Is AI-based recruitment a source of competitive advantage?

Executive Summary

Today’s business processes are undergoing rapid change due to the influence of external forces, which in turn are changing the business landscape. One of the new computational tools is AI, which is being actively incorporated into various business processes. Despite the great benefits that AI brings, some business areas still question the feasibility of adopting this tool. This research proposal will investigate whether AI-powered recruitment is a source of competitive advantage for modern companies.

The research proposal seeks to answer this question of learning how AI affects a company’s economic performance. It will also examine how the company’s organizational culture is changing and whether AI can eliminate the biases and discrimination, which tend to occur during the recruitment process. The research will involve 350 HR practitioners, 50 Candidates and 20 CEO/business owners, collecting data through surveys and interviews. The research will take five months to complete and significantly benefit the HR industry, Academic Communality, and General society.

1.     Introduction

Recent rapid advances in machine learning and artificial intelligence capabilities result from the global transformation in the way people work. This transformation, in turn, originates in global turmoil, such as COVID-19 Pandemic (Minbaeva 2021). Since the workforce becomes more distributed globally, companies employ technologies, allowing such geographically decentralized organizations to operate efficiently via digitally created work environments (Dittes & Smolnik, 2019)

Pan et al. (2022) defined AI as a complex algorithm system that replaces human intelligence due to superior cognitive and learning capabilities. Industry experts foresee a significant increase in AI integration in business functionalities within the next decade, contributing to a total global GDP of 14% by 2030. Global IT giants such as Amazon, IBM, Google, Apple, Tesla, etc., are adapting AI in HR and other business functionalities due to the limitless computing power and problem-solving capabilities (Singh & Shaurya, 2021). IBM revealed evidence of how the implementation of AI has changed their HRM performance: reduces 107 million dollar HR costs by integrating AI-powered HRM tools. (Pan et al., 2022).

However, despite the promising benefits of applying AI-powered business tools, industry researchers observe reluctance and resistance to adopting AI, particularly in globally functioning businesses (Singh & Shauraya, 2021). Such relation is based on the subjectivization of computational metrics resulting from cooperative work by the programmers and employers from previous decisions. Industry researches also argues that companies are not ready to holistically integrate AI in their HRM functions due to the lack of a fundamental understanding of technology and know-how methodology. (Pan et al., 2022).

This research addresses the practical question, “Is AI in Recruitment & Selection (R&S) is a source of competitive advantage?”. Answering such a question will enable academic and industrial research to understand AI’s practical role in HRM. Furthermore, researchers may utilize this paper to base their further exploration of AI in HRM, particularly in determining the methodologies and tools for successful implementation of such technologies onto other business functionalities. This research will also help companies considering implementing AI-powered recruitment to understand if they can take advantage and invest in such an approach or stick to the traditional recruitment methods.

The current state of academic foundation, technological development and industry evidence, allows to assume that AI can be a source of competitive advantage. However, this statement applies only to those companies that seek to achieve a particular level of automation of their recruitment processes and have a high level of expertise in IT sector. Hence, companies like IBM, Apple, Google, and other technological giants certainly achieve a competitive advantage, allowing AI to execute routine operations and save costs allocated to more strategic activities. Small companies, however, can take advantage of cloud versions of AI-based recruitment solutions to avoid significant expenses on the development of in-house solutions. It is assumed that the main resistant factor to the implementation of AI is defective data sets, which AI uses to learn and establish a decision-making framework. If data is based on past decisions, including bias or discrimination tendencies or any other negative traits, it can extrapolate this inclination to future decisions.

2.     Research Aims and Objectives

Considering the current state of academic foundation in the field of the implication of the AI-based tools, a lack of clear understanding of whether AI-based recruitment is a source of competitive advantage for companies can be observed. Most of the research assesses the benefits and capabilities of AI (Zinsmeister, Yeung & Garrett, 2019). Another develops algorithms and architecture for the neural network that can be used to establish match-making recruiter mechanisms (Quin et al. 2019). Even establishing methodologies and recommendations for companies (Nigam & Saxena, 2019) does not give a clear picture of whether organizations can compete on the market by implementing AI-powered recruitment.

