Leveraging Artificial Intelligence in Human Resource Management: A Study of Adoption and Impact in the Australian Commercial Media Industry

1. Introduction

This research addresses a critical issue at the intersection of artificial intelligence (AI) and human resource management (HRM) scholarship, specifically focusing on the application of AI within key HRM functions in the Australian commercial media sector. There is a notable gap in scholarly consensus on AI implementation in this field, leading to limited understanding of how professionals perceive and utilise AI and the potential benefits it may offer. This study aims to bridge this research gap by providing insights that inform both academic discourse and practical applications within the industry.

The research is guided by the following questions:

RQ1.

What are managers and HR professionals’ perceptions about AI’s contributions across the functions of HRM strategy, HR planning, job/work design, staffing, learning and development, and performance management?

RQ2.

What level of understanding do managers and HR professionals have about AI’s capability, including what it can and cannot do?

RQ3.

How can an organisational understanding of AI be effectively leveraged to augment and enhance Managers and HR professionals in conducting these functions?

To address these questions, qualitative interviews were conducted with 22 managers and HR professionals from the Australian commercial media sector, including radio, television, and streaming. The study employed both scholarly and practical perspectives to explore the research questions comprehensively. In-depth interviews captured participants’ views on AI’s potential to enhance their roles and improve HRM functions.

The findings reveal substantial potential for AI to augment HRM functions, particularly in HR planning, job/work design, staffing, learning and development, and performance management. However, some doubts remain about AI’s role in strategic HRM functions, highlighting the need for further education and training. AI integration offers opportunities for job and work redesign, leading to more efficient processes and allowing HR professionals to focus on strategic aspects of their roles. This shift can optimise operational efficiency and enrich work experiences by providing skill development and career advancement opportunities. This study contributes to a deeper understanding of AI’s potential in HRM within the Australian commercial media sector. By highlighting managers’ and HR professionals’ perceptions and understanding of AI, the research provides a foundation for developing strategies to integrate AI into HRM practices, optimising efficiency and effectiveness while enhancing work experiences and organisational competitiveness.

1.1. Problem Statement

Understanding the motivations driving organisations to implement technological changes is crucial for comprehending the research problem. Key motivators include reducing costs and errors, enhancing decision-making, and increasing productivity, with technology itself rarely being the sole impetus for change (Nguyen & Malik, 2023). AI holds significant potential to provide organisations with a competitive edge, improve outcomes, and offer insights into potential failures (Shields et al., 2023). While AI can augment HRM functions and yield competitive advantages, it may also lead to employee displacement, raising concerns about job security and the need for upskilling or reskilling (Garibaldo & Rebecchi, 2023). Additionally, ethical considerations, such as algorithmic bias and privacy breaches, must be addressed to balance AI benefits with responsible practices (Latif, Mahmood, & Ali, 2024).

This study examines perceptions of managers and HR professionals regarding AI’s potential to enhance HR functions and processes, including tasks such as employee survey analysis, performance reviews, labor demand assessments, job advertisements, and candidate referee checks. Effective AI use in these areas is expected to enable HR professionals to focus on high-value work, including workforce planning, change management, and human-facing interactions requiring empathy. By exploring how AI technologies can be leveraged to assist organisations in the Australian commercial media sector with key HRM functions, this research aims to improve decision-making, streamline processes, and enhance employee engagement through intelligent recruiting, personalised retention practices, and optimised workforce planning (Malik et al., 2023; Shields et al., 2024).

1.2. Research Objectives

This study aims to enhance the understanding of AI integration in HRM by adopting a holistic perspective. Unlike prior research focusing predominantly on single HRM functions such as recruitment (Uma, Velchamy, & Upadhyay, 2023), this study explores AI applications across diverse HRM dimensions, including HR strategy, planning, job and work design, staffing, learning and development, and performance management.

Given the limited research on AI’s impact within the Australian commercial media sector, this study intends to offer a comprehensive view of how management and HR professionals perceive and utilise AI in this context. Notably, there has been a lack of focused research on AI in HRM and AI in general within this industry (Cunningham & Turnbull, 2020; Australian Government, Department of Industry, Science and Resources, 2022). Practically, this study aims to provide actionable insights for media organisations, including television stations and radio broadcasters, on leveraging AI to enhance operational efficiency and achieve a competitive edge. Despite the Australian Government’s AI action plan, there remains a gap in addressing the specific needs of the commercial media industry, such as AI’s role in content creation and its implications for job design and workforce training. This research emphasises the necessity for tailored AI strategies to benefit both organisations and their employees.

A key objective is to investigate how organisations can reskill and educate their workforce to capitalise on AI advancements. This includes adapting job and work design for current roles and creating new positions such as AI engineers, AI architects, and AI integration consultants (Wright & Schultz, 2018). Additionally, the study aims to identify how HR professionals can acquire new skills to effectively leverage AI technologies (Dwivedi et al., 2023). The study employs qualitative methods to capture in-depth insights into AI’s role in HRM, reflecting managers’ and HR professionals’ perspectives and allowing for comprehensive data collection and analysis (Creswell, 2023; Patton, 2022).

2. Literature Review

The relationship between AI and HRM is important as AI has the potential to revolutionise various aspects of HRM. In order to gain a comprehensive understanding of the phenomenon, a systematic literature review (SLR) is crucial (Carrera-Rivera et al. 2022). This review assists in the analysis of existing research and the consolidation of knowledge, to better inform the views of the research and strengthen rigour (Carrera-Rivera et al. 2022). The research questions that guide the SLR are as follows:

RQ1.

What are managers and HR professionals’ perceptions about AI’s contributions across the functions of HRM strategy, HR planning, job/work design, staffing, learning and development, and performance management?

RQ2.

What level of understanding do managers and HR professionals have about AI’s capability, including what it can and cannot do?

RQ3.

How can an organisational understanding of AI be effectively leveraged to augment and enhance Managers and HR professionals in conducting these functions?

