Open access peer-reviewed chapter

AI-Powered Workforce Management and Its Future in India

By Mrinmoy Roy

Submitted: November 13th 2020Reviewed: April 21st 2021Published: May 18th 2021

DOI: 10.5772/intechopen.97817

Downloaded: 102

Abstract

Day in and day out, the Workforce Department faces new problems and operational demands. It is very important for the department to respond quickly and understand the best possible action to be taken in each single case. It is unknown in a compromised setting of near-constant shifts in forecast and scheduling, increased customer demands, and changing recruitment and retention of employees. Workforce management around the world has begun to use artificial intelligence (AI)-based workforce management (WFM) software to solve the above problems and reach goals. These tools transform workforce management by helping to anticipate and plan short- and long-term planning. These tools improve Workforce Management by helping to predict short- and long-term scheduling and recruiting requirements, communicate with staff, and at the right time bring customers in contact with the right agent. This chapter addresses AI workforce management intervention and WFM instruments with industry-specific case studies and its experience with the product Workforce Dimensions. Present status and future expectations are also critically reviewed. Techniques of AI and machine learning (ML) are transforming industries, as are goods from thermostats to cars. The global enterprise value generated from AI continues to grow, according to Gartner, and is projected to reach up to $ 3.9 trillion by 2022. But what do these approaches mean for workforce management in the field? The current chapter examines the growing use of artificial intelligence (AI) in various HRM functions, as well as the ongoing debate about the expected decline in the usability of human resources in organizations. In the presence of AI in the workplace, HR practitioners are constantly afraid of being replaced by computers/robots/smart business machines. The study aims to recognize AI’s important contribution to enhancing organizational decision-making processes, as well as to enhance awareness of AI’s acceptability and inclusion in the HRM department. Despite the fact that the combination of AI and HRM is attracting a large number of researchers, many aspects of the field remain unexplored. The current research proposes a collaborative approach by stressing the complementary role of HRM in the successful use of AI, and it contributes to the existing literature. Since AI and HR are so intertwined, organizations should concentrate on incorporating AI as a supporting tool for HR rather than attempting to take over HR’s function. Business systems and smart business machines should be designed in such a way that they cannot produce results without the help of HR.

Keywords

  • AI Powered Workforce Management
  • WFM
  • Artificial Intelligence

1. Introduction

Artificial intelligence (AI), also known as machine intelligence in computer science, is intelligence exhibited by computers as opposed to natural intelligence shown by humans and other species. AI research is characterized in computer science as the study of “intelligent agents,” or devices that perceive their environment and take actions to increase their chances of achieving their objectives. “A system’s ability to accurately interpret external data, learn from those data, and use those learnings to accomplish clear goals and tasks through flexible adaptation,” say Kaplan and Haenlein. When a computer mimics “cognitive” tasks that humans associate with other human brains, such as “learning” and “problem solving,” the word “artificial intelligence” is used [1].

The reach of AI is debatable: as machines become more efficient, tasks previously considered to require “intelligence” are increasingly excluded from the concept, a phenomenon known as the AI effect, leading to Tesler’s Theorem’s quip, “AI is whatever hasn’t been done yet.” Optical character recognition, for example, is often left out of discussions of “artificial intelligence,” despite the fact that it has become a commonplace technology. Successfully understanding human voice, playing at the highest level in strategic game systems (such as chess and Go), autonomously driving vehicles, and intelligent routing in content distribution networks and military simulations are all examples of digital computer capabilities that are commonly known as AI.

Artificial intelligence is categorized into three forms by Kaplan and Haenlein: analytical, human-inspired, and humanized artificial intelligence. Analytical AI only has features that are associated with cognitive intelligence, such as the ability to produce cognitive representations of the environment and the ability to learn from previous experiences to guide future decisions. Human-inspired AI combines cognitive and emotional intelligence, recognizing, in addition to cognitive components, human emotions and taking them into account while making decisions. Humanized AI demonstrates all forms of competencies (cognitive, emotional, and social intelligence), as well as the ability to be self-aware and self-conscious in interactions with others.

