The augmented recruiter: examining AI integration and decision-making dynamics in Qatar's organisational hiring practices

The integration of artificial intelligence (AI) into human resource management (HRM), particularly in recruitment and selection processes, represents one of the most significant technological disruptions in contemporary organisational practice (Nawaz et al., 2024; Shams et al., 2025). As organisations worldwide grapple with the complexities of talent acquisition in increasingly competitive labour markets, AI-powered recruitment tools offer potential solutions to longstanding challenges of efficiency, objectivity and effectiveness (Budhwar et al., 2022). These technologies promise to transform recruitment from a predominantly manual, time-intensive process to one characterised by automation, data-driven decision-making and predictive capabilities (Madanchian, 2024).

The adoption of AI in recruitment encompasses diverse applications, including applicant tracking systems (ATS), CV screening algorithms, automated interview scheduling, chatbots for candidate engagement and predictive analytics for candidate assessment (Tambe et al., 2019). While proponents advocate that these tools enhance organisational efficiency and reduce human bias (Boateng, 2021), critical perspectives highlight concerns regarding algorithmic bias, candidate experience and the diminution of human judgement in selection decisions (Madanchian, 2024). This tension between technological optimisation and human-centred recruitment practices forms the conceptual backdrop against which this study is situated.

Despite growing scholarly and practitioner interest in AI-powered recruitment, empirical research examining organisational outcomes of AI recruitment implementation remains limited, with existing studies predominantly focused on technical capabilities rather than practical implications (van Esch et al., 2019). Second, there is little research exploring stakeholder experiences across organisational hierarchies, with most studies privileging executive perspectives over those of recruitment specialists, line managers and other affected employees (Vardarlier and Zafer, 2020). Third, geographical imbalance in existing research has resulted in limited understanding of AI recruitment in non-Western contexts, particularly in technologically advancing Middle Eastern economies like Qatar (Al-Shaiba et al., 2020).

This study addresses these gaps by examining how the implementation of AI-powered recruitment processes impacts organisational efficiency within Qatari companies. Drawing on qualitative data from key stakeholders across two prominent Qatari organisations, the research explores how AI recruitment tools are reshaping traditional hiring practices, the challenges and opportunities they present and their implications for organisational effectiveness. While existing research has predominantly focused on Western settings (Bankins et al., 2024; Madanchian, 2024; Shams et al., 2025) and technical implementation aspects (van Esch et al., 2019), this study provides empirically grounded insights into how AI recruitment technologies interact with the unique socio-cultural dynamics of Qatari organisations. By situating this analysis within Qatar's unique socioeconomic context, characterised by rapid technological advancement alongside distinctive cultural and institutional arrangements, this study offers valuable insights for both theory and practice.

The research is guided by the following objectives: (1) to understand how AI integration affects recruitment process efficiency in Qatari organisations; and (2) to explore the connection between AI systems and human judgement in recruitment decision-making.

The remainder of this paper proceeds by reviewing the literature on AI-enabled recruitment, organisational efficiency and technology adoption in Middle Eastern contexts. We then outline the research methodology and analytical approach, present findings structured around four themes and discuss theoretical and practical implications, before concluding with limitations and directions for future research.

The integration of AI into recruitment marks a significant shift within HRM, reshaping how organisations attract, assess and select talent. Although early conceptual discussions of AI-supported HRM emerged in the 1990s (Lawler and Elliot, 1996), sustained scholarly attention has intensified only in the past decade alongside advances in machine learning, big data analytics and digital platforms (Budhwar et al., 2022). Despite this growth, conceptual clarity regarding AI's strategic role in HRM remains limited, particularly in relation to contextual, institutional and ethical dimensions (Prikshat et al., 2023). Recruitment has become the primary domain for AI adoption, driven by organisational pressures to enhance efficiency, manage large applicant volumes and improve decision consistency (van Esch et al., 2019; Tambe et al., 2019).

Early e-recruitment systems largely digitised administrative tasks such as job advertising and CV screening (Tambe et al., 2019). Contemporary AI-enabled recruitment tools extend beyond digitisation to incorporate predictive analytics, automated screening algorithms and conversational agents that interact with candidates in real time (Li et al., 2023). These developments reflect Davenport and Ronanki's (2018) framework of AI applications, including process automation, cognitive insight and cognitive engagement, which are increasingly embedded within recruitment workflows (Bankins et al., 2024). Empirical studies suggest that AI adoption can enhance recruiter productivity, accelerate decision cycles and improve candidate communication, with many HR professionals reporting perceived improvements in hiring outcomes (Ebrahim and Rajab, 2025; Stefanowicz, 2025).

