AI For Business Intelligence

From recruitment to performance: AI and ML transforming HR operations

The current era, known as the ‘digital age’, has seen digital transformation become a global consensus among businesses. Many experts believe that transformation has been accelerated by as much as half a decade in the wake of the pandemic, which saw digital transformation at a never-before-seen scale.

This transformation is disrupting almost all industries, and human resources (HR) is no exception. In this digitised world, HR must reinvent itself. By stepping up to this challenge, HR can create immense value for both organisations and their employees. However, it is crucial to note that AI and ML are not designed to replace managers but rather to support HR decision-making.

Artificial intelligence (AI) and machine learning (ML) have become prominent buzzwords in recent years, with vast potential applications in HR. AI refers to a computer program’s ability to learn and think, effectively simulating human behaviour and thought processes. ML, a subfield of AI, uses data-driven algorithms to perform complex tasks. These technologies can automate routine tasks, enhance decision-making processes, and enable HR professionals to focus on strategic initiatives.

Re: HR Analytics Dashboard – Microsoft Fabric Community

For example, despite the engaging nature of many jobs, repetitive tasks such as generating reports, verifying information, and analysing data will inevitably be done by AI. Utilising AI programs to perform these repetitive tasks can save time, increase productivity, and facilitate managerial decision-making.

One significant area where AI and ML are transforming HR is recruitment and talent acquisition. These technologies focus on collecting and using data to make informed talent decisions. AI-powered systems can analyse vast amounts of applicant data, screen and evaluate resumes, acknowledge and reject applicants, schedule interviews, check candidates’ backgrounds, and even conduct initial interviews. By identifying patterns and matching candidates with job requirements, AI enhances the efficiency of the hiring process.

Continual learning and development are essential for employees to stay competitive in today’s rapidly changing business landscape. AI and ML can personalise learning experiences by analysing individual skill gaps, learning styles, and career aspirations. Intelligent learning platforms can recommend tailored training programs, provide real-time feedback, and facilitate self-paced learning. These advancements enable HR to foster a culture of continuous learning, promote employee growth, and improve organisational performance.

Traditional performance management systems are often criticised for their lack of effectiveness. AI and ML can transform this process by providing real-time feedback, objective evaluations, and data-driven insights. AI-powered systems can automate performance reviews, allowing HR professionals to focus on more strategic initiatives.

HR Analytics Power bi Solution – EClytics: Business Intelligence

AI’s increasing influence on HR practices is evident, but it is crucial to remember that AI is a tool dependent on human input. The effectiveness of AI systems relies on the data people provide. Additionally, AI reflects the ‘coded gaze,’ which includes the preferences, priorities, and prejudices of those who develop these technologies. Algorithms use past information to predict future outcomes and ML systems imitate human behaviour.

These technologies act as invisible gatekeepers, increasingly influencing hiring decisions, employee performance assessments, and tenure decisions. Consequently, inaccurate data used by these algorithms could significantly impair someone’s career. The future is thus in the hands of software programs whose exact operations are often unknown.

The absence of a human factor in these processes could lead to bias and discrimination. Organisations must ensure that AI algorithms are regularly tested and examined to identify and correct any biases that may develop over time. While the potential of AI and ML in HR is vast, addressing the ethical considerations associated with their implementation is essential.

HR professionals must ensure transparency, fairness, and data privacy. Balancing human touch and technology is crucial to avoid a complete reliance on AI or ML, which could result in a loss of personal connection and empathy.

HR analytics Power BI Dashboard – Data visualization dashboard

In conclusion, the integration of AI and ML in HR is vital for the success of HR professionals. By staying updated on advancements in AI and ML, they can harness these technologies to drive positive change and make a significant impact on organisations. It is also important to consider how HR professionals and employees view and use AI and ML technologies.

Resistance to change and job displacement are common concerns. Organisations should offer opportunities for upskilling and training to improve technological literacy. Nevertheless, the increased use of these systems without understanding their implications could negatively impact our lives.

Ishraat Saira Wahid, PhD is an Assistant Professor of Business Administration & Human Resource Management at the College of Business Administration, Prince Mohammad Bin Fahd University, Kingdom of Saudi Arabia. Email: [email protected]

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.

How Darussalam Assets is tapping AI in HR operations

Darussalam Assets, a holding company managing a diverse portfolio of government-linked companies in Brunei, has been leveraging the power of artificial intelligence (AI) to improve its human resources (HR) operations.