Although AI and automation may be replacing workers, it’s easy to overlook the fact that these same innovations play a critical role in recruiting, hiring, and retaining employees.” “Finding the right talent is more difficult than ever in this period of constant change and digital skills shortages. Businesses can quickly and efficiently find a wide range of top candidates using AI and automation, and at a speed that keeps up with the frantic pace of modern business.”

Screening chatbots and automated social media scraping tools are two examples of simple artificial intelligence applications that can assist recruiters with the sourcing and screening processes. These tools are intended to provide just a sliver of information about an applicant’s chances of succeeding at the business. According to the Society for Human Resource Management, Mya, an AI hiring assistant created by FirstJob, communicates with applicants to verify they meet job criteria, answer questions, and keep them updated on their application’s status. This bot provides help 24 hours a day, seven days a week via chat, text message, Skype, or e-mail, and will contact a person if it is unable to complete a mission. Another form of artificial intelligence recruitment tool is social media scraping software. These bots will gather a lot of information from an applicant’s social media accounts and use it to predict those activities, such as potential interaction levels.

Tests, gamification, and simulations that gather data directly from the candidate are examples of intermediate artificial intelligence systems used in the hiring process. Unilever is one organization that has introduced a competitive artificial intelligence screening method developed in partnership with Pymetrics. Applicants would spend approximately twenty minutes playing neuroscience-based Pymetrics games as part of the hiring plan. These scientific games covertly test a candidate’s memory, risk tolerance, ability to read contextual cues, and ability to concentrate. These intermediate AI applications typically offer hiring managers a clear indication of whether an applicant is a good match for the role, but they do not rely on precise job metrics.

Advanced artificial intelligence solutions, on the other hand, employ custom algorithms designed specifically to relate specific job performance metrics to potential candidates who best exhibit these characteristics. HireVue is a firm dedicated to the development of innovative AI human resource solutions. Their most effective program currently entails a video interview with questions “specifically crafted to evoke answers indicative of work satisfaction and find the right behaviors.” Every applicant’s responses, body language, speech, emotional state, and keywords are analyzed by the HireVue artificial intelligence program. Affectiva has also developed emotion detection software that can be used to assess a candidate’s authenticity and emotional intelligence. These AI software systems are so advanced that they can “detect the flash of disdain that passes over an applicant’s face as he mentions his ex-boss,” according to WirePiazza [2].

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2. Human resource functions and artificial intelligence

2.1 Personalized employee experiences

IBM officials addressed how AI can be successfully woven into an employee’s onboarding program in their report. New workers who want to meet people and learn more about the company can be unsure where to go. They might inquire of their desk neighbor. But what if she’s allocated to a different division? “What if Joe had been greeted with new recruit details customized to his first task on his mobile device?” In a paper on transforming HR with AI, IBM officials wrote. IBM is developing a system that can respond to a new employee’s most urgent or job-critical concerns in order to help them get up to speed quickly. For example, an AI might recommend training or provide the names, places, and contact details for people he should meet on his first day or so. AI engines can also remind the same employee that a new recruit website provides a wealth of useful information.

2.2 Decision-making with cognitive support

IBM officials, who are promoting their own AI capabilities through IBM Watson, also demonstrated how cognitive engines could assist employees in making key day-to-day decisions in the workplace. Normally, HR team members will be in charge of the following tasks:

  • Holiday requests - Workers who request vacation days are told that they are unlikely to be accepted because many people have already scheduled time off during that period.

  • Assessing your mood - A worker answers a phone call from a customer. Following the call, the employee is told that he is stressed and that he should take a break before his meeting.

  • Team planning - When a company wishes to take a more comprehensive approach to employee training, team members are offered a list of training choices to choose from.

  • Recruiting practices - A hiring manager is told that the company’s recruitment policy is unsuccessful because it interviews far too few applicants. Cognitive solutions can help businesses tap into various data sources and uncover new insights, among other things, to help them create applicant profiles. Automatically and leverages historical data patterns, seasonality and cyclicality to make forecasting more accurate.

