April 24, 2024
ICT

Generative AI in HR Market Size to Garner USD 2,091.4 Million by 2032

The global generative AI in HR market size was estimated to be around US$ 483.59 million in 2022. It is projected to reach US$ 2,091.4 million by 2032, indicating a CAGR of 15.77% from 2023 to 2032.

Generative AI in HR Market Size 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 market research report on the Generative AI in HR market provides a comprehensive analysis of various key aspects. It includes the definition, classification, and application of Generative AI in HR products. The report examines the development trends, competitive landscape, and industrial chain structure within the industry. Furthermore, it presents an overview of the industry, analyzes national policies and planning, and offers insights into the latest market dynamics and opportunities at a global level.

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Generative AI in HR Market Report Scope 

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 Garner USD 278.28 Million by 2032

The report presents the volume and value-based market size for the base year 2022 and forecasts the market’s growth between 2023 and 2032. It estimates market numbers based on product form and application, providing size and forecast for each application segment in both global and regional markets.

Focusing on the global Generative AI in HR market, the report highlights its status, future forecasts, growth opportunities, key market players, and key market regions such as the United States, Europe, and China. The study aims to present the development of the Generative AI in HR market by considering factors like Year-on-Year (Y-o-Y) growth, in addition to Compound Annual Growth Rate (CAGR). This approach enables a better understanding of market certainty and the identification of lucrative opportunities.

Regarding production, the report investigates the capacity, production, value, ex-factory price, growth rate, and market share of major manufacturers, regions, and product types. On the consumption side, the report focuses on the regional consumption of Generative AI in HR products across different countries and applications.

Buyers of the report gain access to verified market figures, including global market size in terms of revenue and volume. The report provides reliable estimations and calculations for global revenue and volume by product type from 2023 to 2032. It also includes accurate figures for production capacity and production by region during the same period.

The research includes product parameters, production processes, cost structures, and data classified by region, technology, and application. Furthermore, it conducts SWOT analysis and investment feasibility studies for new projects.

This in-depth research report offers valuable insights into the Generative AI in HR market. It employs an objective and fair approach to analyze industry trends, supporting customer competition analysis, development planning, and investment decision-making. The project received support and assistance from technicians and marketing personnel across various links in the industry chain.

The competitive landscape section of the report provides detailed information on Generative AI in HR market competitors. It includes company overviews, financials, revenue generation, market potential, research and development investments, new market initiatives, global presence, production sites, production capacities, strengths and weaknesses, product launches, product range, and application dominance. However, the data points provided only focus on the companies’ activities related to the Generative AI in HR market.

Prominent players in the market are expected to face tough competition from new entrants. Key players are targeting acquisitions of startup companies to maintain their dominance. The report

Reasons to Purchase this Report:

  • Comprehensive market segmentation analysis incorporating qualitative and quantitative research, considering the impact of economic and policy factors.
  • In-depth regional and country-level analysis, examining the demand and supply dynamics that influence market growth.
  • Market size in USD million and volume in million units provided for each segment and sub-segment.
  • Detailed competitive landscape, including market share of major players, recent projects, and strategies implemented over the past five years.
  • Comprehensive company profiles encompassing product offerings, key financial information, recent developments, SWOT analysis, and employed strategies by major market players.

Key Players

  • IBM Watson
  • ADP
  • Oracle
  • Workday Inc.
  • Cornerstone
  • SAP SE

Generative AI in HR Market Segmentations

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

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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Prathamesh

I have completed my education in Bachelors in Computer Application. A focused learner having a keen interest in the field of digital marketing, SEO, SMM, and Google Analytics enthusiastic to learn new things along with building leadership skills.

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