Therefore, this research aims to determine whether AI-powered recruitment is a source of competitive advantage by covering the following aspects:

 2.1. Adoption of AI in R&S.

The existing HRM academic foundation fails to provide an adequate theory and characteristics of AI-powered recruitment, making it hard to establish an informational background in relation to the adoption of AI in R&S procedures (Pan et al., 2022). Some studies indicated that most HR practitioners and experts agree that AI brings significant advantages to companies and that development in this field attracts a considerable amount of investment.Despite that, many factors are hindering the development and adoption of AI. First is the complexity and difficulties associated with using AI by HR practicians who are not technologically advanced. The second factor is company resources, and despite the apparent obviousness, even mature companies are not strategically prepared for the development and integration of AI in R&S practices.

As can be seen, most researchers base the advantages of AI in R&S on the example of adoption by tech giants, ignoring companies that do not possess advanced technological equities and knowledge bases. Thus, it is required to establish a relationship between the level of AI adoption in R&S practices and the advantages that this technology brings to the companies that are not at the forefront of technological progress.

2.2.  Ongoing problems with AI in the recruitment processes

Academics identified the main problems associated with slowing down the process of AI-based recruitment adoption: Biases, Discrimination and Data insecurity. Considering the resistance of such drawbacks caused by the imperfection of the AI model, the proposed research aims to determine whether these issues are only a social factor or they also decrease the productivity of organizations, particularly whether this reduces financial performance. Such a goal will make it possible to distinguish social issues and organizational performance, which will isolate AI as a potential source of competitive advantage.

2.3. Economic performance of companies.

To answer the main research question, the study needs to clarify what competitive advantage is and the options for companies to gain it. Adopting AI-powered recruitment and selection affects organizations in aspects such as:

1. Financial performance. Investment, development, and adoption of AI-powered R&S lead to increasing the organization’s financial performance by improving the quality of human resources, speed of recruitment, and recruitment costs.
2. Organizational culture. Delegating HR sourcing to digital recruiters (powerful computing capabilities) could establish a favorable corporate culture, reduce staff turnover, and increase employee performance and satisfaction. In turn, all these factors will be considered a source of competitive advantage in the HR stage of the value chain.

3.     Background

Academics have significantly advanced in their theoretical exploration of Artificial Intelligence and its’ implications in HRM and R&S. The existing knowledge of this topic originated from the forces that cause such a rapid digitalization of business models and disruption of HR practices. A significant contribution to the development of digitalization theory was made by Minbaeva (2021). Minbaeva (2021) and Amonkwah-Amoah et al. (2021) connected the dots between remarkable events that happened in recent years. They concluded that COVID-19 is one of the leading causes that affect the corporate environment and how people work nowadays. In turn, Ritter & Peresen (2020) emphasized the phenomenon of data-driven disruption that is currently taking place in business practices. This disruption is crucial for companies to develop and integrate AI as an enabler of high-power computing capabilities for organizations.

Black & Esch (2020) have found traces of shifting the sources of companies’ success. They identified that in the course of history until the end of the 20^th century, the success factors for business were tangible assets, such as factories, inventory, equipment, etc. However, in recent years Human Resources have become the fundamental and central source of competitive advantage. This finding becomes the basis for further investigating the interconnection between AI, Recruitment, and Competitive Advantage.

Breaugh (2013) considered the properties of the recruitment process from two perspectives: candidate and employer. He identified that candidate’s primary consideration for the recruitment is the psychological factor. Thus, to produce a favorable outcome from the recruitment process, the organization has to put effort into making a positive impression on the organizational working environment. In this vein, the research aims to identify how candidates perceive the presence of AI-powered recruitment at the organization. On the other hand, companies consider the utilization of AI-powered recruitment as a purely cost-effective instrument to achieve business goals (Zinsmeister, Yeung and Garrett, 2019). This is another consideration for AI-powered recruitment to determine whether it can be a source of competitive advantage for modern companies.

Considering the emergency of the social issues in highly-diversified societies, modern companies searching for tools will help reduce/eliminate biases and discriminations in their HR practices (Kochling & Wehner, 2020). However, due to how AI works, it can be a doubtful solution for companies. Dastin (2018) raised the case of women’s discrimination in Amazon’s AI-powered approach to recruitment. Kochling & Wehner (2020) shed light on the reason for this case, which turned out to be biased data on which AI was trained to make a hiring decision. If the data consists of past decisions with biases or discrimination in their framework, AI blindly absorbs it as the only correct approach.