2.3 Automation of ‘repetitive, low-value add tasks’

AI gives HR the chance to automate “repetitive, low-value add tasks” and concentrate on more strategic work. HR teams will concentrate on “value-add work including mentoring and continuous feedback” by saving time on the steps of onboarding a new employee (allocating space, provisioning a laptop, etc.).

HR teams will be “free to do some of the innovative and strategic work that has a bigger effect on the performance of their companies” as AI tools automate basic HR activities like benefits management and triaging common questions and requests.

Companies have been gathering data on their consumers for years in order to obtain information and predict future actions. HR departments have a long way to go in terms of using these people analytics. AI would be able to play a bigger role in HR if it can find out what data to monitor, evaluate, handle, and secure. “In the never-ending battle for talent, businesses will seek out novel approaches to attracting top talent. Companies can be differentiated from one another by innovations that boost the candidate experience and fulfil the candidate’s digital expectations.”

2.4 Removing biases

According to the Human Resources Professional Association’s study, even when managers try to be inclusive, they can subconsciously favor applicants that are similar to them, a phenomenon known as “unconscious bias.” Another bias, language bias, has been discovered by the Implicit Association Test (IAT), a psychological instrument that shows that people’s subconscious word associations suggest bias. “These prejudices are ingrained in job descriptions and resume selections. “Now, thanks to AI, algorithms can be programmed to assist employers in recognizing and eliminating prejudice trends in the language they use to enhance their recruiting communications and accept diverse applicants,” according to HRPA researchers. AI may also present managers with applicants who were previously filtered out due to the human propensity to prefer candidates with similar characteristics or skills. HRPA researchers’ algorithms are free of these tendencies, enabling managers to focus on data-driven evaluations rather than gut feelings.

2.5 Detecting workers on their way out

Veriato’s AI systems are built to detect employees who are on their way out. It keeps track of employee computer activity — emails, keystrokes, web surfing, and so on — for a month and then uses an AI system to analyze the data to create a baseline of normal activity patterns in the company. “It flags outliers and reports them to the employer based on that knowledge,” HRPA researchers wrote, “and also identifies shifts in the overall tone of employees’ communications to predict when employees may be thinking about leaving.”

2.6 Litigation strategy

Employment-related cases are typically fact-based, so collecting documentation and other facts is crucial. However, according to Littler’s study, only 5% of respondents use advanced analytics to direct their litigation strategy. According to Crews, employers can be unaware of the advantages of using analytics in this scenario. “It’s groundbreaking to be able to use data early in a case to eke out insights before taking a deposition or testing the credibility of witnesses.” Having the opportunity to locate key records allows an employer to see what employees were doing at a given time and can aid in the construction of a tale, he explained.

Consider the case of a refrigerator repair technician who travels to people’s homes and files a wage and hour class action alleging that he or she was not paid for all hours worked. The employees’ version of their days will be exposed in the lawsuit and subsequent depositions, but analytics may be used to validate or refute their narrative. Crews said, “The more knowledge you have, the better the decision-making process you will participate in.” GPS data from work vehicles, routing orders, messages about the technicians’ assignments, invoices, and cellphone and login information could all be collected by the employer. These numbers can help to build an image.

The data could show that the technicians did not work off the clock and were properly paid for all hours worked, in which case the employer has some good proof on its hand. However, if the data shows that the workers’ arguments are legitimate, it is best to check the data and know about it up front rather than going through time-consuming and expensive litigation, according to Crews.

2.7 Pay equity

Pay equity can also be assessed using data analytics. At the state and local levels, legislation in this region is increasingly evolving. At least 12 states, for example, have enacted legislation banning employers from questioning work applicants about their past pay. The aim of such legislation is to bring an end to long-standing wage inequalities based on gender, race, and ethnicity.

Crews recommended that employers keep an eye on employee salaries for disparities based on protected groups, adding that certain jurisdictions with pay equity regulations offer a safe haven for employers who perform investigations and strive to close gaps.

He went on to say that there are resources available that make it simple to create a user-friendly experience as well as evaluate, understand, and communicate data. “You don’t have to rely on a math-filled Excel spreadsheet any longer.” Having a clear view of what is going on in the company is beneficial, according to Crews. “HR practitioners will have the dialogue with the compensation committee, executives, and other decision-makers when advanced technology is combined with strong storytelling and visualization.”