Recruitment effectiveness has traditionally been assessed using metrics such as time-to-hire, cost-per-hire and quality-of-hire (Phillips and Gully, 2015). AI technologies have been associated with improvements across these indicators, particularly through automating high-volume screening and standardising evaluation criteria (El Ouakili, 2025). By processing large datasets rapidly and applying consistent decision rules, AI systems can reduce administrative burdens and enable recruiters to focus on higher-value activities. Additionally, AI-driven chatbots and recommendation systems may enhance candidate engagement through timely feedback and personalised interaction (Nawaz et al., 2024).

However, efficiency gains do not necessarily equate to improved decision quality or fairness. Scholars caution that algorithmic screening systems may exclude qualified candidates whose profiles deviate from historically dominant career patterns or predefined keyword parameters (Tambe et al., 2019). Evidence indicates that rigid filtering mechanisms can disadvantage individuals with non-linear career trajectories or unconventional educational pathways, potentially narrowing the diversity of applicant pools (El Ouakili, 2025). These risks are compounded by AI's limited capacity to interpret contextual nuance, non-standard CV formats and culturally embedded indicators of competence (Rathore, 2023), highlighting tensions between standardisation and inclusivity.

Ethical concerns surrounding algorithmic bias and governance have therefore become central to debates on AI-driven recruitment. AI systems are shaped by the data on which they are trained, and where historical datasets reflect structural inequalities, algorithms may reproduce and amplify those biases (Raghavan et al., 2020; Boateng, 2021). Such concerns extend beyond technical bias to broader issues of transparency, accountability and legitimacy in automated decision-making. In recruitment contexts, opaque algorithmic processes may undermine trust among candidates and practitioners while exposing organisations to reputational and regulatory risks.

These challenges are particularly pronounced in non-Western and multicultural labour markets. Many AI recruitment tools are developed within Western institutional contexts and may embed assumptions about qualifications, career progression, or behavioural indicators that do not translate seamlessly across regions (Al-Shaiba et al., 2020; Adekoya et al., 2024). In Gulf Cooperation Council (GCC) countries, recruitment is further shaped by nationalisation policies, sponsorship systems and socio-cultural norms related to nationality and gender (Budhwar and Mellahi, 2016; Waxin and Bateman, 2016). Comparative studies in GCC and emerging economy contexts suggest that while AI adoption can enhance operational efficiency, persistent concerns remain regarding algorithmic transparency, fairness and alignment with local labour regulations (Trigui et al., 2024; Albous et al., 2025).

In response, the literature increasingly advocates “responsible AI” approaches that integrate ethical oversight, bias auditing, explainability and human accountability into recruitment systems (Wilson and Daugherty, 2018; Papagiannidis et al., 2025). This has shifted the scholarly consensus away from narratives of technological replacement toward models of human–AI augmentation. Under such approaches, AI functions as a decision-support tool that enhances human judgement while leaving context-dependent, ethical and relational evaluations to human recruiters (Karaboga and Vardarlier, 2020). Hybrid “human-in-the-loop” configurations enable organisations to harness AI's computational strengths while mitigating risks associated with bias, opacity and decontextualised decision-making (Mori et al., 2025).

Despite its potential, the organisational integration of AI in recruitment remains challenging. Resistance to change, concerns about job displacement and limited digital capabilities among HR professionals can constrain adoption (Horodyski, 2023). Moreover, increasing regulatory scrutiny and societal expectations for algorithmic transparency require organisations to invest in governance capabilities alongside technological infrastructure. These dynamics underscore that AI-driven recruitment constitutes not merely a technical innovation, but a broader socio-organisational transformation requiring alignment between technology, human agency and institutional context.

To explore how AI-powered recruitment systems are understood and enacted within Qatar's socio-cultural and institutional environment, this study draws on socio-technical systems (STS) theory (Trist and Bamforth, 1951; Mumford, 2006) alongside context-sensitive technology adoption models (Bagozzi, 2007; Venkatesh et al., 2003, 2016). Together, these frameworks provide a lens for interpreting how AI technologies are embedded within organisational practices, social relationships and contextual norms.

