Chapter 1: Introduction
As the fourth industrial revolution or industry 4.0 at its onset, a competitive notion is noted amongst firms to compete in the labour market for selecting most suitable, dynamic as well as talented candidates (Bhardwaj, Singh and Kumar 2020; Yawalkar 2019). Through this study a clear idea on the core context of AI as well as its impact on the selection process of HR will be established with clear analysis on the discussing parameters in an elaborative manner. In this chapter, a basic overview on current notion of AI usage for selecting effective candidates and issues of further AI inclusion will be derived to understand the rationale behind conducting this study. Aim, objectives, research questions, hypothesis and structure of the study will be effectively developed before stating the overall summary of this chapter.
Industry 4.0 components such as cloud computing, big data, and Artificial Intelligence (AI) have redefined various tasks for the Human Resource (HR) managers; especially in the domain of talent acquisition by easing the core process of management (Kim, Wang and Boon 2021; Imran 2021). The common steps followed in a recruitment process includes identification of vacancy, preparing job description, identifying sources of qualified applicants, shortlisting interviews, and making decision about selecting the most suitable candidate. Being a long standing process, the process includes task repetitiveness, high cost, human error as well as biases. However, transforming this specific context to AI based platforms can ensure the recruitment parameter gets bias as well as error free alongside reducing the high costs of manual sorting. Platforms such as Fetcher, XOR, Hiretual, Eight fold, My Interview, Pymetrics, Textio and other AI based recruitment software can be considered as some of the most effective tools that eases the process of integrated selection process. A recent survey by Del Giudice et al. (2021) has demonstrated that 49% of the AI usage in HR programs are for recruiting and hiring process which states that this specific function of HR is significantly influenced with the rapid growth of AI. Even though other aspects of HRM such as HR strategy, employee management, analysing organisational policies, workforce automation and other core contexts; yet, recruitment and selection process is the most critical element that is emphasised effectively.
Figure 1.2: Current usage of AI in different functions of HR
Aim And objectives
The underlying aim of the study lies in evaluating current usage of AI and its impact on the Selection process in HR. The core objectives that will help to attain this aim can be stated as follows:
- To evaluate the existing usage of AI in the selection process
- To analyse the impact of AI usage in the selection process
- To recommend further improvement parameters for organisations planning to incorporate AI for operational progress
AI is contemplated as an effective tool for screening candidates, building relationships, improving the quality of hiring, reducing bias, saves time and optimises cost pertaining to which it is considered in organisations around the world (Pathak and Solanki 2021; Parry and Battista 2019). However, specific usage in the context of selection is unknown; nevertheless, it is significantly used for the overall recruitment process. Besides that, multiple researches have stated that AI would not understand a company’s actual requirements as effectively as a recruiter even though it is programmed with the core parameters of management. This is because intangible aspects of weighing such as organisational culture, organisational values and other underlying parameters cannot be included in the discussing domain (Tewari and Pant 2020; Worthington and Bodie 2017). Apart from this, AI is defined to lag ethical responsibilities to a greater extent pertaining to which; usage of AI in the field of selection process gets hindered. In the field of human resources, 33% of the AI indulgence is seen to have significant value; whereas in 36% of the case the value is moderate. Therefore, in order to improve the overall selection process, developing a clear analytics and management growth with AI is needed.
Figure 1.4: Value of AI in different industries
The overarching research question that will be answered in the study is:
RQ: How AI usage can influence the selection process for organisations?
In order to attain the question, sub-research questions are included in the parameter which can be derived as follows:
- What are the existing usage of AI in the selection process?
- How AI impacts the selection process of an organisation?
- What suggestions can be made for further improvement parameters in organisations to incorporate AI for operational progress?
Even though there are numerous studies that defines that defines the usage of AI as well as impact in the entire process of HR; yet none of them only emphasis on the specific criteria of selection process. Thus, there is a void in terms of understanding how AI helps the context of selecting candidates from the applicants. Upon completion of the study, a clear idea on the core context of individual selection process can be gained. Moreover, a practical instance of different tools and techniques usage for selecting the best suited candidates against a specific vacancy can be suggested.