2.8 Chatbots

Using apps like chatbots, workers can get crucial policy and procedure information from anywhere and at any time. Chatbots are text-based chatbots that can answer basic employee questions. According to a 2017 ServiceNow survey of 350 HR leaders, two-thirds of respondents believe workers are more comfortable using chatbots than other means of communication for transactional inquiries about paid-time-off policies, open enrollment, and leaves of absence. ServiceNow, based in Santa Clara, California, is a cloud computing company. Employers who use chatbots must ensure that they are in compliance with federal and state data protection, disabilities, and other jobs laws.

2.9 Legal pitfalls

When using AI to direct human resource planning, HR practitioners must keep an eye out for prejudice in the systems. They must be on the lookout for differential effects, which occurs when an apparently equal or neutral norm is unequal in reality. A recruitment tool, for example, can weed out applicants who live more than 10 miles from the job site. What if the surrounding communities are mostly made up of wealthy white families? This hiring criterion will have a racially and ethnically unequal effect [3].

2.10 Recruiting

We make a lot of choices based on intuition. According to one report, most hiring managers make a decision about an applicant within the first 60 seconds of meeting them, mostly based on the candidate’s appearance, handshake, wardrobe, or voice. Is it really possible to predict which attributes, experiences, schooling, and personality traits would lead to success in a specific role? We do not have any. Despite spending billions of dollars on assessments, evaluations, simulations, and games to recruit applicants, many managers and HR professionals tell me that they still get 30–40% of their candidates wrong.

AI-based algorithms can sift through resumes, identify successful internal applicants, profile top performers, and even decode video interviews to give us clues on who will succeed. One company uses Pymetrics’ AI-based gamified evaluation to screen applicants for marketing and sales positions, and their performance rate has increased by over 30% thus removing both of the existing process’s “interview bias” and “educational pedigree bias.” The use of artificial intelligence in recruiting would be enormous. Although we are all concerned with work skills (software skills, sales skills, math skills, and so on), most research indicates that technological skills account for only a small portion of a person’s performance. According to a recent report on High-Impact Talent Acquisition, level 4 maturity firms, which have the highest financial return from recruiting, devote nearly 40% of their hiring requirements to emotional and psychological qualities such as ambition, learning agility, enthusiasm, and sense of intent. Will AI be able to find this out as well? It’s possible. LinkedIn, Pymetrics, Entelo, HiredScore, IBM, Textio, Talview, Unitive, PredictiveHire, and others are among the vendors in this market.)

2.11 Employee growth and learning

We have no idea how to perfectly “train” people. Most learning professionals estimate that at least half of the global L&D market is wasted (forgotten, incorrectly implemented, or simply wasting people’s time). However, we have no idea which half this is!

Do you know what you “need to learn” to boost your job performance? What if we had algorithms that tracked and analyzed the talents, attitudes, and actions of the top performers in our teams and then simply instructed us how to be more like them? These “Netflix-like” algorithms are now finding their way into the world of learning platforms, making learning as useful and enjoyable as watching cable television. The market is still young, but the potential is immense. According to study, the average employee has less than 25 minutes a week to train and learn; if that time is made more important, everyone will benefit.

Improve your results through getting trained and automatizing the work process via Degreed, EdCast, Filtered, Volley, Axonify, BetterUp, Clustree, Workday etc.

2.12 Leadership and management

We work like Zen masters in terms of management and leadership. We read books, attend seminars, emulate the bosses we respect, and extol the accomplishments of today’s popular leaders. Do we have a firm grip on the science of leadership? I assume it is a passing fad. We’re focusing on meaning, mission, and discipleship this year. It was “servant leadership” just a few years ago, and it was “execution and financial acumen” when I was younger. According to most reports, there are hundreds of management and leadership characteristics that characterize performance, and each of us brings a slightly different and special combination of them to the table.