Its journey began in 2016 with the implementation of SAP’s S/4Hana enterprise resource planning (ERP) suite across its portfolio companies, followed by the roll-out of the SAP SuccessFactors HR suite in 2019.

The adoption of SAP SuccessFactors was a crucial step, enabling the company to streamline its learning management, performance management and recruitment management systems.

More recently, Darussalam Assets has started using SuccessFactors’ business AI capabilities to automate and optimise various recruitment processes, resulting in significant time and resource savings.

An AI-powered job description generator, for instance, allows hiring managers to create job postings in minutes. These postings can then be sent to the HR team for approval within a day, a process that previously took days, according to Apurv Sharma, senior manager of group information systems at Darussalam Assets.

The company has also integrated SuccessFactors’ recruitment capabilities with Microsoft Teams, which allows the AI to generate interview questions based on the competencies and requirements outlined in job descriptions.

When a virtual interview is scheduled, the AI-generated interview questions are automatically populated in the Microsoft Teams platform, which the interviewers can then use to guide the conversation. This saves the interviewers time and effort, as they no longer need to manually craft questions or worry about ensuring consistency across interviews.

Darussalam Assets’ AI journey has not been without challenges, however. For one, it has to ensure that the job description generation tool can deliver accurate and reliable results. To this end, the company’s HR team worked closely with their IT counterparts to train the tool, aligning outputs with company standards and requirements.

“The moment we were done training with 10 to 15 job descriptions, from the 16th or 17th description onwards, it was already generating descriptions the way we wanted,” said Sharma. “It takes a bit of learning, but not too much as it learns very fast.”

Looking ahead, Darussalam Assets plans to leverage SAP’s Joule AI assistant to provide accurate and consistent information to employees on group policies, procedures and regulations. This would reduce the need for manual lookups and improve the overall employee experience.

“Rather than use an outside AI model and getting all sorts of information which might be inaccurate, we want to train Joule on the information we have,” said Sharma. “Across the group, this chatbot can be available at any point of time to provide standard answers to questions like, ‘What’s the amount of annual leave available to executives?’”

Additionally, the company is testing the use of AI to analyse candidate profiles and match them to the required skills and competencies, improving the efficiency of the screening process.

Even as Darussalam Assets is making use of AI-infused business applications like SuccessFactors, the company is still building AI models to bring AI capabilities to other systems it uses that are not AI-capable, at a time when the technology is being overhyped.

Sharma noted that while many technology suppliers may pitch a system as AI-capable, “it doesn’t make sense until you really have it in your hands to see what it has learned and get insights out of it – and if it’s not there, then it’s no use”.

From recruitment to performance: AI and ML transforming HR operations

The current era, known as the ‘digital age’, has seen digital transformation become a global consensus among businesses. Many experts believe that transformation has been accelerated by as much as half a decade in the wake of the pandemic, which saw digital transformation at a never-before-seen scale.

This transformation is disrupting almost all industries, and human resources (HR) is no exception. In this digitised world, HR must reinvent itself. By stepping up to this challenge, HR can create immense value for both organisations and their employees. However, it is crucial to note that AI and ML are not designed to replace managers but rather to support HR decision-making.

Artificial intelligence (AI) and machine learning (ML) have become prominent buzzwords in recent years, with vast potential applications in HR. AI refers to a computer program’s ability to learn and think, effectively simulating human behaviour and thought processes. ML, a subfield of AI, uses data-driven algorithms to perform complex tasks. These technologies can automate routine tasks, enhance decision-making processes, and enable HR professionals to focus on strategic initiatives.

For example, despite the engaging nature of many jobs, repetitive tasks such as generating reports, verifying information, and analysing data will inevitably be done by AI. Utilising AI programs to perform these repetitive tasks can save time, increase productivity, and facilitate managerial decision-making.

One significant area where AI and ML are transforming HR is recruitment and talent acquisition. These technologies focus on collecting and using data to make informed talent decisions. AI-powered systems can analyse vast amounts of applicant data, screen and evaluate resumes, acknowledge and reject applicants, schedule interviews, check candidates’ backgrounds, and even conduct initial interviews. By identifying patterns and matching candidates with job requirements, AI enhances the efficiency of the hiring process.

Continual learning and development are essential for employees to stay competitive in today’s rapidly changing business landscape. AI and ML can personalise learning experiences by analysing individual skill gaps, learning styles, and career aspirations. Intelligent learning platforms can recommend tailored training programs, provide real-time feedback, and facilitate self-paced learning. These advancements enable HR to foster a culture of continuous learning, promote employee growth, and improve organisational performance.