Structure Of The Study
Chapter 1: Introduction
· Background of the context
· Research aim and objective
· Identification of problems
· Stating reasons behind conduction of the study
· Research question depiction
Chapter 2: Methodology
· Research philosophy depiction
· Research approach depiction
· Research design depiction
· Data collection method analysis
· Data analysis method analysis
· Evaluation of ethical parameters
Chapter 3: Data collection and analysis
· Secondary qualitative data analysis
· Thematic data formation for clearer results
Chapter 4: Conclusion and recommendations
· Conclusion of the entire study
· Linking with objectives
· Recommendations for further strategic improvement
· Future scope determination for the study
Table 1.7: Structure of the study
In this chapter of research methodology, the particular process of addressing, selecting, processing as well as synthesizing information about AI usage as well as impact in the context of selection process will be done. Primarily, a brief idea on core context of research philosophy, approach, and research design will be provided along with justification for choosing specific options. Additionally, the idea on data collection, and data analysis will be defined along with deriving the ethical parameters of consideration as well as limitations of the research to define a clearer demarcation of management.
Aim and Objectives
Research philosophy in common instances tend to deal with the source, nature identification as well as development of knowledge. In essence, a proper research philosophy helps a research by stating the researcher’s belief about a way to gather, analyse and measure a specific data (Mishra and Alok 2017; CuervoÃ¢â‚¬ÂÂCazurra et al. 2017). Generally, research philosophy can be divided into three basic attribute including positivism, interpretivism and pragmatism philosophies. Based on the core requirements of management, a specific research philosophy can be chosen. As this study intends to find genuine knowledge in the context of AI usage and implementation through tautological analysis of existing data, consideration of interpretivism philosophy is taken into account. This is because Interpretivism philosophy states that different people can have multidirectional understanding and experience on a specific matter or of the same objective reality (Ryder et al. 2020; Kaushik and Walsh 2019). The second parameter of the chosen topic being an inferential aspect based on factor like organisation, industry, and others, a concise brief will be discussed in the context for better progress demarcation.
Research approach can be contemplated as associated plan and procedure management for research to improve the method of data collection. Research approaches can be of three basic types including deductive approach, inductive approach and abductive approach (Pidgeon 2019; Wang 2019). In this study, the notion of inductive approach is considered as it can provide a clear as well as concise idea on the core aspects of management for reaching premises for reaching conclusion. This is because; secondary observation of AI usage and impact in the context of selection process from primary qualitative data is planned in this research. Moreover, drawing a general conclusion from the results found in the aspect can be done with the help of inductive research approach (McPhail and Lourie 2017). Likewise, in the discussing domain, consideration of inductive approach can help bringing a proper idea on the context of AI usage in the field of candidate selection. Therefore, consideration of inductive approach for the research can further assist in terms of proceeding with a general conclusion from a set of observation gained from the collected data.
The term research design can be contemplated as one of the most critical component that defines the framework considered for proceeding with a research as well as the techniques chosen by researchers for strategically understanding a particular research (Maxwell 2019). Moreover, proper research design set-ups can ensure establishment of cause-effect relationship among different variables which assist in terms of reaching an articulated conclusive standpoint. Commonly, research design can be divided into four basic aspects including correlational, descriptive, quasi experimental, casual comparative and observation-based conclusive design. In this discussing study of identifying AI usage and its impact in the context of selection process in HR, focus on observation-based conclusive design based analysis will be kept. Through conclusive design of research, existing parameters of AI usage will be highlighted. Moreover, a concise idea on the cause-based articulation of AI usage as well as impact can be eventually gained with the help of the chosen design. Therefore, conclusive design of research that helps to derive the element of existing findings and depicting the actual usage in a consolidated manner is undertaken to provide a concise result.
Data Collection Process
Collecting as well as measuring information associated with a specific variable for further inferring results from that particular parameter can be contemplated as the main idea of data collection process (Bryman 2017). Based on the nature of data, data collection process can be divided into two basic aspects including primary data collection and secondary data collection. In this study, primary data collection process is considered to understand the usage and impact of AI on selection. Qualitative data can also be sub-classified into secondary thematic data and empirical findings (Hameed 2020). In this study, a thematic data collection process pertaining to which understanding on the critical contexts of AI use and its impact on selection process can be found effectively. Considering multicast advantages of using primary data such as ease of access, time saving attribute, generating novel insight as well as reducing information duplication; this study emphasised on the core aspect of primary data collection (Clark and Vealé 2018; Islam and Islam 2020). Consideration of journal article, books, and relevant websites for extracting data about AI usage as well as its impact on the selection process is done in the research which will be further consolidated in the next chapter. The interview questions for this study are developed based on the critical concept of AI’s usage understanding in a step-by-step analytical process. Through aligning the research questions in the mentioned aspect, a proper idea on the actual questions regarding AI usage in selection are primarily made clear. Based on that clarity, a concise brief on this specific aspect of questionnaire development is created.