This can now be decoded with the aid of artificial intelligence. Three companies have developed “AI-based” coaching tools, which include programs that solicit feedback, read comments, and infer sentiment from employees and teams. They use this data to equate the problems of these individuals and teams to those of higher-performing teams, and to send managers and supervisors “nudges” on how to improve. One client reported that after just three months of using this method, their leadership teams had seen a 25% rise in organizational values simply by introducing tiny behavioral nudges. (Reflektiv, BetterWorks, Ultimate Software, Zugata, Humanyze, ADP, Impraise, and others are among the vendors in this space.)

The possibilities for fraud and enforcement are enormous. Employees who cheat or commit crimes are “contagious,” according to one report (people who work with them pick up bad habits). AI will analyze organizational network data (email traffic, sentiment of comments) to identify areas of tension, potential ethical lapses, and a variety of other enforcement risks, and then alert HR or compliance officers to intervene before bad conduct occurs. TrustSphere, Keencorp, Volley, Cornerstone, and others are among the vendors in this space.)

2.13 Employee engagement and well-being

Artificial intelligence is now being used to classify habits that contribute to poor job efficiency. In the field of protection, AI can detect actions and interactions that contribute to mishaps. A new generation of survey tools may detect tension and bad behavior trends and warn HR or line managers. Limeaid, VirginPulse, Glint, Ultimate Tech, CultureAmp, TinyPulse, Peakon, and others are among the vendors in this space.)

2.14 Self-service for employees and applicant management

A new generation of intelligent chatbots can make communications smart and easy. (IBM, ServiceNow, Xor, Mya, Ideal, Paradox, and others are among the vendors in this space) [4].

3. Discussion

AI is a collection of algorithms and machine learning software that can quickly ingest data, recognize patterns, optimize, and forecast trends. The systems can recognize photos, understand expression, and use pattern matching to detect signals such as mood, sincerity, and even personality. These algorithms aren’t as “simple” as humans, but they are fast, allowing them to analyze millions of pieces of data in seconds and quickly match them to patterns.

By plotting curves of potential outcomes and then optimizing decisions based on a variety of parameters, AI systems can statistically “predict” and “learn.” As an example, consider an AI system that analyses a candidate’s demographics, work experience, and interview questions and then “predicts” how well they will do on the job. (This is something that HiredScore, Pymetrics, HireVue, IBM, and others are working on.)

Many of these applications are brand new, and as exciting as they can appear, they come with a slew of risks. The most important is that AI cannot function without “Training info.” To put it another way, the algorithms learn from their mistakes. If your current management methods are biased, racist, punitive, or unnecessarily bureaucratic, you may as well institutionalize everything you dislike. We need AI that is transparent and “tuneable” so that we can inspect the algorithms and ensure that they are performing as intended. Our early algorithms would need “bumpers” and “tuning knobs” to learn how to make them more accurate, just as early automobiles did not always drive straight.

Bias can be institutionalized across processes. Assume your organization has never employed a woman engineer and has a small number of African American engineers. Women and black engineers are less likely to succeed in management, according to the AI recruitment system. This form of bias must be carefully eliminated from algorithms, and doing so correctly will take time.

There is a chance of data leakage as well as inadvertent misuse. Consider a popular application of analytics: predicting the probability of a top performer leaving the business. We can inadvertently create the wrong behavior by telling managers that “this person has a high chance of leaving.” For example, the manager may neglect or handle this person differently. We must carefully learn how to apply behavioral economics. Today, AI is a “tool” for advice and change rather than a self-contained decision-making method.

At Entelo, AI experts addressed the value of designing “interpretive” and “transparent” AI systems. To put it another way, if the machine makes a decision, it should clarify why it made it so that we, as people, can determine if the parameters it used are still relevant. This is one of the most critical requirements for new tools, and most AI systems today are completely opaque.

Consider what happens if a self-driving car crashes. We devote a considerable amount of time to evaluating how the accident occurred, what visual or algorithmic processes failed, and what circumstances might have contributed to the accident. What if AI makes a mistake in an applicant recommendation, a wage change, or a management intervention? Are we going to find out? Will we be able to work it out? Would we really be conscious of it before it’s too late? We still have a lot of work to do in terms of instrumenting and learning how to “train” our management-based AI systems to work effectively.