Traditional performance management systems are often criticised for their lack of effectiveness. AI and ML can transform this process by providing real-time feedback, objective evaluations, and data-driven insights. AI-powered systems can automate performance reviews, allowing HR professionals to focus on more strategic initiatives.

AI’s increasing influence on HR practices is evident, but it is crucial to remember that AI is a tool dependent on human input. The effectiveness of AI systems relies on the data people provide. Additionally, AI reflects the ‘coded gaze,’ which includes the preferences, priorities, and prejudices of those who develop these technologies. Algorithms use past information to predict future outcomes and ML systems imitate human behaviour.

These technologies act as invisible gatekeepers, increasingly influencing hiring decisions, employee performance assessments, and tenure decisions. Consequently, inaccurate data used by these algorithms could significantly impair someone’s career. The future is thus in the hands of software programs whose exact operations are often unknown.

The absence of a human factor in these processes could lead to bias and discrimination. Organisations must ensure that AI algorithms are regularly tested and examined to identify and correct any biases that may develop over time. While the potential of AI and ML in HR is vast, addressing the ethical considerations associated with their implementation is essential.

HR professionals must ensure transparency, fairness, and data privacy. Balancing human touch and technology is crucial to avoid a complete reliance on AI or ML, which could result in a loss of personal connection and empathy.

In conclusion, the integration of AI and ML in HR is vital for the success of HR professionals. By staying updated on advancements in AI and ML, they can harness these technologies to drive positive change and make a significant impact on organisations. It is also important to consider how HR professionals and employees view and use AI and ML technologies.

Resistance to change and job displacement are common concerns. Organisations should offer opportunities for upskilling and training to improve technological literacy. Nevertheless, the increased use of these systems without understanding their implications could negatively impact our lives.

Ishraat Saira Wahid, PhD is an Assistant Professor of Business Administration & Human Resource Management at the College of Business Administration, Prince Mohammad Bin Fahd University, Kingdom of Saudi Arabia. Email: [email protected]

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.

Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

From recruitment to performance: AI and ML transforming HR operations

The current era, known as the ‘digital age’, has seen digital transformation become a global consensus among businesses. Many experts believe that transformation has been accelerated by as much as half a decade in the wake of the pandemic, which saw digital transformation at a never-before-seen scale.

This transformation is disrupting almost all industries, and human resources (HR) is no exception. In this digitised world, HR must reinvent itself. By stepping up to this challenge, HR can create immense value for both organisations and their employees. However, it is crucial to note that AI and ML are not designed to replace managers but rather to support HR decision-making.

Artificial intelligence (AI) and machine learning (ML) have become prominent buzzwords in recent years, with vast potential applications in HR. AI refers to a computer program’s ability to learn and think, effectively simulating human behaviour and thought processes. ML, a subfield of AI, uses data-driven algorithms to perform complex tasks. These technologies can automate routine tasks, enhance decision-making processes, and enable HR professionals to focus on strategic initiatives.

For example, despite the engaging nature of many jobs, repetitive tasks such as generating reports, verifying information, and analysing data will inevitably be done by AI. Utilising AI programs to perform these repetitive tasks can save time, increase productivity, and facilitate managerial decision-making.

One significant area where AI and ML are transforming HR is recruitment and talent acquisition. These technologies focus on collecting and using data to make informed talent decisions. AI-powered systems can analyse vast amounts of applicant data, screen and evaluate resumes, acknowledge and reject applicants, schedule interviews, check candidates’ backgrounds, and even conduct initial interviews. By identifying patterns and matching candidates with job requirements, AI enhances the efficiency of the hiring process.

Continual learning and development are essential for employees to stay competitive in today’s rapidly changing business landscape. AI and ML can personalise learning experiences by analysing individual skill gaps, learning styles, and career aspirations. Intelligent learning platforms can recommend tailored training programs, provide real-time feedback, and facilitate self-paced learning. These advancements enable HR to foster a culture of continuous learning, promote employee growth, and improve organisational performance.

Traditional performance management systems are often criticised for their lack of effectiveness. AI and ML can transform this process by providing real-time feedback, objective evaluations, and data-driven insights. AI-powered systems can automate performance reviews, allowing HR professionals to focus on more strategic initiatives.

AI’s increasing influence on HR practices is evident, but it is crucial to remember that AI is a tool dependent on human input. The effectiveness of AI systems relies on the data people provide. Additionally, AI reflects the ‘coded gaze,’ which includes the preferences, priorities, and prejudices of those who develop these technologies. Algorithms use past information to predict future outcomes and ML systems imitate human behaviour.