Data Analysis Process
Cleaning and inferring information from the raw data extracted from primary survey through interview for finding potential answers to specific questions can be contemplated as the main attribute of a data analysis process (Vaclavik, Sikorova and Barot 2018). Data analysis can be of different types including tautological, inferential, analytic based and others. Based on the nature of study and further strategic aspects of study, a concise understanding on the extracted data elements can be found. Likewise, in this study, reliable, relevant and recent journal articles, books as well as websites are thoroughly checked for understanding usage of AI tools along with its impact on selection process. Additionally, a thematic parameter of management is kept in the context for providing a contextual understanding. According to Miles and Huberman (1994), they suggest that to conduct a research by thematic analysis, the research is divided into three different parts. In this study the three parts are:
1) First step, will be transcribing the audio interviews of the 9 participants
2) Data reduction, in which the data collected by the interviews are categorised into themes by summarising and developing themes
3) Finally conclusion drawing, in which the verification of the findings and results will be evaluated by comparing the themes and the responses of the participants. Thus, the analysis of the data is based on these three parts
In research, compliance with ethical parameters is highlighted as one of the most effective requirements for ensuring progress as it sets forth the researcher’s underlying values as well as ensures a research conducted has adhered to the ethical attributes. Commonly, research ethics has several core components such as honesty, objectivity, integrity, openness, and respect for intellectual property, confidentiality as well as responsible publication (Bryman 2017). In this study, the concepts of integrity and respect of intellectual property has been maintained by ensuring all the paraphrased sources of information are cited as well as referenced at the end of the study in an effective manner. Apart from that, avoidance of plagiarism as well as source falsification related aspects are strictly avoided in the study to ensure maintenance of effective ethical parameters.
Findings research gaps holds importance for articulating the findings and analysing chances for further growth (Kaushik and Walsh 2019). Primarily due to time constraints, focus on secondary thematic research has been kept in the study. Additionally, financial factor required to conduct a primary study was absent in the discussing study. Thus, secondary data analysis process conduction was taken into account. Even though the study provides a clear as well as concise idea on the specific aspect of AI usage and its impact; yet due to being secondary study, only existing findings associated with the mentioned topic could have been analysed. With given time and financial support in the context, a more articulated idea on the context of AI usage as well as impact parameters could have been gained.
The data collection and analysis chapter provides a clear idea on the core context of findings gained from different sources to define the research questions (Moser and Korstjens 2018; Mkandawire 2019). Likewise, in this chapter, a concise idea on the critical components of AI usage in selection process by interviewing nine HR professionals working on various companies will be provided. In order to define the themes, a concise code of relevance as well as the theme or concept for proceeding towards the question will be defined followed by inferring those ideas from an analytical viewpoint before stating chapter summary.
Questionnaire for conducting interview or survey can be contemplated as one of the most critical aspects of consideration as it effectively states idea on further strategic development (Childers and Taylor 2021). Likewise, in this study, an open ended questionnaire with seven critical question on different themes of selection process through AI is created for deriving a proper notion of operational parameter. The questionnaire enacted as a piece of operational context for transcribing the idea gained from the interview in a thematic manner (Refer to Appendix 1).
Participants, Transcript And Process
Alpha Financial Markets Consulting
Table 3.3: Participants of the interview
The above table clearly demonstrates the interview participant’s designation which is included to ensure relevance of selecting the interview participants. Additionally, the individual’s names are intentionally avoided to ensure adherence to the context of ethical parameters. Besides that, all the participants are working in different UK based SMEs; therefore, finding their identity from the company website with only their positions would not be possible. The core process considered for conducting the data analysis can be demonstrated as follows:
Figure 3.3: Data collection and analysis process
Themes And Their Analysis
In this specific segment, based on the interview conducted upon nine participants, seven themes are identified. These themes will be presented and discussed in this segment in line with the research questions created in chapter 1. Considering readers prospective to make it easy for them to understand and comprehend the results of this study, the researcher combined the finding and discussion chapter. Therefore, these two chapters are collectively evaluated in one chapter which ensures providing a clarified idea on the core context of operational management. Another reason of combining these two chapter was to give clear understanding of the correlation of the analysis and result to the readers. Hence, in this chapter all the sub-research questions are separated in two broad themes that further defined advantages and disadvantages of usage of AI tools and then those seven themes that are derived from the primary data collection are explored. Further, the supporting quotes are presented for providing a proper inferential parameter of growth which relates to each sub themes which further improves the quality of the synthesis process.