The buzz around AI is at an all-time high right now. Any HR tech vendor claims to have a machine learning team and a best-in-class AI solution. Yes, these skills are critical to the industry, but do not be fooled by the hype.

The accuracy and completeness of an HR tool’s algorithms, the ease of use of its programs, and, most importantly, its ability to offer “narrow AI” – or rather precise solutions that solve your problems – will all play a role in its success. Only when the provider has a large amount of data (to train the system) and a lot of feedback on how well it performs can this be achieved. So, rather than getting great engineers, I believe the barriers to entry would be concentration, business strategy, and client intimacy.

Also, do not buy a black box device unless you can demonstrate its value in your business. Since management and personnel decisions are always informed by a company’s culture, we’ll need to spend some time evaluating and fine-tuning these systems in the real world. For example, IBM has spent years fine-tuning its AI-based compensation and job strategies to suit the company’s culture and business model. They’re now taking these tools to corporate customers, and each one teaches IBM something different about the algorithms, allowing them to improve them for that market, community, or organizational need.

Despite these difficulties and dangers, the potential rewards are immense. Payroll accounts for 40–60% of a company’s income, and much of this massive cost is motivated by management decisions based on gut instinct. I believe we will see drastic changes in efficiency, performance, and employee health as AI systems in HR become smarter, more proven, and more oriented on specific problems. What we need to do now is be patient, alert, and ready to put in the effort.

4. Consequences

The days of spending hundreds of man-hours sifting through thousands of CVs and online job-board profiles in search of new hires are over. For example, an AI recruitment firm claims on their blog that their AI candidate sourcing algorithm can “reduce time to hire from 34 days to 9 days.” Using a non-biased process that eliminates prejudices from sourcing and finds applicants that are professionally suitable for the job, candidate sourcing and onboarding productivity increased by 73.53 percent.

Another role that AI technology can automate is candidate screening. Far more online data, such as information from social media accounts, previous online work records, and educational credentials, can be obtained using this intelligent-style approach, which will boost the ranking system of applicants for recruiters to choose from.

Given that AI technology can easily source and test a large number of applicants, the business’s acceptance criteria and the applicant profile must be balanced in order to find and match the most promising candidates for the job. Using the information collected, AI systems will rate applicants on a scale based on experience, job history, skill sets, and salary requirements in order to find the right applicant. This method of data processing is becoming increasingly useful in today’s market due to its ability to find passive candidates, who are usually the most sought after because they aren’t actively searching for other jobs and are happy in their current role, suggesting that they are an asset to their organization and therefore face less competition to place them.

Interviewing is a big part of the hiring process until you have found the ‘right match’ for the position. Many expats are actively pursuing employment abroad in today’s global work market, but they might not always be able to fly to the country of their choice for an interview. HireVue and Mya, for example, are two startups that specialize in AI interviewing apps. Through using video as a tool, these organizations are turning their emphasis to simplifying the interviewing process. Pre-made questionnaires are used in programs like HireVue, which the applicant can then film themselves responding. This enables the recruiter to perform generic interviews with a greater range of prospective applicants using pre-recorded recordings, which can then be sifted through to find those who will advance in the talent acquisition process.

The footwork is taken care of using AI technology, from sourcing to interviewing, significantly reducing the recruitment timeline. As a result, the talent management team is able to engage with potential applicants and assess their ability to succeed in the particular position, allowing them to make a placement much more quickly.

In today’s economy, countless business dollars are expended on old-fashioned recruiting in addition to numerous man-hours. According to a 2016 report by the Society of Human Resource Management, “the total cost-per-hire is $4,129.” That’s a colossal sum of money to put single person in. Let us place it in context.

Amazon had “541,900 workers in the third quarter of 2017, up from a little more than 300,000 in the same period a year ago,” according to CNN. This suggests that Amazon employed an estimated 241,900 new workers over the course of a year. Granted, Amazon still uses AI technology in its recruiting services, but let us assume they did not (as many large corporations do today) and that their cost of hiring one employee was the same as the average found by SHRM. The cost will be $4,129 (average cost to hire) multiplied by 241,900 (new employees) for a total of $998,805,100 in annual recruiting costs [5].