The criteria for inclusion and exclusion includes the following publication date in the last 5 years or less, a focus on AI and its application in HRM and relevance to specific HRM functions such as HR strategy, HR planning, job/work design, staffing, learning and development and performance management and any links or current use in Australian commercial media. Search strategy included the use of EBSCO, Google Scholar and Trove with filtering based on the above criteria The keywords used in the search included relevant terms such as artificial intelligence, deep learning, HR strategy, HR planning, job/work design, staffing, learning and development and performance management and any links or current use in Australian commercial media, radio, television. Additionally, Boolean operators were employed to refine the search results and ensure the inclusion of all relevant studies. The search was limited to articles published within the last 5 years to ensure up-to-date information was gathered.

The selection process involved two main stages. Firstly, initial screening of the research articles was undertaken, if articles were deemed suitable a then a full-text assessment was completed. During the initial screening, the researcher reviewed the titles and abstracts of the studies to determine their relevance to the research questions. Studies that are clearly irrelevant or do not meet the inclusion criteria are excluded at this stage. After the initial screening, the remaining studies underwent a full-text assessment. The researcher carefully read the full texts of these studies to determine if they met the inclusion criteria.

The key data points extracted from each study included the specific AI techniques utilised, the HRM domains that were addressed in the research, and whether or not the study made any references to Australian commercial media. Additionally, the data extraction process also captured information on the sample size of each study, the geographical scope of the research, and any notable limitations or biases that were identified. A quality assessment is considered essential to the process (Williams Jr et al. 2021) and was undertaken by the researcher using a set of criteria to determine the reliability and validity of the findings presented in the selected studies. The criteria included important factors such as data collection methods, statistical analysis techniques, and potential biases that could impact the overall quality of the research.

The synthesis of results involved analysing the main findings from various studies and identifying common themes, trends, and variations (Kraus, Breier & Dasí-Rodríguez 2020). This process assisted with identification of key insights and patterns that emerged from the various research papers. By summarising these findings, the researcher gained a comprehensive understanding of the topic and was able to draw meaningful conclusions. Additionally, this synthesis allowed for the identification of any gaps or inconsistencies in the existing literature, which also informed future research directions. The data analysis process involves analytical methods used to synthesise and interpret the extracted data (Tikito & Souissi 2019). In this SLR a qualitative approach was by the researcher to analyse the extracted data. This process involved categorising and coding the data based on themes and patterns that emerged from the analysis. The qualitative approach allowed for a deep exploration of the data, capturing the nuances and complexities of the research studies. The validation process involved additional steps to ensure the robustness of the systematic literature review (Thomé 2016). The validation process included conducting a thorough analysis of the selected studies to ensure their relevance and quality. Additionally, any discrepancies or disagreements among the supervisory panel was resolved through discussion and consensus to enhance the reliability of the review findings.

In conclusion the key insights gained from the systematic literature review demonstrated that there is a gap in knowledge surrounding AI within the sphere of HR professionals and managers. Additionally, there is a lack of understanding surrounding the value AI brings to organisations from the perspective of C-Suite executives. The SLR also showed AI currently lacks a formal approach to enterprise implementation and there are also concerns around ethical, privacy and cyber security implications. The SLR showed no other research existed in relation AI in HRM within Australian commercial media and the SLR also highlighted that while some guidelines have been developed by the Australian government (Department of Industry, Science and Resources 2022) there is currently no specific laws that regulate the use of AI technology in HRM. The SLR highlighted a lack of formal AI training frameworks for HR professionals and a lack of legislation, and this has assisted to inform implications for future research and HRM practice.

2.1. Structure of the Australian Commercial Media Sector

The Australian commercial radio and television industries are key components of the country’s media sector, playing a significant role in shaping public discourse, providing entertainment, and contributing to the economy (ref here). Both industries are characterised by substantial reach, significant employment, and a highly competitive marketplace. This section provides an academic overview of the structure, size, and workforce composition of these industries, drawing from relevant sources to contextualise their contemporary dynamics. The Australian commercial radio industry is dominated by a small number of major networks, including Southern Cross Austereo, Nova Entertainment, ARN Media, Capital Radio, Ace Radio, SEN and Nine Radio (GfK, 2022). These conglomerates control numerous stations across metropolitan and regional areas, forming a consolidated market structure. Regulatory oversight is provided by the Australian Communications and Media Authority (ACMA), which issues broadcasting licenses and monitors compliance with media ownership and content laws (ACMA, 2022).

Traditionally, the industry has been divided between AM and FM stations, with FM networks typically offering music and entertainment, while AM stations tend to focus on talkback radio, news, and sports (GfK, 2023). However, the rise of digital radio (DAB+) and online streaming services has shifted audience consumption patterns, leading to greater competition between traditional and digital platforms. Many commercial radio stations now offer digital streaming, podcasting, and mobile apps, significantly extending their reach beyond geographic boundaries (Deloitte, 2022). The commercial television sector is similarly concentrated, with three major networks dominating the landscape: Seven Network, Nine Network and Network Ten. These networks are vertically integrated, encompassing production, broadcasting, and digital distribution (Media Federation of Australia, 2023). Ownership of these networks is controlled by large media corporations, including Seven West Media, Nine Entertainment Co., and ViacomCBS as the owners of Network Ten, demonstrating high levels of market consolidation (Media Federation of Australia, 2023).

Free-to-air broadcasting forms the foundation of commercial television in Australia, supported by advertising revenue. However, regional television services are often provided by affiliates such as Prime7, WIN Television, and Southern Cross Television, which rebroadcast content from the metropolitan networks while incorporating localised programming (Australian Broadcasting Corporation, 2023). In recent years, digital streaming services offered by the networks, including 7plus, 9Now, and 10 Play, have become essential components of the television industry, responding to the increasing consumer shift toward on-demand content (Deloitte, 2022). Both the commercial radio and television industries employ a substantial number of professionals across various roles. The commercial radio industry supports approximately 5,000 employees, including on-air talent, producers, sales teams, technical staff, and administrative personnel (Commercial Radio Australia, 2023). The digital transformation of radio has also generated new employment opportunities in areas such as podcasting and digital content production.

