, from USD 483.59 Million in 2022, growing with a significant CAGR of 15.77% from 2023 to 2032.
Key Takeaways:
- North America is expected to dominate in the global market during the forecast period.
- By deployment mode, the cloud-based segment contributed more than 69% of revenue share in 2022.
- By technology, the machine learning segment shows a leading growth in the generative AI in HR market.
- By application, the recruiting and hiring segment shares the maximum CAGR during the projection period.
The generative AI in HR report offers a comprehensive study of the current state expected at the major drivers, market strategies, and key vendors’ growth. The report presents energetic visions to conclude and study the market size, market hopes, and competitive surroundings. The research also focuses on the important achievements of the market, research & development, and regional growth of the leading competitors operating in the market. The current trends of the global generative AI in HR in conjunction with the geographical landscape of this vertical have also been included in this report.
The report offers intricate dynamics about different aspects of the global generative AI in HR market, which aids companies operating in the market in making strategic development decisions. The study also elaborates on significant changes that are highly anticipated to configure growth of the global generative AI in HR during the forecast period. It also includes a key indicator assessment that highlights growth prospects of this market and estimates statistics related to growth of the market in terms of value (US$ Mn) and volume (tons).
Sample Link @ https://www.precedenceresearch.com/sample/3113
This study covers a detailed segmentation of the global generative AI in HR market, along with key information and a competition outlook. The report mentions company profiles of players that are currently dominating the global generative AI in HR market, wherein various developments, expansions, and winning strategies practiced and implemented by leading players have been presented in detail.
Key Players
- IBM Watson
- ADP
- Oracle
- Workday Inc.
- Cornerstone
- SAP SE
Market Segmentation
By Deployment Mode
- Cloud-based
- On-premise
By Technology
- Machine Learning
- Natural Language Processing
- Deep Learning
- Computer Vision
- Robotic Process Automation
By Application
- Recruiting and Hiring
- Performance Management
- Onboarding
- Improved Efficiency
By Geography
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East and Africa
Research Methodology
The research methodology adopted by analysts for compiling the global generative AI in HR report is based on detailed primary as well as secondary research. With the help of in-depth insights of the market-affiliated information that is obtained and legitimated by market-admissible resources, analysts have offered riveting observations and authentic forecasts for the global market.
During the primary research phase, analysts interviewed market stakeholders, investors, brand managers, vice presidents, and sales and marketing managers. Based on data obtained through interviews of genuine resources, analysts have emphasized the changing scenario of the global market.
For secondary research, analysts scrutinized numerous annual report publications, white papers, market association publications, and company websites to obtain the necessary understanding of the global generative AI in HR market.
TABLE OF CONTENT
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology (Premium Insights)
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Generative AI in HR Market
5.1. COVID-19 Landscape: Generative AI in HR Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Generative AI in HR Market, By Deployment Mode
8.1. Generative AI in HR Market, by Deployment Mode, 2023-2032
8.1.1 Cloud-based
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. On-premise
8.1.2.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global Generative AI in HR Market, By Technology
9.1. Generative AI in HR Market, by Technology, 2023-2032
9.1.1. Machine Learning
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Natural Language Processing
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Deep Learning
9.1.3.1. Market Revenue and Forecast (2020-2032)
9.1.4. Computer Vision
9.1.4.1. Market Revenue and Forecast (2020-2032)
9.1.5. Robotic Process Automation
9.1.5.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global Generative AI in HR Market, By Application
10.1. Generative AI in HR Market, by Application, 2023-2032
10.1.1. Recruiting and Hiring
10.1.1.1. Market Revenue and Forecast (2020-2032)
10.1.2. Performance Management
10.1.2.1. Market Revenue and Forecast (2020-2032)
10.1.3. Onboarding
10.1.3.1. Market Revenue and Forecast (2020-2032)
10.1.4. Improved Efficiency
10.1.4.1. Market Revenue and Forecast (2020-2032)
Chapter 11. Global Generative AI in HR Market, Regional Estimates and Trend Forecast
11.1. North America
11.1.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.4. U.S.
11.1.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.1.5. Rest of North America
11.1.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.1.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.1.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2. Europe
11.2.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.4. UK
11.2.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.5. Germany
11.2.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.6. France
11.2.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.2.7. Rest of Europe
11.2.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.2.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.2.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.3. APAC
11.3.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.4. India
11.3.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.5. China
11.3.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.6. Japan
11.3.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.3.7. Rest of APAC
11.3.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.3.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.3.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.4. MEA
11.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.4. GCC
11.4.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.5. North Africa
11.4.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.6. South Africa
11.4.6.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.6.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.6.3. Market Revenue and Forecast, by Application (2020-2032)
11.4.7. Rest of MEA
11.4.7.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.4.7.2. Market Revenue and Forecast, by Technology (2020-2032)
11.4.7.3. Market Revenue and Forecast, by Application (2020-2032)
11.5. Latin America
11.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.4. Brazil
11.5.4.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.4.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.4.3. Market Revenue and Forecast, by Application (2020-2032)
11.5.5. Rest of LATAM
11.5.5.1. Market Revenue and Forecast, by Deployment Mode (2020-2032)
11.5.5.2. Market Revenue and Forecast, by Technology (2020-2032)
11.5.5.3. Market Revenue and Forecast, by Application (2020-2032)
Chapter 12. Company Profiles
12.1. IBM Watson
12.1.1. Company Overview
12.1.2. Product Offerings
12.1.3. Financial Performance
12.1.4. Recent Initiatives
12.2. ADP
12.2.1. Company Overview
12.2.2. Product Offerings
12.2.3. Financial Performance
12.2.4. Recent Initiatives
12.3. Oracle
12.3.1. Company Overview
12.3.2. Product Offerings
12.3.3. Financial Performance
12.3.4. Recent Initiatives
12.4. Workday Inc.
12.4.1. Company Overview
12.4.2. Product Offerings
12.4.3. Financial Performance
12.4.4. Recent Initiatives
12.5. Cornerstone
12.5.1. Company Overview
12.5.2. Product Offerings
12.5.3. Financial Performance
12.5.4. Recent Initiatives
12.6. SAP SE
12.6.1. Company Overview
12.6.2. Product Offerings
12.6.3. Financial Performance
12.6.4. Recent Initiatives
Chapter 13. Research Methodology
13.1. Primary Research
13.2. Secondary Research
13.3. Assumptions
Chapter 14. Appendix
14.1. About Us
14.2. Glossary of Terms
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