These technologies act as invisible gatekeepers, increasingly influencing hiring decisions, employee performance assessments, and tenure decisions. Consequently, inaccurate data used by these algorithms could significantly impair someone’s career. The future is thus in the hands of software programs whose exact operations are often unknown.

The absence of a human factor in these processes could lead to bias and discrimination. Organisations must ensure that AI algorithms are regularly tested and examined to identify and correct any biases that may develop over time. While the potential of AI and ML in HR is vast, addressing the ethical considerations associated with their implementation is essential.

HR professionals must ensure transparency, fairness, and data privacy. Balancing human touch and technology is crucial to avoid a complete reliance on AI or ML, which could result in a loss of personal connection and empathy.

In conclusion, the integration of AI and ML in HR is vital for the success of HR professionals. By staying updated on advancements in AI and ML, they can harness these technologies to drive positive change and make a significant impact on organisations. It is also important to consider how HR professionals and employees view and use AI and ML technologies.

Resistance to change and job displacement are common concerns. Organisations should offer opportunities for upskilling and training to improve technological literacy. Nevertheless, the increased use of these systems without understanding their implications could negatively impact our lives.

Ishraat Saira Wahid, PhD is an Assistant Professor of Business Administration & Human Resource Management at the College of Business Administration, Prince Mohammad Bin Fahd University, Kingdom of Saudi Arabia. Email: [email protected]

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.

Talent Intelligence: Unlocking The Power Of Multidimensional Data

Joanna Riley, CEO & cofounder of Censia. Striving for a more just and efficient global economy through better talent data and technology.

getty

In the age of data-driven decision-making, the significance of structured, multidimensional data cannot be overstated. This data type forms the backbone of advanced AI systems, facilitating nuanced insights and predictive analytics essential for modern business strategies, including talent management.

Data-Driven Recruiting And Talent Management

Companies that harness data in talent management have seen remarkable improvements in both efficiency and insights into workforce dynamics. For instance, incorporating data-driven strategies in recruiting has been shown to significantly enhance the quality of the workforce, increase productivity, and support unbiased hiring decisions. Workable’s comprehensive guide on data-driven recruiting emphasizes the importance of choosing the right data and metrics, collecting data efficiently, and acting on this data to improve hiring processes.

The Rise Of Multidimensional Data in HR

Multidimensional data isn’t a new concept. Most business intelligence (finance, consumer behavior, etc.) relies on structured data across multiple dimensions to gain profound insight and determine future strategy. HR has always been the big exception because HR has traditionally been one-dimensional (a linear resume of qualifications matched to a linear list of job requirements), often inaccurate or messy (outdated profiles, multiple job titles meaning similar things, etc.), and limited only the candidate and the role—not, for instance, other important factors such as the industry or the organization.

By cleaning and structuring this talent data and introducing new layers of information (company size, revenue levels, company events, industry and more), the insight that AI can derive from this data grows immensely.

The deployment of structured, multidimensional data in talent management allows organizations to gain a deep and holistic understanding of their workforce, encompassing diverse dimensions such as people, jobs, skills, and industries. This comprehensive dataset supports AI-driven solutions, enabling organizations to navigate complexities with precision and agility. MyHRfuture highlights how data-driven HR impacts recruitment and talent management by measuring key talent acquisition data points for greater impact, supporting workforce planning, and facilitating training and development.

Inferring Skills And Bridging Gaps

The capacity of structured data to infer skills is invaluable in talent acquisition and workforce planning, where accurate skill assessment is crucial. Analyzing patterns and contextual information allows AI-powered systems to uncover latent skills, providing a broader understanding of individuals’ capabilities.

Transformative Impact Of AI In Talent Management

The convergence of structured data and AI heralds a new era in talent management, with organizations increasingly turning to AI-driven solutions to streamline processes and decision-making. Korn Ferry’s insights into the telecom sector, for example, reveal the critical role of strategy and talent in organizational success, emphasizing the importance of learning agility as a predictor of long-term leadership potential.

To further underscore the transformative power of data in talent management, it’s essential to recognize the nuanced ways in which organizations leverage this data to foster a culture of continuous learning and adaptability. A data-driven approach streamlines recruitment and talent development and underpins strategic workforce planning and development initiatives. According to research by Deloitte, integrating data analytics into HR practices enables organizations to forecast talent needs, identify skill gaps, and optimize resource allocation more effectively. This strategic alignment ensures that talent management efforts are about filling positions and building a resilient, skilled workforce capable of driving long-term business growth.