Theme 1: Efficiency (After implementing AI)
One of the theme which this research explored is that how AI tools impact the efficiency of the recruiter in shortlisting the candidate application in selection process. On one hand, AI tools helps in screening multiple profile in no time and providing screened relevant profiles as per the job description. Other hand, it also save recruiters time from evaluating fake candidates profile by identifying and eliminating such profiles. Below are the examples from the participant 2, participant 9 and participant 5 who said that:
Structure of the Study
Participant 2: ‘The AI helps to find the resume which have our criteria’.
Participant 9: ‘Definitely you can use AI tool and you can just shortlist millions of profiles in a minute and the profile could be very crisp.’
Participant 5: ‘We need to hire in a bulk. Identification tool we can see it can easily identify that there is a fake company in the cv. so that way it has saved a lot of time.’
Nevertheless, the other participants provided nominal answers against the question which could not be inferred. This finding is in line with the study of Hmound and Laszlo, (2019) who mentioned that in conventional methods of screening and selection require a lot of human effort that can be utilized in more productive activities. AI provides a very useful solution in this regard as it helps save time, money, and effort. Additionally, in similar context Vedapradha, Hariharan, and Shivakami, (2019) also argued in favour of Advantage of tools in selection process by mentioning that AI tools can complete the selection process in a reasonably short amount of time and money by setting specific traits or filters. It assists in lowering or eliminating time-consuming activities, streamlining and automating resume screening, and more efficiently and effectively matching job requirements and available talents of candidates, allowing professionals to make quick decisions. In the current research participant also supported by remarks from their experience that AI helped them to be more efficient by saving their time from mundane time consuming work of screening thousands of profile in no time.
Theme 2: Hiring Bias (After Transformation Of Manual Selection With AI)
In contrast AI tools are subjected to hiring bias and discrimination as these tools work based on pre-designed algorithms and evaluate data based on the set pattern to draw solution. Therefore, it has high chances of biasness if the data or algorithms are corrupted. Even, researcher could find similar pattern in the interviews of participates 7 and participates 4 that are mentioned here in below example:
Participant 7: ‘So we can't just purely depend on AI. Ai as a tool can multiply the biases if you need, but it depends on the user if they use it to feed the right kind of algorithm, you can get a more appropriate or more optimized answer.’
Participant 4: ‘So in terms of machines don't follow any ethics, So there's definitely a lot of ethical considerations and recruiting which we need to follow, that the whole inclusion diversity aspects are being captured by a tool or not. ‘
This finding is in line with the study of Morgan and Morgan (2000) in that researcher mentioned that human typical traits can be represented in the digitized version of human identity. In support of this limitation Bostrom (2014) in his study also stated another important point that machines making decisions on behalf of humanity might lead to disaster. Even the survey conducted in by HR research institute 2019 of 484 HR professionals and published by the Oracle.com (2021) mentioned that second most adverse impact of AI technologies is that it possibly discriminate due to bad programing or machine learning aspect on the gender, ethnicity or other areas, (Oracle.com. 2021). However, the other participants did not provide further idea on the mentioned context as their responses were minimal and in terms of yes, no answers. Nevertheless, maximum participants demonstrated the context with minimal answers; thus, any specific inferential idea from that context could not be driven. Nevertheless, the synthesis depicted that all the HR professionals believes that hiring bias can be significantly reduced with the help of AI usage in the selection process. Therefore, considering above arguments and the current research participant shows that the major limitation of biasness cannot be completely eliminated from the tools because AI technologies are ultimately developed by the human being.
Chapter 2: Methodology
Productivity (As Compared To Manual Process)
In the second research question researcher tried to understand how useful AI tools are for the recruiters in selection of right candidates. Based on the findings this research clearly showed positive influence of AI tools in the productivity of the recruiters in terms of selecting right candidates. Participants clearly mentioned in the interview that over the time AI tools has become more accurate in fetching relevant results from the big pool of talent data, even AI based assessment tools used by the recruiters also provide more accurate reports about the skill set, knowledge and personality of the candidate that help them to select right talent who is culturally fit and relevant. In support of above finding mention below are the examples from the interview where participant 4, participant 3 and participant 8 stated similar observations.