Amazon may have invested upwards of $1 billion USD on recruiting expenses alone over the course of a year in this case, according to reports. This is incredible and almost incomprehensible, yet it is possible. With many AI recruiting and Human Resources programs available that provide custom packages on a monthly, quarterly, or yearly subscription basis, it’s easy to see how switching to AI technology solutions will save you a lot of money.

Richard Hughes, Senior Vice President of UnitedHealth Group, said at the recent IBM Think 2018 panel, “The future will be centered on the employee experience, with individual involvement being prioritized. We can’t do it on any scale without using the best of what ‘data science’ has to offer.” AI is not just about assisting humans with lower-level tasks; it’s also about data. AI will collect massive quantities of data at a pace that is exponentially faster than humans could ever hope to achieve. This knowledge is what drives companies in general, not just HR teams.

Many workers of larger companies will feel disengaged and unappreciated by their bosses and HR departments. For most workers, anything from not being accepted for holiday requests to unenforced policies may be the turning point, resulting in high turnover rates. A high salary does not guarantee that an employee would be happy. Most people consider their work to be their lives, and who wants to live a life that is unhappy? Nobody.

Having access to a larger set of data ensures you can improve your workers’ overall experience by analyzing the data and making appropriate changes to the working environment or internal processes. Hughes wrote in a recent tweet, “Having good data is comparable to being in good health. You take it for granted once you have it. You panic if you don’t.” Simply put, more data implies more data analytics, which means more advanced insights that lead to better working conditions for workers and lower turnover.

5. Digitizing HR functions

5.1 Recruiting and talent management

Ksenia Zheltoukhova, head of research at the Chartered Institute of Personnel and Development (CIPD), claimed in an article by Hagginbottom [6], which included excerpts from interviews with corporate experts from various organizations, that organizations are opting for video interviews to give candidates a personalized feel. Despite the fact that such new practices can jeopardize HR’s value, some researchers claim that capabilities such as imagination, empathy, and a humanistic approach to problems have yet to be discovered by automation or AI. Alan Stukalsky, chief digital officer at Randstad US, agrees, believing that the use of chatbots in the recruiting process is still in its early stages. A robot cannot determine a candidate’s cultural and behavioral characteristics. The most specialized role of HR, recruiting, profits the most from AI. Computer-assisted, automated systems handle everything from creating job descriptions to shortlisting the best candidates from a pool of applicants [7].

Onboarding After an employee has been on-boarded, a sequence of routine actions for the new hire begin. For HR department directors, directly handling newly recruited employees would be incredibly time consuming and draining. As a result, businesses employ user-friendly software that contains all of the necessary information for a new recruit. It contains simple answers to common questions posed by new employees. The HR manager will save time by getting involved with new hires later in the onboarding process. It is also assumed that AI’s position in human resources is more of a facilitator, allowing human resources to complete tasks in less time, freeing up time for them to concentrate on studying and improving key positions such as “creative thinking,” “design skills,” and “psychological understanding,” among others, in order to make strategic decisions for the company [8].

5.2 Development and learning

According to Jain [9] using AI-enabled software to design learning and development opportunities for employees would improve employee work satisfaction. Career growth programs that are tailored to the needs of workers result in lower absenteeism and higher productivity. We are already using AI as a learning tool, as Melissa Lamson, President and CEO of Lamson Consulting, points out in a post. Getting feedback from Alexa and Siri is nothing more than a form of learning. Today, we prefer to learn by flows rather than guidance through a digital experience. Focus learning through experience has been facilitated by AI, stressing design and critical thinking. AI has facilitated the production of individual employee charts, which is a new form of learning and development technique.