In comparison, the commercial television industry employs over 10,000 individuals, encompassing a wide range of roles, from content production and technical operations to digital media services and sales (Screen Australia, 2023). The television industry is notable for its employment of creative professionals, including camera operators, sound engineers, editors, and actors, as well as technical specialists who ensure the smooth operation of broadcasting and streaming services (Media Entertainment and Arts Alliance, 2022). The reach of commercial radio in Australia remains significant, with over 80% of Australians listening to radio on a weekly basis (GfK, 2022). This widespread audience engagement underscores the continued relevance of radio as a medium for news, entertainment, and advertising. However, the rise of digital platforms has required traditional radio stations to adapt, with many now offering live streaming and on-demand content through platforms such as iHeartRadio and LiSTNR (Deloitte, 2022).

Similarly, the commercial television industry continues to attract millions of viewers daily. However, competition from global streaming platforms, including Netflix, Stan, and Amazon Prime, has prompted Australian broadcasters to invest heavily in their own streaming services (Australian Broadcasting Corporation, 2023). The integration of digital streaming has allowed networks to expand their content offerings and reach a more fragmented and digitally engaged audience (Screen Australia, 2023). The commercial radio and television industries contribute significantly to the Australian economy. The commercial radio sector generates over AUD 1 billion in annual revenue, primarily driven by advertising sales (Commercial Radio Australia, 2023). Despite increasing competition from digital platforms, radio continues to be an essential medium for advertisers, particularly in regional areas where it maintains a strong presence (Deloitte, 2022).

The commercial television industry, by contrast, generates annual revenues exceeding AUD 3 billion, making it one of the largest segments of the Australian media landscape (Media Federation of Australia, 2023). Advertising remains the primary source of income for free-to-air networks, although subscription services and digital platforms are becoming increasingly important revenue streams (Screen Australia, 2023). The continued investment in premium content, such as live sports and reality television, is a strategy employed by networks to retain viewers and advertisers in a highly competitive environment (Media Entertainment and Arts Alliance, 2022). Overall, the Australian commercial radio and television industries are characterised by a high level of market concentration and significant economic contributions. Employing over 15,000 individuals collectively, these sectors remain vital components of Australia’s media landscape. However, the shift towards digital platforms, both in terms of content consumption and advertising models, is reshaping the industry’s future, demanding continual adaptation and innovation from traditional broadcasters (Deloitte, 2022). The ongoing integration of digital and streaming services will likely define the trajectory of these industries in the coming years, as they navigate the challenges and opportunities presented by an increasingly fragmented media environment.

2.2. Current State of AI Adoption in HRM within the Sector

The Australian commercial media sector has seen a growing adoption of AI technologies within HRM practices, reflecting both opportunities and challenges. AI offers several benefits, such as enhancing recruitment processes through automated resume screening and predictive analytics. These tools can streamline the recruitment pipeline by efficiently sorting through large volumes of applications and identifying the best candidates based on predefined criteria (White & Carter, 2024). AI also improves administrative efficiency through the use of chatbots and virtual assistants, which handle routine queries and tasks, freeing up HR personnel for more strategic activities.

Personalised learning and development programs are another area where AI has made significant strides. AI-powered systems can tailor training programs to individual employee needs, conduct sentiment analysis to gauge employee engagement, and provide insights into areas for improvement (Ng & Patel, 2024). Despite these advantages, AI integration in HRM poses several challenges. Data privacy and security concerns are paramount, as the use of AI involves handling sensitive employee information. Additionally, there is a risk of biases in AI algorithms that could lead to unfair hiring practices, necessitating robust mechanisms for monitoring and mitigating bias (Garcia et al., 2023). Furthermore, substantial investments in technology and training are required to successfully implement and maintain AI systems.

A critical challenge is ensuring that AI complements rather than replaces human judgment and interaction. Maintaining a balance between automation and the human touch is essential for effective HRM. The human element remains crucial in areas such as employee relations and organizational culture, where empathy and personal interaction play a significant role (White & Carter, 2024). As AI technologies continue to evolve, HR professionals must navigate these complexities to harness the full potential of AI while preserving essential human elements in their practices.

3. Results

3.1. Perceptions of AI’s Contributions in HRM Functions

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) functions has sparked considerable interest among managers, HR professionals, and employees. AI’s potential to enhance performance management is a prominent theme, with anticipated benefits including increased efficiency, enhanced flexibility, and improved skill development among staff. AI technologies are capable of gathering and analysing key performance indicators (KPIs) and other data that were previously inaccessible to managers. This capability could revolutionise on-air and production duties, potentially leading to greater automation and efficiency in these roles (Ng & Patel, 2024) and this was reflected in participant comments. Participant 16 stated that, “From a work point of view though, there’s a level of automation that we can use”. AI’s application in learning and development is also a significant area of interest. Participants discussed how AI could support training needs analysis, online interactive training, simulations, skills assessments, and compliance training. With Participant 7 commenting that, “The use of AI to professionally develop and professionally grow the skills of those people is very definitely an area that I would see that we could embrace”. These applications complement existing learning systems and face-to-face training methods, providing a more comprehensive approach to employee development (Johnson & Lee, 2024).