Originating from Trist and Bamforth (1951), STS theory conceptualises organisations as comprising interdependent social and technical subsystems whose alignment shapes organisational functioning (Mumford, 2006). From this perspective, technological change is not treated as a discrete intervention but as a socially mediated process that reconfigures roles, workflows and decision authority. In AI-enabled recruitment, algorithms do not merely automate screening tasks; they also reshape how competence is defined, how evaluation criteria are constructed and how accountability is negotiated (Leicht-Deobald et al., 2019). Within HRM research, STS has been used to interpret how digital tools interact with organisational routines and professional identities (Makarius et al., 2020). This perspective is therefore valuable for understanding how recruiters make sense of AI systems and how organisational actors adapt to emerging technological affordances (Fenwick et al., 2024).

Complementing STS, the Unified Theory of Acceptance and Use of Technology highlights how technology adoption is shaped by user perceptions, organisational readiness and institutional expectations (Venkatesh et al., 2003, 2016). Unlike earlier models such as TAM (Davis, 1989), UTAUT2 incorporates demographic and cultural moderators, making it particularly relevant in cross-cultural settings. In the Middle East, institutional logics, religious norms and labour policies such as Qatarisation influence how recruitment technologies are interpreted and legitimised (Budhwar and Mellahi, 2016; Waxin and Bateman, 2016). By integrating STS and context-sensitive adoption models, this study conceptualises AI recruitment as a socially constructed and institutionally embedded process rather than a purely technical innovation.

This study adopted an interpretivist research philosophy, recognising that organisational experiences with AI recruitment technologies are shaped by the perceptions, meanings and interactions of various stakeholders (Saunders et al., 2019). Given the socio-technical complexity of AI and the relative novelty of its application in Middle Eastern recruitment practices, this philosophical lens enabled contextual sensitivity and the exploration of subjective experiences often overlooked in positivist approaches (Baroudi et al., 1986). The research followed an abductive reasoning approach, which involves an iterative movement between theory and empirical data (Saunders et al., 2019).

We adopted a case study design, focussing on two prominent Qatari organisations. These organisations were purposively selected based on three criteria: (1) recent implementation of AI-driven recruitment tools (within 12–18 months), (2) representation of different AI adoption approaches within Qatar's diversified business sector and (3) organisational willingness to provide access across hierarchical levels. Both firms are large, diversified business groups employing over 500 staff. Firm A integrated an ATS with AI screening capabilities, while Firm B used AI tools, including ChatGPT, for CV screening. Their shared national context but differing implementation pathways offer valuable comparative insights into organisational adaptation (Yin, 2018), representing broader trends in Qatar's business landscape where organisations adopt AI incrementally alongside existing systems. Figure 1 shows a snapshot of the methodological process.

Purposive sampling was used to identify individuals directly involved with or significantly exposed to AI recruitment technologies (Patton, 2015). Twenty-two participants were recruited across both organisations, reflecting representation at multiple organisational levels: top-level executives (n = 4), mid-level managers (n = 11) and lower-level staff (n = 7). This distribution ensured hierarchical diversity in perspectives on AI-enabled recruitment (See Table 1).

The following ethics approval, semi-structured interviews were conducted between January and March 2025. Interview questions explored: (1) AI tools currently used and their functionality, (2) perceived efficiency changes (time-to-hire, cost and workload), (3) decision-making processes and human-AI interaction, (4) implementation challenges and training needs and (5) recruitment outcomes and quality-of-hire perceptions. Questions were tailored to respondent roles; for example, executives addressed strategic investment decisions, while HR staff focused on operational impacts. Eighteen interviews were conducted face-to-face (audio-recorded and transcribed verbatim), while four responded via email due to scheduling constraints.

Research quality was ensured through established trustworthiness criteria (Lincoln and Guba, 1985). Credibility was supported by triangulating interview data with organisational documents (AI implementation policies, recruitment metrics reports and training materials) to verify and contextualise participant accounts, and by peer debriefing sessions to test emerging interpretations. Transferability was enhanced by providing rich, thick descriptions of both organisations' contexts, including their size, sector, AI implementation approaches, organisational structures and recruitment processes, enabling readers to assess the applicability of findings to their own settings (Saunders et al., 2019). Dependability was addressed through maintaining a transparent audit trail, with consistent application of coding frameworks and code-recode checks. Confirmability was supported through reflexive journaling and documentation of analytic reasoning. Thematic saturation was reached when no new codes or themes emerged across the final four interviews (Guest et al., 2006).