Participant 4: ‘So what I'm seeing like in last five years, I've seen that the predictions are becoming much more accurate’
Participant 3: ‘So that piece of it, letting AI do that first round is extremely helpful not only in terms of speed but also with accuracy and just being able to do it right.’
Participant 8: ‘AI tools on assessment are 100% sure, there are lot of platform they offer they are 100% curated’
This findings is in line with the survey conducted in 2019, on 484 HR professionals by the Oracle.com (2021), HR Research Institute), from the results it is clearly evidence that there is increase of 8% in HR professionals from previous two years in usage of AI assessment tools because of their accurate in finding appropriate results and these AI assessment tools help them to prioritize candidate applications. In the same study, 36 % of the HR professionals who use AI assessment tools there is increase of 6% from last two years because they believe that these tools help them in providing matching application for the open job requirement, (Oracle.com. 2021). Moreover, as per the findings of current research also highlighted the positive impact of the AI tools because they provide relevant results quickly and effectively. In this specific interview, the fourth interviewee depicted that AI is providing more accurate insights for selection than that of five years ago. On the other hand, the third participant depicted that AI usage can predominantly assist in terms of accuracy and speed. Finally, the eighth participant depicted that AI tools on assessment are most accurate and there are several platforms that offer optimum level of accuracy as well as speed. Thus, based on the responses gained from the mentioned context, it can be defined that implementation of AI based selection method can provide organisations with greater accuracy and speed for strategic furtherance.
Human Element In Final Decision (Measuring Compatibility Of AI In Human Elements)
Considering the second research question, one of the downside came in this research finding that even though AI tool help recruiters to shortlisting and processing the right candidates but AI tools still lacking or yet to reach to that level where it can be considered for the final decision of selection of candidate. Therefore, companies still relay on the human wisdom and intellect for the ultimate decision of hiring. Below mentioned discussion are the examples from the interview of Participant 1 and Participant 6 who established the enlisted finding:
Participant 1: ‘But again, I have to call it whatever output we do use from Pymetrics. It's not a very conclusive. It is one of the data point. But whether a hire or not hire decision is not based on it.’
Participant 6: ‘The downside of this technology is what I see is overlooking, like, say, experienced technical guy looking at conducting interviews. He can have modulations as per the skill, as per the experience, as per the response of the candidates, which is not in the AI based interviewing techniques.’
This funding is aligned with the study of Gentle (2019) in his study he also mention about the disadvantage of AI tools has one of shortcoming that it remove the human factor and judgment completely. He further illustrated by giving an example that if there will be candidates that may not fully meet the criteria, yet they may be the best fit based on characteristics such as charisma, charm, motivation, and integrity which can only be identified by human judgment. Even though the question was asked to all nine participants of the study, only a two of the participants elaborated on the idea in an explanatory manner. The first participant depicted that in their company, Pymetrics is used for AI based analysis which is not very conclusive. Therefore, the organisation only considers it as a data point. However, whether to hire employees or not is decided by recruiters which defines that human element is mostly prioritised for making final decision. On the other hand, the sixth participants depicted that even though the technology can provide effective level of strategic improvement in the existing aspect of growth; it overlooks skill modulations which may create effective level of strategic challenges. Moreover, in the current finding also concluded that same aspects in the above illustration from the viewpoints of participants where they also highlighted about the importance of human element that is very important for the selection of right candidate in terms of attitude, character and integrity.
In order to understand this research question better, findings are divided into two parts based on the role of participants within the organisation. First challenge is related to the impact of cost in adopting AI tools to understand this aspect, researcher has taken view point of participants who are in decision making role within the organization and HR professional in lead positions. Second challenge is related to the resistance of change here researcher has considered point of view of HR managers, head recruitments.
Economies Of Scale (Costing Element) - Decision Maker Point Of View
Under the third research question, this study tried to understand how much cost can influence the acceptance of AI tools. Based on the findings, companies are investing on AI tools considering the long term benefits. Even though cost of AI tools are slightly high initially but companies consider their operations in multiple location and providing services to multiple clients recognize that these software ultimately leads them to economy of scale for the entire selection cost. IT companies and agencies are willing to invest in AI technologies as these tool are beneficial for the company in a longer run. Below mentioned aspects are the examples from the participants in support of the above findings.