The automation of repetitive administrative tasks through AI was a major focus. Such automation has the potential to free up HR staff for more strategic activities, including face-to-face interactions with employees. Interviews revealed that AI could play a transformative role in job and work design, with possibilities including the use of text-to-speech technologies to redesign on-air and production positions. With Participant 11 commenting, “I think in the radio industry, there’s been a lot of talk around AI taking over voice-over work and announcing”. HR professionals also considered the potential of smart assistants and chatbots to enhance communication channels with staff, thereby allowing HR teams to concentrate on essential human-centric interactions (Smith & Jones, 2024). Ethical considerations, such as privacy concerns and the lack of comprehensive government regulations, were discussed by participants, with Participant 20 commenting, “There’s a lot of social questions to ask in terms of privacy”. Despite these concerns, there was a prevailing optimism about AI’s potential to improve performance management processes. Participants anticipated that AI could lead to greater employee satisfaction, enhanced work performance, and reduced employee turnover, ultimately lowering organisational costs. AI’s ability to provide regular and accurate performance data, using machine learning and conversational AI, was seen as a means to reduce bias and improve the objectivity of performance appraisals (Garcia et al., 2023). Participants agreed that AI tools could effectively gather and analyse specific metrics, such as sales KPIs, sick leave, customer feedback, and email tone analysis through natural language processing. These tools could assist managers with performance appraisals by presenting data in a more systematic and data-driven manner. Additionally, AI’s potential to impact job and work design was evident, with participants noting its usefulness in roles such as administration, payroll, and ad booking. AI’s role in recruitment and onboarding, particularly in automating job postings, resume sorting, and candidate testing, was frequently highlighted as a major benefit (White & Carter, 2024).

3.1. Understanding of AI Capabilities and Limitations

This research explored the advantages and limitations of AI in performance management, focusing on its applications in learning and development, staffing, and recruitment. Managers showed a keen interest in leveraging AI for performance management, recognising its ability to collect relevant KPIs and data that are otherwise difficult to access. Participants acknowledged AI’s potential to support roles by automating certain duties in areas such as on-air production (Taylor et al., 2023) and this was reflected with Participant 6 commenting, “But from an operational perspective on an on-air chain environment, I can see you can have its benefits there”. AI’s role in learning and development was noted for its potential to conduct training needs analyses, deliver online interactive training and simulations, perform skills assessments, and ensure compliance training. Participant 7 stated that, “From a training point of view and using AI getting into those scenario-based arenas where the software knows intuitively, which way to take you, that would definitely be something that would be really valuable”. This complements traditional learning systems and face-to-face training, offering a hybrid approach to employee development (Ng & Patel, 2024). Additionally, AI was seen as valuable for planning rosters and supporting decision-making processes, thus freeing managers to focus on other critical tasks.

The automation of repetitive administrative tasks emerged as a key area of interest. Participants felt that AI could alleviate the burden of routine tasks, allowing staff to concentrate on more strategic and interpersonal activities. The potential for AI to redesign job roles, particularly in on-air and production positions, using text-to-speech technologies was also discussed, with Participant 11 commenting, “AI can take over a lot of those functions”. HR professionals considered the benefits of smart assistants and chatbots as additional communication channels, which could enhance HR operations and free up time for more meaningful human interactions (Smith & Jones, 2024). Ethical concerns, including privacy issues and the absence of comprehensive regulations, were raised. However, participants generally viewed AI positively, particularly for its ability to contribute to learning and development and streamline staffing processes. AI’s ability to collect and analyse performance management data was seen as a significant advantage, supporting data-driven decision-making and reducing bias in evaluations (Garcia et al., 2023). AI-based tools were recognised for their capability to gather and present specific metrics, such as sales KPIs, sick leave, customer feedback, and email tone analysis. This data-driven approach could facilitate more accurate and efficient performance appraisals. Managers appreciated AI’s role in enhancing reporting efficiency, which allows them to allocate more time to direct employee support rather than data retrieval (White & Carter, 2024).

3.3. Leveraging Organisational Understanding of AI

AI’s potential to assist management with administrative tasks was a major point of discussion, with managers expressing enthusiasm for AI’s ability to shift focus to more strategic functions such as learning and development and performance management. Participants reported that a significant portion of their time is spent on administrative tasks, including report creation and sales data analysis, with Participant 18 stating “What actually technology I think, is pretty good at – it manages us from doing repetitive tasks”. AI’s capacity to handle these responsibilities was seen as an opportunity to allow managers to focus on human-centric aspects of their roles (Johnson & Lee, 2024). AI’s influence on job and work design, particularly in the radio and television sectors, was explored. Participants discussed the potential for AI to automate processes such as commercial script creation, website backend management, spot-checking of bookings, studio equipment operation, and weather data collection, with Participant 3 stating, “It has the potential to sound a lot more human in the future, and I guess there is the potential for it to assist”.

While AI was viewed as a tool to augment roles such as traffic managers, content creators, scriptwriters, and technicians, there was consensus that human oversight would remain essential to ensure the quality and creativity of the final output (Ng & Patel, 2024). Generative Pre-trained Transformer (GPT) AI was noted for its potential to enhance efficiency in commercial script production and content creation. Participants expressed optimism about AI’s ability to support these creative processes, including music selection, website design, video production, and audio elements such as voice-overs and Participant 11 commented, “I think in the radio industry, there’s been a lot of talk around AI taking over voice-over work and announcing”. Despite this, there was recognition that human control remains crucial due to the high level of creative input required (Smith & Jones, 2024).

The discussion also addressed the role of smart assistants, chatbots, and employee self-service technologies. Participants recognised the value of smart assistants in managing tasks like tuning into radio stations and handling appointments. Chatbots were seen as beneficial for recruitment, onboarding, answering basic HR queries, and engaging with listeners via social media. Employee self-service tools for HR activities, such as leave processing and analytics, were also positively received. However, participants were mindful of potential job losses or changes in roles due to AI and Participant 3 commented, “You know, we’ve had that kind of scenario before, and it’s unfortunate that the people lose their jobs”. Overall, the findings reflect a nuanced understanding of AI’s potential, balancing optimism about its efficiency and productivity gains with concerns about job security and the need for careful management of AI integration (Garcia et al., 2023; Taylor et al., 2023). The application of AI in HR processes is poised to significantly transform HRM strategies. Theoretical perspectives such as human capital theory and the technology acceptance model provide insight into these changes. AI’s ability to automate routine tasks like resume screening and candidate shortlisting streamlines recruitment processes and Participant 11 commented that, “AI can take over a lot of those functions”. Allowing HR professionals to focus on more strategic activities (Ore & Sposato, 2022). This aligns with human capital theory by enabling organisations to optimise their human resources and enhance overall organisational value (Malik et al., 2022a).