Moreover, adopting AI and data analytics in talent management extends beyond operational efficiency to enhancing the employee experience. Organizations can address individual career aspirations and skill development needs by personalizing learning and development opportunities, thereby increasing engagement and retention. This personalized approach, grounded in data, signifies a shift from traditional, one-size-fits-all HR practices to more dynamic, responsive strategies that value and cultivate individual talent.

In this rapidly evolving landscape, harnessing and interpreting multidimensional data becomes a critical competitive advantage, enabling organizations to navigate the complexities of the modern workforce with agility and insight.

Common Pitfalls When Adopting Talent Intelligence

Several common pitfalls occur when organizations adopt this type of technology. The first is that system implementations are prone to failure. According to a Deloitte study, 70% of digital transformation efforts are considered less than successful, and organizations require three years to start competing in digital markets. Working with APIs or native integrations to upgrade current systems is one way to avoid the financial, time and engagement loss caused by this.

The other big pitfall is not understanding the data well enough. It is essential to ask how the data is collected, cleaned and structured. If a provider runs new algorithms on old data or limited source data, you’ll get biased and unreliable results.

And finally, you’ll want to understand the legal restrictions you might face in your field. Several regions have already restricted the use of AI in HR decision-making, so you’ll want to make sure that you deploy it in a way that assists, not replaces, your team and their decisions.

Embracing A Data-Driven Future

As organizations undergo digital transformation, the importance of leveraging structured, multidimensional data and AI will continue to grow. McKinsey’s guide on building a data-driven strategy highlights the need for an integrated approach to data sourcing, model building, and organizational transformation tailored to the company’s desired business impact.

In conclusion, structured, multidimensional data is the cornerstone of AI-driven talent management, enabling organizations to unlock insights, optimize processes, and drive strategic outcomes. By leveraging advanced analytics and AI technologies, organizations can chart a course toward a data-driven future where talent becomes a true differentiator in driving business success.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

From recruitment to performance: AI and ML transforming HR operations

The current era, known as the ‘digital age’, has seen digital transformation become a global consensus among businesses. Many experts believe that transformation has been accelerated by as much as half a decade in the wake of the pandemic, which saw digital transformation at a never-before-seen scale.

This transformation is disrupting almost all industries, and human resources (HR) is no exception. In this digitised world, HR must reinvent itself. By stepping up to this challenge, HR can create immense value for both organisations and their employees. However, it is crucial to note that AI and ML are not designed to replace managers but rather to support HR decision-making.

Artificial intelligence (AI) and machine learning (ML) have become prominent buzzwords in recent years, with vast potential applications in HR. AI refers to a computer program’s ability to learn and think, effectively simulating human behaviour and thought processes. ML, a subfield of AI, uses data-driven algorithms to perform complex tasks. These technologies can automate routine tasks, enhance decision-making processes, and enable HR professionals to focus on strategic initiatives.

For example, despite the engaging nature of many jobs, repetitive tasks such as generating reports, verifying information, and analysing data will inevitably be done by AI. Utilising AI programs to perform these repetitive tasks can save time, increase productivity, and facilitate managerial decision-making.

One significant area where AI and ML are transforming HR is recruitment and talent acquisition. These technologies focus on collecting and using data to make informed talent decisions. AI-powered systems can analyse vast amounts of applicant data, screen and evaluate resumes, acknowledge and reject applicants, schedule interviews, check candidates’ backgrounds, and even conduct initial interviews. By identifying patterns and matching candidates with job requirements, AI enhances the efficiency of the hiring process.

Continual learning and development are essential for employees to stay competitive in today’s rapidly changing business landscape. AI and ML can personalise learning experiences by analysing individual skill gaps, learning styles, and career aspirations. Intelligent learning platforms can recommend tailored training programs, provide real-time feedback, and facilitate self-paced learning. These advancements enable HR to foster a culture of continuous learning, promote employee growth, and improve organisational performance.

Traditional performance management systems are often criticised for their lack of effectiveness. AI and ML can transform this process by providing real-time feedback, objective evaluations, and data-driven insights. AI-powered systems can automate performance reviews, allowing HR professionals to focus on more strategic initiatives.