Participant 8: ‘Enterprises are ready to invest provided there is a right solution is available even the solution provides 10 to 12 percentage difference is a lot of value for these enterprises.’
Participant 7: ‘Today a lot of AI tools are quite SaaS model so solution as a service model. So you only pay as you u3se.’
Participant 6: ‘But return on investment is more. Although upfront. If you see it, you will feel that charges are high, but on the longer if you completely calculate and compare, these are more economical’.
Participant 1: ‘For example, Callify right now, we are not using it across multiple geographies. So the number of geographies you're using is quite limited. So because of that, the cost might be slightly up. But when you look at the economies of scale, when you're using some other tool across a larger geography, multiple countries, and multiple candidates. So that way the cost goes on. So I think it depends on the context. The scale at which you are using it is what makes it.’
The article written by Shaun (2021) is in line with the current findings where he stated that in IT companies leaders understand the costs hires and try to reduce these cost by increasing the accuracy in finding the right candidate for the open position where AI tools helps in finding the right fit that save time and money of overall selection process (RICCI, 2021). Another study by Searle also mentioned that with the use of AI based psychometrics assessments and new media in selection, drive the entire process more cost-effective, faster and reduce chance of error (Searle, 2006). Likewise, in the current finding participants mention that adopting AI tools companies are trying to reducing the long term cost of hiring and trying to achieve economy of scale by investing on AI based tools. Against this question, four of the nine participants depicted an exploratory answer. Most of the answers depicted that, even though the initial investment is higher; it can be managed by organisations as the return of investment is higher. Additionally, organisations find concise idea on how AI can be used for selection process through utilisation of these tools which helps to achieve economies of scale. Thus, AI based selection methods can be considered as effective as well as it can achieve economies of scale when combined with the context of human intelligence.
Limited Budget: HR Manager/ Recruiter’s Point Of View-
As per the findings, some of the participants who are in lead position in HR department within the organization but not the sole decision maker, highlighted cost as a disadvantage for the small scale companies who had limited budget and for startups. Below mention are the example of participant 9 and participant 1 in support of findings.
Participant 9: ‘Every small scale company cannot afford that.’
Participant 1: ‘So I think it's very subjective to the company. But if I have to make a plan statement, generally, AI tools are fairly quite expensive, that's a bottom line.’
These findings are in line with the survey conducted in 2019, by the Oracle.com (2021, HR Research Institute), where result figure depicted that out of 484 participants two-thirds (68%) HR professionals considered budget is a major hindrance for accepting and implementing AI based software in the selection and recruitment process (Oracle.com, 2021). Similarly, in the current research it was shared by the participant that the cost of AI tools is the major challenge for many small and midsize companies to implement and use these tools for their benefits.
Resistance To Change
In the current study, researcher came across another finding where few of the participants communicated that the major challenge they are facing is to induce recruiters to use these AI tools. In this finding, researcher came across that some IT companies facing challenges to convey recruiters to adopt new technologies. Some recruiters are convinced strongly by the old tested process of hiring and they comfortable in that process that make them resilient to switch to a new technologies. Even after formal training, managers has to do continues follow-ups sessions and convening is required to instrument these AI tools. In support of the findings mentioned below are the example from the participant 1 and participant 2:
Participant 1: ‘So you need to help make them understand the entire different session for educating them to understand what the tool is about, what are the benefits, why they should start using? Because sometimes what happens is you give a tool to them, but then they might not always use it. They will just keep reverting to the older ways of doing it.’
Participant 2: ‘So sometimes it's a hard sell to some people, but on the whole, when they see what it can do, then they also change their ways.’
The current finding is in line with the recent article (Ideal, 2021) where it is mentioned that HR professionals involved in talent acquisition are sceptical to adopt of AI technologies because they are not convinced about the quality of result while using these software as compare to the work done by any recruiter manually. In the current findings, participant also stated about resistance recruiters demonstrate to adopt AI technology because they are not fully convinced by the result of the software and they go back to their old process of hiring therefore managers conduct more educational sessions to exhibit on the job benefits of AI tools.