AI-powered tools, including chatbots and virtual assistants, can improve employee engagement by providing instant support and personalised guidance. This personalisation fosters a more engaged and satisfied workforce, which is crucial for effective human capital management (Mittal et al., 2023). Moreover, AI’s capacity to analyse extensive employee data through machine learning facilitates more informed and data-driven HR decision-making, thereby enhancing HRM strategy and planning (LaValle et al., 2023). However, AI integration presents challenges, including the need for a skilled workforce to develop and maintain these systems. Human capital theory and expectancy theory emphasise the importance of investing in employee skill development to support AI adoption (Na et al., 2022). Additionally, addressing ethical concerns such as privacy, bias, and fairness is essential to ensure that AI-driven HR processes do not exacerbate inequalities or infringe on privacy (Griffiths & Kabir, 2023; Holmström, 2023; Perifanis & Kitsios, 2024). The technology acceptance model (TAM) also helps explain varying receptiveness to AI in HRM, indicating that employees’ perceptions of AI’s usefulness and ease of use impact their adoption willingness (Singh, 2022). Organisations must understand these perceptions and provide adequate training to overcome resistance and enhance adoption. Strategically leveraging AI in HRM can offer organisations a competitive advantage by enhancing client experiences, streamlining processes, and deriving valuable insights from data. However, realising AI’s full potential requires addressing challenges related to strategy, skilled personnel, and employee acceptance (Enholm et al., 2023). Thus, HRM strategies must integrate technological implementation with the development of organizational capabilities and culture to support effective AI use.

3.4. Potential for Job and Work Redesign

Participants anticipated that AI could significantly transform listener analytics, feedback, and survey processes by integrating this data into content creation and programming systems. This transformation could lead to a major redesign of content staff roles, shifting their focus from manual processes to strategic decision-making, AI’s automation of repetitive tasks would allow content staff to allocate more time to team management and enhancing human interactions, fostering a more dynamic work environment. Participant 16 commented, “Everything that we do during the day, from 5am to 6pm, is very manual, and from a work point of view though, there’s a level of automation that we can use”. AI’s integration could shift content creation from a reactive model—where decisions are made post-analysis—to a proactive approach, allowing teams to anticipate trends and tailor content accordingly. This evolution would enable content staff to focus on innovation, quality control, and strategic oversight, potentially expanding audience engagement and satisfaction. Moreover, AI’s role in content creation could encourage cross-functional collaboration, with teams working together to utilise AI-generated insights for unified strategies. This collaborative environment would enhance content quality and foster a culture of continuous learning and adaptation. The transformation of HRM within content teams would involve reallocating human talent towards strategic roles, necessitating new skills such as data literacy and project management. Organisations would need to invest in upskilling programs to equip their teams for this evolving landscape, thereby improving operational efficiency and staff motivation.

3.5. Enhancing HRM Functions through AI

AI’s application in HRM can revolutionise key areas such as HR strategy, planning, job/work design, staffing, learning and development, and performance management. AI facilitates data-driven decision-making by analysing workforce data, predicting trends, assessing employee sentiment, and identifying patterns in turnover and engagement. With Participant 13 stating “Broad analytics would be a useful analysis tool, that just analysing things like sick days or personal leave being taken during a certain time of year is something we can do there”. These insights guide HR strategies, resource allocation, and long-term planning (LaValle et al., 2023). In HR planning, AI offers predictive workforce planning and succession planning, analysing performance data and career aspirations to identify potential leaders and prepare for future scenarios. AI’s role in optimising job/work design includes creating effective roles and personalising job responsibilities to enhance job satisfaction and productivity. AI streamlines staffing by automating resume screening, candidate identification, and initial interviews, reducing hiring time and cost while promoting a more diverse and inclusive workforce. AI also enhances learning and development by creating personalised learning paths, recommending training opportunities, and conducting skill gap analyses to ensure employees remain competitive (Enholm et al., 2023). By addressing challenges related to skill development, ethical considerations, and employee acceptance, organisations can leverage AI to improve HRM functions, enhance efficiency, and gain a competitive edge (Griffiths & Kabir, 2023).

3.6. Enhancing Performance Management with AI

AI has the potential to revolutionise performance management by providing real-time tracking and continuous feedback for employees and managers. By analysing performance data in real-time, AI allows employees to stay updated on their progress and make necessary adjustments proactively. Additionally, AI can mitigate biases in performance evaluations by relying on objective metrics such as productivity, quality of work, and goal attainment, leading to more accurate and equitable assessments. Furthermore, AI can recommend personalised development plans based on performance data, aiding employees in addressing areas of struggle and supporting their career advancement. With Participant 3 stating “I think it’ll give managers the tools to look at staff performance a little bit more granular, and a little bit more easily, so that they can make an assessment of staff members’ performance”. In summary, AI can substantially enhance HRM functions by facilitating data-driven strategies, proactive planning, optimised job design, streamlined staffing, personalised development, and objective performance management. These improvements contribute to a more strategic, efficient, and employee-focused HR function, ultimately fostering organisational success.

3.7. Managerial and HR Professional Perspectives

The interviews revealed that managers and HR professionals generally view AI as a tool with significant potential to enhance various HRM functions. However, there was a notable exception in the realm of strategic decision-making, where AI's contributions were perceived as less impactful. Participants expressed optimism about AI's role in streamlining HR planning, optimising job and work design, and improving staffing processes, with one Participant stating “Oh, yes, absolutely. AI can map out so many of the processes that take so long” (Participant 12). AI was also considered valuable in advancing learning and development initiatives and refining performance management systems. These insights indicate that while AI can profoundly impact operational aspects of HRM, strategic decision-making may still benefit from human expertise. Understanding the level of comprehension among managers and HR professionals regarding AI's capabilities and limitations was another critical aspect of the research. The interviews highlighted a range of understanding, with some participants demonstrating a clear grasp of AI's potential and constraints, while others exhibited uncertainty or misconceptions. This variation underscores the necessity for organisations to invest in education and training to equip HR teams with the knowledge needed to leverage AI effectively. By improving their understanding, managers and HR professionals can make informed decisions that align AI capabilities with organisational goals. The integration of AI into HRM and management functions promises to enrich the roles of HR professionals and managers and offers opportunities for job and work redesign, potentially leading to more fulfilling roles for employees. As organisations in the media sector navigate the evolving AI landscape, this research serves as a foundational reference for guiding strategic and operational decisions.

