How artificial intelligence is changing the face of human resources management
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Competitive Differentiation
Publication | Update: Nov 2022
Human resources (HR) is a crucial aspect of any business, and artificial intelligence (AI) has played an important role in improving the department HR. The global HR technology market was valued at $ 24.02 billion in 2021 and is expected to reach .68 billion by 2028, at a CAGR of 5.8 percent from 2022 to 2028, according to a recent report by development consulting firm SkyQuest. The use of AI in HR operations will improve businesses as these apps can assess, anticipate and diagnose to help HR professionals make better decisions and improve the employee experience. These are some of the most common applications of HR technology:
1. Talent Acquisition and Recruitment
The HR department plays a critical role in acquiring talent, as bringing exceptional individuals into the fold will contribute to the future growth of the company. The most widespread application of artificial intelligence in HR is likely to be talent acquisition. AI minimizes the time and effort spent on tedious tasks such as screening applicants, maintaining databases, scheduling interviews and answering and resolving applicant questions. It drastically simplifies the hiring process and the time spent, allowing HR 's team to focus on other important tasks such as sourcing, people management, recruitment marketing and other productive tasks. AI-assisted recruitment facilitates the selection of a candidate who meets most of the company's criteria. The screening approach is therefore simple, efficient and rewarding. Chatbots are used to identify and communicate with those with the greatest potential. These automated chatbots offer jobs and positions to newly hired employees based on their job profile. The most qualified and worthy applicant who exactly matches the job description is selected. As a result, interviews are arranged with the best candidates.
2. Training and skills: personalized career paths
The AI approach to education is leading to a shift from business skills acquisition to career personalization. With the advent of learning analytics, training methods are also evolving. Data tracking learning modalities (the time taken to acquire knowledge and the level of understanding) can be used to map how people learn and personalize recommendations to improve skills. AI is also used to enable employees to move internally, depending on their preferences, skills and available options within the company. AI-based start-ups offer solutions to facilitate mobility management that integrate assessments, training and recommendations on career paths, positions and skills development programmes. Saint-Gobain, a French multinational company, has chosen to harness the power of machine learning to improve its human resources management. A project team with different profiles (HR, data scientists, lawyers, business units, etc.) was assembled with two objectives: To use artificial intelligence to discover talent not recognized by HR and the management teams, and to identify talent at high risk of leaving the company. Confidentiality is guaranteed and no decisions are transferred to machines.
3. Data Aggregation
In today's world, it is quite difficult for HR professionals to just screen applicants and conduct interviews. They also have to check candidates' social media profiles and see if there are any red lights. With AI-powered data aggregation in HR, it is easy and fast to automatically search the myriad databases on social media platforms and other websites. Many companies, including eBay and IBM, are already using this technology to sift through a variety of data sources, gather information about potential job candidates and assess their experience and market value.
4. HR Tech is Getting Smarter
By monitoring hiring decisions, personal development and overall team climate, the new technology enables HR professionals to analyze factors such as effectiveness, efficiency and employee satisfaction. Although some departments at HR are already using AI in their decision-making processes, the technology still needs to be developed further to realize its full potential. Currently, small and medium-sized enterprises have access to data-driven insights that can drive their business and provide them with important information on how their employees and teams should behave at work. However, the technologies are not yet scalable and cannot be deployed across large enterprises. Nonetheless, using these applications will not only drive manufacturers forward, but also help a company better understand its employees and focus on emerging issues.
5. Reducing Bias
Because AI is unbiased, more and more recruiters are relying on it to find candidates for their companies. It works with logic and reasoning, which makes it ideal for recruitment. There are numerous types of bias. For example, recruiters may prefer candidates who speak a different dialect than their own. The bias may be unconscious, but it is still a bias and harms the recruitment process. There are many types of prejudice, such as racial and gender bias. They are all detrimental to a company's success because it has to hire based on merit. The problem can be solved by AI systems. Employer bias can be detected and eliminated by developing algorithms. Recruiters can use this technology to screen rejected applicants who may have been discriminated against. With the help of such an algorithm, the company would be able to hire a more diversified pool of people. AI helps HR managers to engage based on data rather than emotion.
 People matters. (n.d.). HR Tech market to touch .68 billion by 2028 with AI revolution. Retrieved from: https://www.peoplematters.in/article/hr-technology/hr-tech-market-to-touch-3568-billion-by-2028-with-ai-revolution-35244
 Dutta, B. (2021, October). What is the Role of AI in Human Resource Management? Analytics Steps Infomedia LLP. Retrieved from: https://www.analyticssteps.com/blogs/what-role-ai-human-resource-management
 Chevalier, F. (2022, February). AI in HR: How is it really used and what are the risks? HEC Paris. Retrieved from: https://www.hec.edu/en/knowledge/articles/ai-hr-how-it-really-used-and-what-are-risks#:~:text=AI%20in%20practice,is%20%22a%20promising%20market%22.
 Scott, A. (2022, February). Applications of Artificial Intelligence in Human Resource Management. TechTarget, Inc. Retrieved from: https://www.datasciencecentral.com/applications-of-artificial-intelligence-in-human-resource-management/
 Hppy. (2021, December). Possible applications of AI in HR. Retrieved from: https://gethppy.com/hrtrends/possible-applications-of-ai-in-hr
 Goyal, K. (2021, January). Artificial Intelligence in HR: 8 Exciting Applications in 2022. upGrad Education Private Limited. Retrieved from: https://www.upgrad.com/blog/artificial-intelligence-in-hr/
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.