5.3 Mentoring and coaching

Coaching and mentoring are another important HR feature that is unlikely to be replaced by AI. Contrary to common opinion, apps such as “mobile coach” helps workers understand their needs and develop personalized learning and development programs. As a result, software will determine the employee’s job requirements and guide them toward their preferred career paths. Creation of a party Various types of mini courses provide workers with ready-to-use coaching and mentoring resources. However, certain delicate issues involving employee actions and attitudes also necessitate the intervention of a human being to interpret the situation [10].

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6. Future research prospects

Indeed, AI has provided for very systematic and precise HR business solutions. Employee record preservation, talent management, employee growth, employee appraisals, employee compensation distribution, employee selection, employee engagement, monitoring employee success and input, and other complex tasks are made easier by AI. HR managers must determine the extent to which technology is used in HR. Some researchers claim that AI will never be able to replace HR’s valuable feedback, while others believe that AI will soon supplant HR’s vital position because it has an advantage over HR in terms of error-free and fast responses. At this stage, when AI and HR are combined, it is up to HR managers to determine how much AI should be used in HR functions. A strong distinction between AI-controlled activities and HR-managed assignments must be made, and AI should be supplemented by HR influence in all functions. The current study examined the views of various corporate experts on the subject and concluded that AI plays a role in the efficient implementation of HR functions. Despite its ability to make work easier and more effective in different areas of HR, AI is still unable to substitute human participation in the implementation of HR functions. The HR department is primarily responsible for personal relations and critical behavioral knowledge of human minds. If all duties are delegated to robots, the company can never be able to retain its employees or their loyalty to the company. As a result, human resources must continue to do what they were doing before, but with AI’s help, they can do it better. In numerous articles and research papers, the increasing use of technology in HR has been addressed, either in the context of HR digitization or the role of AI in HR. This paper aimed to cover various aspects of technology used in HR functions, but there is still space for future study. This research lays the groundwork for future research in the fields of AI and HR. Researchers should take advantage of the emerging stage of AI and HR in the future to contribute to the literature in the field by conducting empirical studies. Different perspectives of technology in HR functions, such as digitization of HR functions, the use of Expert Systems, technologically dominated work systems, technologically aided HR systems, the role of AI generated methods in HR, and so on, can be studied separately to understand HR’s position in the evolving scenario. Researchers who are interested in comparing the ease of work before and after AI participation in HR functions should conduct a comparative analysis. In addition, the current study may provide food for thought for some researchers as they consider the different job profiles in the face of AI.

7. Conclusion

As much as AI manages to disrupt the HR technology environment. HR departments must strike a balance between cognitive technology advances and accountability. To avoid unintentionally incorporating prejudice into their services, HR leaders and practitioners must have a good understanding of how decisions are produced. This openness will be critical in ensuring that workers have confidence in the latest technologies.

As you may have guessed, there are many benefits of using AI technology for your human resources and hiring needs. However, nothing in life is perfect, particularly a new type of technology that has yet to achieve its full potential. Many of the services and programs available which lack features that people are used to in the non-AI world, such as the ability to leave feedback on an applicant after a video interview or clear CV search requirements. One big feature of AI technology that it lacks is empathy and human contact, or the ability to handpick an applicant and get to know them personally during the recruiting process, which cannot be done right now if you are relying on AI technology to do it for you. AI sees data, while humans experience emotion, and this will not change in the near future.

As a result, it is up to you to determine if AI technology is the best fit for your company’s needs. Are you a large organization with thousands of workers that spends a lot of money on recruiting each year and is searching for a way to streamline the process and save money? Are you a small company with less than 15 employees that takes pride in getting to know each employee as if they were family? Every organization has different priorities, budgets, and expectations, but the beauty of the AI industry is that there is a solution for everyone, regardless of size.

It’s up to you to find out what works best for your business, whether it’s adjusting to the ever-growing AI market, keeping things the same, or making incremental improvements over time. In any case, AI technology will continue to advance, and at some stage in the future, AI will be the standard, making conventional methods of recruitment and human resource management appear obsolete.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Mrinmoy Roy (May 18th 2021). AI-Powered Workforce Management and Its Future in India, Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications, Eneko Osaba, Esther Villar, Jesús L. Lobo and Ibai Laña, IntechOpen, DOI: 10.5772/intechopen.97817. Available from:

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