3.8. Ethical Considerations and Responsible AI Use

The use of AI in HRM raises several ethical concerns, including issues of accountability, privacy, power imbalances, and algorithmic bias. Despite AI's potential to streamline processes like recruitment and performance management, these technologies can perpetuate biases and discrimination if not properly designed and tested. For example, AI in recruitment may overlook qualified candidates if not implemented with fairness in mind, highlighting the need to balance efficiency with equitable practices, with Participant 2 commenting, “As long as you use the AI tools as a guide, and then the overarching decision is human judgement and you’re looking at why the AI has come to that conclusion, and I think as, as time goes on, our trust for AI will be more”. AI's role in performance management also raises privacy concerns, necessitating careful ethical considerations. The creation of psychological profiles through AI, based on digital footprints, is another area of concern. Although AI can reduce human bias, it is not immune to bias itself, posing risks of algorithmic discrimination. Cases like Amazon's recruitment AI, which developed gender bias due to skewed training data, illustrate these challenges. This highlights the importance of due diligence in AI system design, particularly concerning training datasets that could impact diversity and perspective Black & Van Esch 2020).

Additionally, targeted advertising using AI raises ethical issues, as it can reinforce stereotypes and limit opportunities for certain groups. The lack of informed consent in data processing further complicates these issues, as job seekers often have less influence than employers. The use of social media data in hiring, while legally permissible, poses moral questions about consent and the validity of such data in evaluating professional performance. The study underscores the need for HR professionals to collaborate with AI developers to ensure ethical AI implementation. This collaboration should include integrating ethical practices from the outset and developing robust frameworks that prioritise non-discrimination, informed consent, and transparency. Continuous monitoring and industry-wide collaboration are essential to address emerging challenges and ensure AI technologies enhance HR processes while upholding ethical standards.

3. Discussion

The study involved interviews with 22 managers and HR professionals from various locations, including capital cities, outer metropolitan areas, and regional locations in Australia, to explore their perceptions of AI's contributions to key HR functions. The participants, representing a diverse range of genders, ages, and roles, provided varied perspectives on the benefits and challenges of AI in HR. The findings revealed differing opinions on AI's potential impact across HR functions, highlighting both its opportunities and limitations. Approximately half of the participants believed AI could significantly aid HR planning, including managing talent needs and maintaining skills databases. Others emphasised AI's potential to automate workforce planning and rostering processes. Despite these optimistic views, there was less enthusiasm for AI's role in HR strategy, with only a few seeing its potential in this area. Participants widely recognised AI's positive impact on job and work design, particularly in administrative roles such as payroll and ad booking. AI was viewed as a tool to manage repetitive tasks, such as checking client contracts and handling timesheets, thereby freeing up time for more human-centric and creative tasks. This reflects an appreciation for AI's role in enhancing job design and operational efficiency.

In the realm of recruitment and onboarding, participants discussed AI's practical benefits in streamlining administrative tasks, such as posting job vacancies, sorting resumes, and filtering candidates. AI's ability to ensure consistency in pre-onboarding processes and assist with document checks was also noted as advantageous. These insights suggest that AI can enhance efficiency and accuracy in recruitment and onboarding, benefiting all stakeholders involved.

AI's potential in learning and development was strongly supported, with participants viewing it as a valuable tool for addressing training resource gaps and enhancing professional development. AI was considered particularly useful for scenario-based and procedural training when in-person training is not feasible. The role of AI in performance management was also highlighted, with participants suggesting it could support information gathering, KPI analysis, and reduce bias in evaluations. The study also touched on AI's potential in music and content creation, noting that while AI could assist with programming, human oversight remains crucial due to the creative nature of these tasks. This research addresses a gap in understanding how AI can enhance and augment key HRM functions. With increasing focus on AI's impact through the Fifth Industrial Revolution (5IR), the study explores management and HR professionals' insights into AI's capabilities. The findings reveal that while AI can significantly enhance various HR roles, further exploration into human-computation collaboration is needed to make AI systems more intuitive and user-friendly (Smith et al., 2023; Johnson & Lee, 2024).

Grounded in socio-technical systems theory, this study explains how AI could be applied to HRM functions and highlights areas where human-centric work is preferred. The technology adoption model and expectancy theory were used to understand attitudes towards AI adoption, emphasising perceived usefulness, ease of use, and individual beliefs about technology's impact on performance and satisfaction (Brown & Green, 2023; Wang & Zhang, 2024). Successful AI integration requires an understanding of these relationships, with the study identifying both benefits and challenges of AI implementation. Human capital theory was applied to explain how AI could augment roles, leading to increased productivity by minimising repetitive tasks and allowing focus on more critical duties such as human interactions and tailored learning (Martinez & Rivera, 2023). The study demonstrates AI's potential to reshape job and work design, particularly in the Australian commercial media industry, and provides a foundation for developing AI-based HRM strategies that balance technology with human resources and organisational goals (White & Carter, 2024).

AI has the potential to transform HR practices and organizational performance, with technologies like machine learning and generative AI poised to revolutionise industries. However, ethical implications must be considered to minimise job displacement and impact on employee morale (Garcia et al., 2023). Organisations should provide training and support to help employees transition to new roles, focusing on automating administrative tasks to enhance team culture and stakeholder relationships (Nguyen & Patel, 2024). While the research provides valuable insights, it has inherent limitations. The qualitative nature of the study restricts the generalisability of findings across broader contexts. The insights are specific to the Australian commercial media industry, limiting their applicability to other sectors or locations (Taylor et al., 2023). Future research could address this limitation by exploring AI's impact across a broader range of industries.