Chapter 4: Conclusion And Recommendation
With the advent of information technology, most of the business entities are found to show their concern towards redefining existing operational activities, specifically in the field of recruitment and selection process. From the above discussion, it is identified that AI has positively influenced the practises of the HR department and helps to make optimal decisions in real-time. It is identified that integration of AI in the practises of HR enhances the chance for building an effective workforce by recruiting dynamic, suitable, and talented candidates. Talent acquisition is identified as one of the integral parts in businesses and it might create complexities for the HR managers to select the most suitable candidate through manual sorting process. In this context, the overall discussion has shed light on determining AI impact on the selection process by underlying that AI plays a significant role in entire talent acquisition process. It is seen that integration of AI helps to perform repetitive tasks such as scheduling interviews, selecting the most suitable candidate, resume screening, and others.
Different types of AI recruitment platforms such as Fetcher, XOR, Hiretual, Eight fold, My Interview, Pymetrics, Textio and others are found to be utilised by HR managers. Each of the mentioned software platforms serve as beneficial in terms of selecting the most desirable and appropriate candidate for a specific vacant position. In this context, it can be mentioned that AI is considered as one of the most compelling and promising technologies that continuously transform and impact upon every sphere in the business world. It is identified that application of AI based platforms in the selection process is found to be associated with automated data that minimise the time for responding to the specific candidate. The key reason behind this statement is that the discussion has shown that due to the resistance of automated resume screening, it makes it easier for determining suitable candidates for the required job position.
Considering the evidence mentioned in the earlier chapters, it could be clearly stated that advent of technology and emergences of AI add value and reshape the entire selection process. There are different types of challenges such as inadequate talent sourcing, lack of suitable candidates, training the passive candidates and others associated with the selection process. In this context, the advent of AI resolves the identified issues and provides opportunities for the recruitment team to be more goal-oriented and productive. From the discussion, it is identified that the business world has been changing at a rapid pace and HR departments are found to struggle hard in order to conform to the new business reality. In this regard, the entire illustration and interpretation of the present research is found to be emphasised upon determining the significant impact of AI in the selection process. It is seen that application of AI has strengthened the approach to recruitment and enhanced the chance for attracting talent in a more proficient and effective manner. It is seen that recruiters can effectively visualise and identify suitable talent through application of AI in their existing HR practises. AI is found to design intelligently to resolve biases that are commonly identified during the selection process.
From evidence identified in the earlier chapters, it is seen that in most of the time, recruiters are required to spend a large part of time evaluating and reading each of the applied resumes. Selecting through a manual process is found to be difficult and might require high cost. Advancement of technology like AI has entirely changed the manual selection process strategically and enhanced efficiency of the overall selection process. Thus, interpreting the present research helps to undertake the application of AI along with its efficacy in the field of selection process. It can also be mentioned that application of AI optimises a wide range of activities involved within the selection process. It is identified that with the growing market competition, it becomes difficult to identify suitable candidates for the required position. In order to minimise these challenges, demand for AI integration in the selection process is found to be enhanced over the past few years. Along with this, it is observed that business entities across the world are found to become more dependent on utilisation of technology-based platforms to execute required activities efficiently and conveniently. Overall discussion of the present research has provided in-depth analysis and understanding on the way usage of AI impacts upon the overall selection process in a systematic and logical manner.
Linking With Objectives
Objective 1: To Evaluate The Existing Usage Of AI In The Selection Process
Objective 1 of the present research is found to be emphasised upon determining the usage of AI in the selection process in the present time. In the background section, in-depth analysis has made to meet the first objective of the present research. Additionally, a brief idea on the way usage of AI has entirely changed and strengthened the selection process has been highlighted. Suitable examples of existing AI usage in the form of software platforms such as digitalise interviews, screening software, and others has been underlined by providing their effectiveness in the selection process. On the other hand, in the data analysis chapter, thematic analysis has been performed to meet the mentioned objective critically. Comparison between different existing usages of AI in the selection process has been highlighted along with the way AI application reshaping in the HR practises in the present business environment. Thus, it can be mentioned that the first objective of the mentioned research has been met in data analysis chapter.
Objective 2: To Analyse The Impact Of AI Usage In The Selection Process
Second objective of the present research is found to be emphasised upon analysing the significant impact of AI in the context of selection process. In order to meet this objective, the background segment has provided a brief illustration. Starting from determining the way AI has been utilised in the selection process to underline the contribution of AI in optimising HR practises has been interpreted critically. From the data analysis chapter, it is identified that application of AI positively affects upon the selection process by generating positive outcomes in the context of cost and time saving, bias removal, accuracy, efficiency maximisation and others. On the other hand, in the data analysis chapter, with the help of previously published information on the AI usage, the impact of the mentioned technology has been identified clearly. Along with this, it has also been identified that with the rapid growth of AI usage, functions of HR such as the selection process are found to be influenced significantly. The primary reason behind consideration of AI in the selection process has also been identified in this chapter critically. Thus, it can be mentioned that second objectives of the present research have been met in data analysis.