The data were analysed using thematic analysis following Braun and Clarke's (2006) six-step framework (See Figure 2). This approach is suitable for identifying and interpreting patterns of meaning within qualitative datasets, while remaining flexible in terms of context and depth.

Thematic maps were used to visualise relationships and guide theme refinement. To enhance analytical rigour, analyst triangulation was applied, with two researchers independently coding and reconciling discrepancies (Patton, 2015). Member checking was conducted by sharing preliminary findings with participants for feedback, enhancing interpretive accuracy (Lincoln and Guba, 1985). Reflexive journaling (a process through which researchers documented assumptions, analytic decisions and evolving interpretations to enhance transparency and confirmability) was also used throughout the analysis to track analytic decisions and support transparency. Table 2 presents the summary of the thematic analysis.

From the analysis, four themes emerged: (1) process optimisation through AI integration, (2) subjectivity in AI-Powered recruitment, (3) recruitment strategies in the age of AI and (4) strategic investments in AI (See Table 1). These themes are discussed in this section.

The analysis revealed a significant focus on how AI-powered recruitment tools have transformed organisational processes. This theme captures efficiency enhancements following AI implementation and includes two distinct subthemes: organisational efficiency enhancement and process streamlining and standardisation.

The implementation of AI-powered recruitment tools demonstrated significant impacts on organisational efficiency. At Firm A, the integration of an ATS transformed recruitment pipeline management by creating a unified database for CV storage, retrieval and assessment. This centralised system particularly improved interdepartmental communication by replacing fragmented email-based processes.

The most dramatic efficiency improvements were described by the CEO of Firm B (Participant 9), who uses ChatGPT for CV screening:

These findings illustrate how AI-powered recruitment tools significantly streamline hiring processes, enhance communication and reduce administrative burdens. From ATS integration to ChatGPT-assisted screening, the tools improve speed, accuracy and collaboration. Overall, AI adoption in recruitment not only boosts operational efficiency but also empowers decision-makers with timely, data-driven insights for more strategic talent acquisition.

The implementation of the ATS at Firm A facilitated a transition to largely paperless recruitment processes. Previously fragmented document management systems were consolidated. The AI-powered system improved procedural compliance and accountability through enforced process steps:

This enforced compliance ensured complete documentation and reduced errors in the recruitment process. The standardisation of templates and communication pathways significantly reduced administrative overhead in applicant interactions, while enhanced precision in candidate filtering ensured that only qualified candidates progress through the recruitment pipeline.

A recurring pattern in participant responses concerned the link between AI's objective assessment capabilities and the subjective dimensions of recruitment decisions. This theme emerged from participants' reflections on human judgement in an increasingly automated process. Three subthemes emerged: bias mitigation through AI implementation, human component and cultural fit assessment.

The integration of AI tools demonstrated potential for reducing human biases. Participants consistently acknowledged that AI systems can help mitigate partiality by implementing objective screening criteria:

However, an important distinction emerged regarding the use of AI systems to filter candidates based on specific criteria such as gender, language skills, or nationality. Participants emphasised that such filtering does not necessarily stem from bias but rather reflects legitimate job requirements:

These findings reveal that while AI tools can reduce human bias through standardised and objective screening criteria, their use still reflects underlying human decisions regarding job requirements. Although filtering based on specific attributes may be necessary in certain roles, it raises ethical considerations. Thus, AI's bias mitigation potential depends on how inclusively and responsibly these systems are configured and applied.

Despite the advantages of AI in reducing bias and improving efficiency, participants unanimously emphasised the continued importance of human judgement in the recruitment process:

Participants positioned AI as an assistive tool rather than a replacement for human recruiters:

These insights highlight that while AI enhances recruitment efficiency and supports bias reduction, it cannot fully replace human involvement. Participants view AI as a valuable assistant, not a substitute, recognising the irreplaceable role of human intuition, contextual understanding and interpersonal judgement in making nuanced hiring decisions and ensuring candidate suitability beyond algorithmic assessments.

The significance of cultural fit emerged strongly across participant accounts, highlighting it as a critical factor in hiring decisions that remains outside the current capabilities of AI systems:

Poor cultural fit was commonly linked to higher rates of employee turnover:

Organisations were found to weigh interpersonal compatibility alongside technical competencies:

These findings underscore the irreplaceable role of human judgement in assessing cultural fit, which remains beyond the reach of current AI capabilities. While AI supports technical screening, successful hiring decisions also depend on interpersonal compatibility, alignment with organisational values and workplace integration, factors critical for retention and long-term performance that are best evaluated through human insight.