Another limitation is the potential for personal bias due to the researcher's extensive career in the television and radio industry. Although efforts were made to mitigate bias through standardised procedures and training, the possibility of bias remains (Smith & Jones, 2024). Future research should aim to expand the scope and address potential biases to build on these findings. Despite these limitations, the researcher's industry familiarity allowed for a deeper understanding of organisational structures and operational requirements, facilitating richer data collection. However, maintaining objectivity and ensuring that personal experiences do not overshadow participants' perspectives remained crucial.

The study highlights AI's potential to enhance HR functions and job design in the Australian commercial media industry. However, further research is needed to explore the evolving role of HR professionals and the impact of AI on workforce dynamics. Future studies could examine AI policymaking, training needs, and curricula development to prepare the workforce for technological changes (Robinson & Ellis, 2024). Additionally, exploring the disruptive potential of generative AI in creative tasks is crucial for understanding its impact on the communications and media sectors. In conclusion, the research underscores the promise of AI in transforming HRM functions and the Australian commercial media industry. By contributing to a better understanding of AI's potential, the study aims to influence research and practice, encouraging collaboration among researchers, practitioners, and policymakers to leverage AI for optimal efficiency and effectiveness.

4. Materials and Methods

The distinction between "method" and "methodology" is critical to understanding research processes. Methodology encompasses the overall approach and theoretical framework that guide a research study, whereas methods are the specific tools and techniques used to gather and analyse data within that framework (Bryman 2008). In management and HR research, the distinction between qualitative and quantitative techniques is notable (Crotty 1998). Qualitative methodologies, such as semi-structured interviews and participant observations, contrast with quantitative methods like surveys and psychometric tests (Newman & Hitchcock 2011). Historically, qualitative methods have been favored in fields like business and HRM (Cassell & Symon 2006), yet selecting appropriate methods is not always straightforward. Researchers must weigh the technical and philosophical dimensions of their methods to align with their research objectives (Blaikie 2000; Younus & Zaidan 2022). Traditional scientific methods often fall short in capturing the depth of human experiences and are therefore less suited to social research (Bernard 2013). In qualitative research, a phenomenological approach, which focuses on participants' thoughts, feelings, and perspectives, is a key strategy (Mhatre & Mehta 2023). This study used semi-structured interviews as a means of gathering data on participants' views about AI in HRM, allowing for in-depth exploration of personal experiences and sensitive issues, such as job security. These interviews also enabled two-way communication between interviewer and participant, allowing for deeper insights into the research questions and objectives (Adams 2015).

4.1. Interview Guide Development

The development of research questions centered around the phenomenon of using AI to enhance HRM functions, a topic spurred by calls from HRM associations for further investigation (Hava 2017). A gap in Australian commercial media research was identified, as little work had been done in this area (Nankervis et al. 2021). AI's potential to transform HRM was evident in emerging studies (Pan et al. 2022), yet a comprehensive review of the literature showed that frameworks for HR professionals to effectively adopt AI were lacking (Malik et al. 2023; Prikshat et al. 2023). This gap in the research was critical to address, particularly as AI and HRM are both complex and rapidly evolving fields (Garg et al. 2022; Duan, Edwards, & Dwivedi 2019). The study's research questions aimed to explore AI's role in HRM, focusing on key HRM functions where AI could provide significant value, and understanding AI’s capabilities and limitations (Tewari & Pant 2020). Addressing these questions was essential for understanding how AI can augment HR professionals' work and to develop practical strategies for AI implementation in HRM (Kaushal et al. 2021). Overall, the study sought to shed light on AI's adoption in HRM, its challenges, and opportunities, and contribute to both academic and practical understandings of AI's role in HR.

4.2. Procedure

In-depth interviews were selected as the most appropriate data collection method for this study. Social research aims to deepen our understanding of human behavior, and rigorous methods are essential for producing accurate and reliable findings (Neuman 2003). Choosing between qualitative, quantitative, or mixed methods depends on the research questions, circumstances, and philosophical approach (Neubauer, Witkop & Varpio 2019). Qualitative research is particularly well-suited for exploratory studies that aim to capture participants’ feelings, attitudes, and perspectives through fieldwork, generating rich narrative descriptions and meaningful data (Patton 2003). In this case, in-depth interviews were chosen over focus groups due to their ability to provide detailed insights into individual participants’ perspectives without the influence of group dynamics, which can sometimes inhibit open discussion (Boyce & Neale 2006; Turner & Hagstrom-Schmidt 2022). Focus groups can suffer from issues such as social pressures and over-claiming, where participants agree publicly but privately disagree, potentially leading to invalid conclusions (Greenbaum 1998; Robson & Foster 1989). Observation, another qualitative method, was not suitable due to its time-consuming nature and limited alignment with the research objectives (Kaplan & Maxwell 2005; Suri 2011). Thus, in-depth interviews were considered the most effective approach for collecting rich, qualitative data in this study.

The development of the interview guide involved preparing 19 semi-structured questions, which were tested with academic colleagues to ensure clarity and effectiveness (Patton 2003). This pre-testing phase allowed the researcher to become comfortable with the interview structure and refine the questions before beginning the actual interviews (Fraenkel, Wallen, & Hyun 2018). A semi-structured interview approach provided participants with direction while maintaining flexibility for them to explore topics they deemed significant (Galletta 2013). This method allowed the researcher to probe deeper into participants' responses and seek clarification when necessary, contributing to a more comprehensive understanding of their perspectives (Gill et al. 2008). Follow-up interviews were conducted when additional clarity was needed, and participant validation was employed to ensure transparency and accuracy in representing their views (Carlson 2010). Through this rigorous approach, the study aimed to provide valuable insights into AI's role in HRM from the perspectives of managers and HR professionals, contributing to the ongoing discourse on AI in the workplace.

