by The generative AI in HR market size is poised to grow by USD 2,091.4 million by 2032 from USD 483.59 million in 2022, exhibiting a CAGR of 15.77% during the forecast period 2023-2032.
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.
Precedence Research has conducted a comprehensive market study that provides valuable insights into the performance of the market during the forecast period. The study identifies significant trends that are shaping the growth of the Generative AI in HR market. In this recently published report, essential dynamics such as drivers, restraints, and opportunities are highlighted for both established market players and emerging participants involved in production and supply.
To begin with, the Generative AI in HR Market report features an executive summary that offers a concise overview of the marketplace. It outlines the key players and industry categories expected to have an impact on the market in the coming years. The executive summary provides an unbiased summary of the market.
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Report Scope of the Generative AI in HR Market:
| Report Coverage | Details |
| Market Size in 2023 | USD 559.85 Million |
| Market Size by 2032 | USD 2,091.4 Million |
| Growth Rate from 2023 to 2032 | CAGR of 15.77% |
| Largest Market | North America |
| Base Year | 2022 |
| Forecast Period | 2023 To 2032 |
| Segments Covered | By Deployment Mode, By Technology, and By Application |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Read More: Silos Market Size To Rake USD 278.28 Bn By 2032
The empirical study on the global Generative AI in HR market primarily focuses on the drivers in subsequent sections. It demonstrates how changing demographics are projected to influence the supply and demand dynamics in the Generative AI in HR Market. Our market report for the Generative AI in HR market also delves into the significant rules and regulations that are likely to impact the future growth of this sector. Moreover, in order to comprehend the underlying demand factors, industry experts have provided insights into its fundamental origins.
Top Key Players:
- IBM Watson
- ADP
- Oracle
- Workday Inc.
- Cornerstone
- SAP SE
Data Sources and Methodology
To gather comprehensive insights on the Global Generative AI in HR Market, we relied on a range of data sources and followed a well-defined methodology. Our approach involved interactions with industry experts and key stakeholders across the market’s value chain, including management organizations, processing organizations, and analytics service providers.
We followed a rigorous data analysis process to ensure the quality and credibility of our research. The gathered information was carefully evaluated, and relevant quantitative data was subjected to statistical analysis. By employing robust analytical techniques, we were able to derive meaningful insights and present a comprehensive overview of the Global Generative AI in HR Market.
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- The research report has been meticulously crafted to provide comprehensive knowledge on essential marketing strategies and a holistic understanding of crucial marketing plans spanning the forecasted period from 2023 to 2032.
Key Features of the Report:
- Comprehensive Coverage: The report extensively encompasses a detailed explanation of highly effective analytical marketing methods applicable to companies across all industry sectors.
- Decision-Making Enhancement: It outlines a concise overview of the decision-making process while highlighting key techniques to enhance it, ensuring favorable business outcomes in the future.
- Articulated R&D Approach: The report presents a well-defined approach to conducting research and development (R&D) activities, enabling accurate data acquisition on current and future marketing conditions.
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
Reasons to Consider Purchasing the Report:
- Enhance your market research capabilities by accessing this comprehensive and precise report on the global Generative AI in HR market.
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- Obtain detailed insights into evolving market trends, current and future technologies, and strategic approaches employed by key players in the global Generative AI in HR market.
- Receive valuable recommendations and guidance for both new entrants and established players seeking further market expansion.
- Discover not only the cutting-edge technological advancements in the global Generative AI in HR market but also the strategic plans of industry leaders.
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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