Kochling & Wehner (2020) and Pillai & Sivathanu (2020) characterized the current research development of AI that is advanced in domains such as Image and Speech Recognition, Machine Learning (ML), and Natural Language Processing (NLP). Charlier & Kloppenburg (2017), in turn, framed three AI types that are currently applied in R&S: Augmented AI (immersive experience of collaboration between human and computer), Assisted AI (Chat Bots, planners, recommendations), and Autonomous ( The highest level of control over work, fully autonomous decisions). Abert (2019) pointed out three AI-based tools used in R&S: chatbots, CV/Resume screening software, and Applicant Tracking Systems (Task automation).

Modern recruiting is a resource-intensive process. In accordance with current reports, companies spend $4,000 and 40 days to liquidate one open position (find an employee) (ADP 2022)From a purely economic perspective, AI-powered recruitment can be considered a competitive advantage, but only as a tool to increase the efficiency of the decision-making process (Kochling & Wehner, 2020; Albert, 2019). Albert (2019) also stated that in some cases, AI helped companies drastically increase profitability, but only with the right approach to implementation. Thus, it is seen that companies are actively looking for opportunities to adopt AI as one of the tools to gain a competitive advantage. However, there is still a lack of research focused on the implementation of AI in the central point of interaction between candidate and employer, which is the recruitment and selection process.

Since the primary goal of Recruitment and Selection (R&S) is to source the right candidate, most AI implications aim to establish a decision-making framework using Natural Language Processing capabilities, considering the text as the primary source of information (Ivashenko & Milutkin, 2019). A group of researchers (Qin et al., 2018) utilized and synthesized the computational capabilities of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) and developed a “Person-Job-Fit” model for the recruitment approach. This approach allowed to extract text from candidates’ CVs and classify and comprehend the text’s similarities to produce the most prominent pair Candidate-Job.

4. Research Significance and Innovation

Achievement of the main objectives of this research will allow academics to raise the bar of existing knowledge in this field and establish a connection between business goals and Artificial Intelligence as a computing instrument. A further extrapolation of findings will potentially help companies make decisions associated with investments and integrate an AI-powered recruitment system in their HR practices. Additionally, this research will help categorize and classify companies according to their type of activity, size, and HR approach to highlight those that will potentially benefit the most by adopting AI-powered recruitment.

4.1. Significance within the HR field: Development of new approaches

Suppose adoption of AI will be proved to increase the organizations’ performance, which will subsequently make it a source of competitive advantage. In that case, there are several ways to benefit the HR field:

1. Augmented AI is a form of a synergistic unit of human and computer. Successful achievement of research objectives will help HR practitioners establish new recruitment methods/tools that will utilize the benefits of both approaches: AI and Traditional recruitment.
2. Autonomous AI-based recruitment that makes decisions without human intervention. Determining AI-powered recruitment as a source of competitive advantage may be a starting point for further investment in this sector, which subsequently transform HR practices into fully autonomous processes. This can fully revolutionize the approach to recruitment.

4.2. Significance for business

Based on the literature review, it can be observed correlation between the type of business and the level of AI adoption. The research will determine which business segments will benefit the most if their HR practices are moved to AI-based, in particular:

• Return on Investment (ROI)

ROI is a key indicator that can characterize AI as a potential source of competitive advantage. If investment and adoption of AI-powered recruitment increase a company’s profitability, it becomes an attractive tool for other organization.

• Organizational culture

In some applications, AI can become a central point of interaction between candidate and employer. This raises whether AI-powered recruitment can help organizations mitigate discrimination and biases in the workplace. A positive organizational culture with a healthy level of diversity can potentially decrease conflicts and workforce turnover, which will be considered a secondary sign that constitutes a competitive advantage.

Therefore, a thriving research objective will potentially solve acute social issues which benefit the work society. If organizations are sure that AI-powered recruitment can help eliminate discrimination conflicts, they will more willingly invest in adopting such approaches.

4.3. Significance for industry and innovation.

The research outcomes will be significant for the HR industry since they will help establish new theoretical approaches to the implications of AI. First, it will help to clarify whether automation makes recruitment more productive. Second, understanding how different types of AI (Assisted, Augmented, and Autonomous) have the potential to transform the employee experience. Finally, the focus on characteristics of AI-powered recruitment can reveal the opportunities to make an interaction with the computer more human. Raising the level of research and finding intersection points between Business and Science will help promote innovation, which will drive the vehicle of global evolution forward.

5. Research Methods

A qualitative design method will be applied to take the objectives and nature of this research. The focus of the qualitative approach will be on investigating a general perception of the adoption of AI into recruitment procedures by HR practitioners and insights from business owners who have successfully adopted AI-powered recruitment. Additionally, the qualitative research exploration will cover the candidate’s side in order to determine their psychological considerations of being assessed by AI.