Objective 3: To Recommend Further Improvement Parameters For Organisations Planning To Incorporate AI For Operational Progress
AI is considered as one of the most recent and growing platforms that plays a significant role in the context of talent acquisition process. Most of the business entities in this present dynamic business environment are found to show their concern towards implementing AI in its operational activities. However, organisations that are planning to implement AI for improving operational processes are required to consider some specific implementation aspects to achieve success. Thus, the third objective of the present research is going to be met in the next adjoining paragraph.
Based on the overall discussion, the following recommendation could be considered by the organisations that are willing to implement AI to improvise their operational process in the field of talent acquisition and selection process.
Recommendation 1: Understand AI Usage And Business Capabilities
Understanding the concept and effectiveness of AI can be considered as one of the integral parts before implementing the AI implementation plan. Organisations those are willing to plan for incorporating AI needs to understand the AI feature along with business capabilities. Most of the time, it is identified that businesses fail to determine effectiveness of AI implementation in their business operations by determining their existing business capabilities. Thus, it can be recommended that businesses that are willing to implement AI need to understand business needs for AI implementation in accordance to which proactive measures could be undertaken.
Recommendation 2: Identification Of Issues That Businesses Willing To Resolve Through AI
Before implementing the AI, it will be essential for businesses to determine the field or existing operational issues that they are willing to improve through implementing AI. For example, in the context of present research it is identified that most of the business entities are found to show their concern towards implementing AI to improve the selection process. It is identified that it becomes difficult to select and recruit appropriate and preferred candidates with the help of manual sorting or selection process. In order to resolve these issues, AI is being utilised that helps in optimising the selection process and enhancing the chance for obtaining the required candidate. In this context, organisations that are willing to incorporate need to determine their existing HR challenges that they need to solve in accordance to which AI requires to implement.
Recommendation 3: Identification Of Right Platform For Selection Process
Selecting the right candidate is important for any business organisation to generate operational efficiency and business success. For instance, specific AI platforms utilised by particular companies in the selection process might not serve as beneficial for other businesses. Depending on the business capabilities, workplace environment, expertise, and needs, AI platforms are found to be utilised. In this context, it can be recommended that organisations need to select suitable and appropriate AI platforms to improve the selection process according to the business needs and flourish future business growth by building a strong workforce.
Recommendation 4: Align AI With The Core Competency Of Business
Business entities are found to leverage their strengths as well as resources in terms of optimising business performance. In order to incorporate AI in business operations, it is essential to determine whether the implementing AI will be advantageous for the company or not. In this context, it can be recommended that implementation of an AI plan needs to be aligned with the organisation’s core competency. This will serve as beneficial to accomplish business goals by strengthening operational activity in a more efficient manner after implementing AI.
Recommendation 5: Redesign HR In To The “HAIR”
In order to implement AI in the HR practises, it would be important to transform the existing HR into HAIR (Human AI Resource). The key reason behind this recommendation is that HR department will be required to understand the forms and operational activities performed by AI applications. This would also serve as beneficial for the HR managers to understand about the way they will require to optimise the selection process based on which proactive measures could be undertaken. Thus, it can be recommended that organisations that are willing to incorporate AI for operational progress need to redesign their exiting HR practises so that business success through selecting the right candidate can be achieved after implementing AI.
Future Scope Of The Study
While carrying out any research related activities, it is important for the researchers to analyse the future scope to determine the research areas that can be explored in the future. In the context of present research, overall data interpretation and illustration is based upon secondary theme-based analysis. Consideration of secondary analysis helps to collect all the research relevant information efficiently in a cost effective manner. However, omitting the primary research minimises the chance for obtaining accurate results regarding the AI usage in real-world environments accurately. In this regard, in the future by utilising information of the present research, it could be extended to obtain accurate real-world information about the AI usage efficiently through primary analysis. On the other hand, it is identified that usage and contribution of AI in the selection process is found to be different for different business entities depending upon the business needs. Thus, in the future, considering primary research, comparison between different business entities that are currently utilising AI in the selection process can be made. This would enhance the chance for generating fruitful results based on the actual real-world business evidence regarding AI usage and its impact.
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