AI’s increasing influence on HR practices is evident, but it is crucial to remember that AI is a tool dependent on human input. The effectiveness of AI systems relies on the data people provide. Additionally, AI reflects the ‘coded gaze,’ which includes the preferences, priorities, and prejudices of those who develop these technologies. Algorithms use past information to predict future outcomes and ML systems imitate human behaviour.

These technologies act as invisible gatekeepers, increasingly influencing hiring decisions, employee performance assessments, and tenure decisions. Consequently, inaccurate data used by these algorithms could significantly impair someone’s career. The future is thus in the hands of software programs whose exact operations are often unknown.

The absence of a human factor in these processes could lead to bias and discrimination. Organisations must ensure that AI algorithms are regularly tested and examined to identify and correct any biases that may develop over time. While the potential of AI and ML in HR is vast, addressing the ethical considerations associated with their implementation is essential.

HR professionals must ensure transparency, fairness, and data privacy. Balancing human touch and technology is crucial to avoid a complete reliance on AI or ML, which could result in a loss of personal connection and empathy.

In conclusion, the integration of AI and ML in HR is vital for the success of HR professionals. By staying updated on advancements in AI and ML, they can harness these technologies to drive positive change and make a significant impact on organisations. It is also important to consider how HR professionals and employees view and use AI and ML technologies.

Resistance to change and job displacement are common concerns. Organisations should offer opportunities for upskilling and training to improve technological literacy. Nevertheless, the increased use of these systems without understanding their implications could negatively impact our lives.

Ishraat Saira Wahid, PhD is an Assistant Professor of Business Administration & Human Resource Management at the College of Business Administration, Prince Mohammad Bin Fahd University, Kingdom of Saudi Arabia. Email: [email protected]

Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions and views of The Business Standard.

How Darussalam Assets is tapping AI in HR operations

Darussalam Assets, a holding company managing a diverse portfolio of government-linked companies in Brunei, has been leveraging the power of artificial intelligence (AI) to improve its human resources (HR) operations.

Its journey began in 2016 with the implementation of SAP’s S/4Hana enterprise resource planning (ERP) suite across its portfolio companies, followed by the roll-out of the SAP SuccessFactors HR suite in 2019.

The adoption of SAP SuccessFactors was a crucial step, enabling the company to streamline its learning management, performance management and recruitment management systems.

More recently, Darussalam Assets has started using SuccessFactors’ business AI capabilities to automate and optimise various recruitment processes, resulting in significant time and resource savings.

An AI-powered job description generator, for instance, allows hiring managers to create job postings in minutes. These postings can then be sent to the HR team for approval within a day, a process that previously took days, according to Apurv Sharma, senior manager of group information systems at Darussalam Assets.

The company has also integrated SuccessFactors’ recruitment capabilities with Microsoft Teams, which allows the AI to generate interview questions based on the competencies and requirements outlined in job descriptions.

When a virtual interview is scheduled, the AI-generated interview questions are automatically populated in the Microsoft Teams platform, which the interviewers can then use to guide the conversation. This saves the interviewers time and effort, as they no longer need to manually craft questions or worry about ensuring consistency across interviews.

Darussalam Assets’ AI journey has not been without challenges, however. For one, it has to ensure that the job description generation tool can deliver accurate and reliable results. To this end, the company’s HR team worked closely with their IT counterparts to train the tool, aligning outputs with company standards and requirements.

“The moment we were done training with 10 to 15 job descriptions, from the 16th or 17th description onwards, it was already generating descriptions the way we wanted,” said Sharma. “It takes a bit of learning, but not too much as it learns very fast.”

Looking ahead, Darussalam Assets plans to leverage SAP’s Joule AI assistant to provide accurate and consistent information to employees on group policies, procedures and regulations. This would reduce the need for manual lookups and improve the overall employee experience.

“Rather than use an outside AI model and getting all sorts of information which might be inaccurate, we want to train Joule on the information we have,” said Sharma. “Across the group, this chatbot can be available at any point of time to provide standard answers to questions like, ‘What’s the amount of annual leave available to executives?’”

Additionally, the company is testing the use of AI to analyse candidate profiles and match them to the required skills and competencies, improving the efficiency of the screening process.

Even as Darussalam Assets is making use of AI-infused business applications like SuccessFactors, the company is still building AI models to bring AI capabilities to other systems it uses that are not AI-capable, at a time when the technology is being overhyped.

Sharma noted that while many technology suppliers may pitch a system as AI-capable, “it doesn’t make sense until you really have it in your hands to see what it has learned and get insights out of it – and if it’s not there, then it’s no use”.

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