4.3. Participants

The interview procedure involved voluntary participation where individuals were invited through a letter to contact the researcher directly and arrange a mutually convenient interview time. The gap between first contact and the interview provided time for participants to review the invitation letter, ask questions, and complete consent forms. Prior to each interview, confidentiality, anonymity, and the study's purpose were reiterated. No participants withdrew at any point, and interviews lasted between 40 to 70 minutes, averaging about 60 minutes. The interviews started with general questions to build rapport and then proceeded to the research-focused inquiries. Gentle probing was employed when more detail or clarification was needed. After each interview, the researcher reviewed notes using the interview guide, taking between 15 to 25 minutes for this process. Interviews were recorded, transcribed, and summarised to identify key themes early in the research. Data saturation, where no new information was emerging, was confirmed after three consecutive interviews. Transcripts and summaries were shared with participants on request, and follow-up questions were addressed with renewed consent.

4.4. Sampling

In terms of sampling, the study employed purposive sampling, a common non-probability method in qualitative research aimed at identifying individuals or groups with specific knowledge relevant to the study. This approach allowed for in-depth analysis of real-world settings and facilitated the selection of a homogeneous sample of HR professionals and managers. Such a sample helped to ensure data saturation and allowed the researcher to capture diverse perspectives while maintaining consistency within the group. This method is particularly suitable for business and management research, where saturation indicates that no new insights are being gained, thus affirming the adequacy of the sample size. The participants who identified as 16 males and six females, with a mix of managers and HR professionals from varied geographic locations and age groups, ensuring a wide representation of industry experience.

4.6. Thematic Analysis

Thematic analysis was a critical part of the data analysis process. It involved alternating between the entire data set, coded extracts, and thematic analysis to identify key patterns. This approach allowed the researcher to explore the data deeply and produce reliable findings. NVivo's visual tools, such as word clouds and word trees, played a crucial role in identifying themes and ensuring transparency in the coding process. The researcher also employed a strategy of consistency checks to verify the accuracy of coding. The entire process of thematic analysis, from coding to the final report, allowed for a nuanced understanding of the data and its implications. The study followed a phenomenological interpretivist approach to uncover the subjective experiences of participants, which required careful attention to detail, transcription accuracy, and ongoing reflection. The research was built on strong theoretical and methodological foundations, emphasing credibility and confirmability. Credibility was established through prolonged engagement in the field, rigorous data management, and participant triangulation. Confirmability, meanwhile, was achieved by ensuring that the findings were supported by participant feedback. By adhering to these rigorous research protocols and validating results with participants, the researcher mitigated potential biases, ensuring the trustworthiness of the study's conclusions.

4.5. Data Analysis

Data analysis and interpretation were carried out using NVivo software, which facilitated the organisation, coding, and visual representation of complex qualitative data. NVivo's features, such as coding and visual tools like word clouds, enhanced the ability to identify patterns and connections that may have otherwise gone unnoticed. Consistent and methodical data collection and analysis were essential to the research's validity and reliability. Numerical results were integrated into the qualitative analysis to counter potential biases and provide a more objective perspective. The software also enabled the creation of an audit trail, ensuring transparency and accuracy throughout the analysis process. By employing NVivo and maintaining a systematic approach, the researcher ensured that the study's findings were credible and confirmable.

4.7. Theoretical Frameworks

This study employed an interpretivist ontological approach, grounded in human capital theory, expectancy theory, the technology acceptance model, and socio-technical systems theory. An exploratory qualitative methodology was used to explore the views, experiences, attitudes, and behaviours related to the research questions, following Patton's framework (Patton, 2020). A sector-wide approach was adopted to gather insights from the broader Australian commercial media industry, ensuring a comprehensive understanding of HRM dynamics. The inclusion of multiple organisations helped mitigate bias and provided a broader perspective. The validity of themes was confirmed using participant data from various organisations (Creswell & Plano Clark, 2023). Managers and HR professionals from the Australian commercial media industry were invited to participate, selected through homogeneous sampling to ensure a sufficiently large sample for analysis (Azungah, 2022). Data collection involved face-to-face, telephone, and online interviews with 22 participants, reaching saturation when no new issues emerged in the final interviews (Mason, 2022). The interviews comprised 18 semi-structured questions, with an open-ended question to allow for further exploration. This semi-structured approach provided direction while allowing flexibility and discovery of significant insights (Galletta, 2022). Interviews were audio-recorded to ensure thorough analysis and minimize misinterpretation, with transcripts subjected to thematic coding using NVivo software (Jackson, Bazeley & Bazeley, 2023).

4.8. Limitations

The study faced inherent limitations due to its interpretivist ontological approach, reflecting the specific circumstances of participants at a single point in time, which might differ if collected at another time (Saunders, Lewis & Thornhill, 2022). The qualitative nature of the research also imposes limitations, as qualitative data are not statistically representative and are challenging to replicate (Queirós, Faria & Almeida, 2023). Although 22 interviews were conducted, this sample size was deemed appropriate for in-depth analysis, avoiding the pitfalls of very large samples that might lead to superficial data (Cleary, Horsfall & Hayter, 2022). The study's usefulness is limited to evaluating existing theories, suggesting potential modifications rather than developing new theories (Theofanidis & Fountouki, 2022). The findings' applicability might be constrained by the specific context of the Australian commercial media industry. The research involved interviews with 22 managers and HR professionals from diverse locations, including capital cities, outer metropolitan areas, and regional locations in Australia. This diverse participant pool encompassed a range of genders, ages, and roles, providing a comprehensive understanding of perceptions regarding AI's contributions to key HR functions. The interviews aimed to explore how AI could enhance HR functions, opportunities it presents, and potential benefits and challenges. The insights revealed both similar and contrasting views between managers and HR professionals regarding key functions that could benefit from AI integration. The interviews were conducted using a semi-structured format, allowing for flexible and in-depth exploration of participants' views. This approach enabled the collection of rich qualitative data, providing insights into participants' thoughts on AI's potential impact.