The explanatory research approach to the question “Is AI-powered recruitment is a source of competitive advantage?” is seen as a critical association between AI and HR practices and explain why or why not it is a competitive advantage. Therefore, the qualitative design will allow researchers to concentrate their academic efforts on current issues associated with AI integration in recruitment practices. Despite the wide range of goals set for researches, the qualitative nature of this research will not provide academics with numerical data. Instead, the conclusive statement of this research will take the form of a subjective analysis of indicators obtained directly from HR practitioners, which is beneficial for this research. Therefore, the explanatory method will be relevant to this research.

The companies will be selected for the research based on their size, type of business activities, and industry sectors. To develop rational and valid results, the research will cover industries with a high turnover flow and require a significant level of automation: Finance and insurance, Construction, Manufacturing, Information Technologies, Administrative, and Retail. Such sampling will provide the optimal degree of error to ensure valid results. In addition, those companies will be chosen that recently adopted AI in their recruitment processes. These companies will be divided into two parts: those that build in-house AI solutions to keep sensitive data and those that outsource development to third-party vendors. This will help clarify internal security considerations that potentially become resistance to AI adoption.

The data will be collected using surveys and interviews for the qualitative research design. The sample comprises 350 HR practitioners, 20 CEO/ Business Owners and 50 candidates in order to create the most rational sample. HR practitioners and candidates will be approached through e-mail to ask about their willingness to participate in the survey. After their approval, the survey will be sent to them. CEOs/Business owners will be approached by phone and asked for a short interview via phone. Interviewing CEOs/Business owners should include addressing the data from the corporate reports to identify how AI-powered recruitment affected their company’s financial performance.

The first stage of the research is Data collection preparation (Figure 1), where building the research hypothesis and constructing surveys and interview questionnaires occur. After that, the study enters into the stage of collection of data. Parallel to this process, a thematic analysis of gathered data is being executed. The analysis results then being tested against the research hypothesis, which subsequently reveals the answer to the main research question.

Figure 1 Research Timeline

The research project will be considered successful if it clarifies the question “Is AI-powered recruitment is a source of competitive advantage.” Another measurement of success is a value that research will add to the academic and industrial foundation through its’ findings. A positive answer to the question “Yes, AI-powered recruitment is a source of competitive advantage,” this research becomes a basis for strategic decisions by companies. With the mass adaptation of technology, the industry moves to a higher level of development, which is undoubtedly a sign of the success and value of this study. On the other hand, a negative outcome such as “No, AI-powered recruitment is not a source of competitive advantage” will also benefit the companies and industry since it can help save development efforts and resources and redirect them to other sectors.

6.     Conclusion

The controversy around Artificial Intelligence has reached its’ peak. Industry experts and researchers claim AI is a powerful computing tool that can increase the efficiency of many routing tasks, save costs, and improve organizational performance. However, there are vital signs of resistance to the practice called adopting such technologies in various business processes. Organizations whose activities are far from the latest IT technologies have more concerns about adopting AI. Most of them are related to data security issues and the decision-making framework. Thus, applied to HR and recruitment in particular, AI becomes a source of discrimination and biases, which is counter-productive and raises more social issues and concerns. HR practitioners also express considerations regarding the reasonableness of moving toward AI-powered recruitment and the replacement of traditional approaches to recruitment.

This research aims to clarify the current level of interaction between AI, Recruitment, and Business outcomes. The direction of this research will come from a fundamental question that is important to the academy and the industry as a whole “Whether AI-powered recruitment is a source of competitive advantage.” To answer this question, it needs to consider the current state of adoption of AI-powered recruitment, ongoing problems with AI in the recruitment processes, and how the company’s performance has changed since adopting AI-powered recruitment.

With the positive development of the research, the outcome will bring a wide range of benefits for business, industry, and society. Revealing AI-powered recruitment as a source of competitive advantage will help HR practitioners develop new recruitment methods, utilizing augmented and autonomous AI capabilities, which will revolutionize the approach to recruitment. Additionally, the business will be more willing to invest and adopt AI in their HR practices if it is officially claimed as a competitive advantage.

To collect the data, a set of questionnaires for interviewing business owners and a short survey for other HR practitioners and candidates will be developed. The research will be divided into five stages and will take five months to complete. A clear answer to the question “Is AI-powered recruitment is a source of competitive advantage” will be considered a sign of success.

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