Despite embracing AI tools, participants consistently described maintaining diverse recruitment approaches tailored to specific circumstances. This theme emerged from participants' detailed accounts of their practical sourcing methods and strategic hiring decisions. The results are organised into two subthemes: diverse sourcing channels in modern recruitment and role-specific recruitment approaches.

Organisations in Qatar continue to utilise multiple sourcing channels despite implementing AI-powered recruitment systems. LinkedIn emerged as a predominant sourcing channel at Firm A, consistent with broader industry trends. Social media plays a significant role, with Participant 4 noting that “about 30–40% of our sources are through social media.” Employee referrals remained a cornerstone of Firm A's hiring strategy, accounting for approximately “40%” of their hiring according to Participant 19, the Learning and Development Manager. For specialised positions, Firm B occasionally turned to external expertise, with Participant 17 noting that sometimes they source candidates from a “relative, friend” or use “a recruitment agency.”

These findings show that even with AI-powered recruitment systems, organisations in Qatar maintain a diverse mix of sourcing channels. Platforms such as LinkedIn, social media, employee referrals and external recruitment agencies continue to play vital roles. This hybrid approach allows firms to reach wider talent pools, leverage trusted networks and tailor sourcing strategies to specific hiring needs.

Job role characteristics significantly influence recruitment strategies. Organisations seek candidates with specific qualifications to meet job requirements, but this process varies by position difficulty:

The time required for recruitment similarly depends on role characteristics, with Participant 22 observing that “there are positions that are faster than before,” while Participant 2 indicated that hiring time varies “depending on the position because some positions take a long time.”

These findings highlight that recruitment strategies and timelines are heavily influenced by the nature of the job role. Specialised or hard-to-fill positions often require proactive sourcing methods like headhunting, while routine roles may follow standardised procedures. Consequently, recruitment duration and effort vary, underscoring the need for adaptive strategies aligned with specific role requirements.

Financial considerations and future planning have emerged as significant concerns for organisations implementing AI recruitment technologies. This theme captures participants' strategic thinking about technological investment and long-term implementation planning. Analysis revealed two distinct subthemes: cost-benefit analysis of AI recruitment tools and strategic vision and implementation roadmap.

Organisations implementing AI-powered recruitment tools evaluate both immediate costs and long-term returns on their investments. At Firm A, the adoption of the ATS was viewed as a strategic investment aimed at addressing specific operational challenges. Participant 12 was unequivocal in their assessment: “It's worth all the money and effort.”

The CEO of Firm B (Participant 9) provided a detailed perspective on the relationship between time efficiency and financial impact:

These insights underscore the perceived value of AI recruitment tools in terms of both efficiency and financial return. While initial investment costs exist, participants emphasised substantial time savings that translate directly into cost reductions. AI tools are thus seen not just as technological upgrades but as strategic assets that enhance productivity and deliver measurable economic benefits over time.

Beyond current implementations, the interviews revealed clear strategic visions for expanding AI's role in recruitment processes. Firm A's current AI implementation focuses primarily on CV filtering and database management, but the organisation has identified specific expansion areas:

At Firm B, the strategic vision encompasses a more comprehensive digital transformation of the recruitment process:

These findings reveal a forward-looking commitment to expanding AI integration in recruitment. Both firms recognise current limitations and are planning strategic enhancements, such as candidate evaluation tools, video interviews and full-cycle digital recruitment systems. This proactive vision reflects a broader goal of transforming recruitment into a seamless, data-driven and fully automated process aligned with organisational growth and innovation goals.

This research sought to explore how AI integration affects recruitment process efficiency in Qatari organisations and to explore the connection between AI systems and human judgement in recruitment decision-making. The findings reveal a complex interplay between technological capability, human judgement and cultural context in shaping AI-assisted recruitment practices within Qatari organisations. While AI integration has undoubtedly enhanced efficiency, standardisation and transparency in recruitment, the evidence underscores that its value lies in augmentation rather than the replacement of human decision-making. Efficiency gains were not limited to faster candidate screening or reduced administrative workload; they also reflected a more strategic reallocation of human effort toward higher-value tasks requiring empathy, contextual interpretation and interpersonal evaluation. This shift supports Wilson and Daugherty's (2018) argument that AI enhances human capability rather than rendering it obsolete.

Participants' experiences demonstrate that AI streamlines repetitive tasks, such as CV screening, data management and interdepartmental coordination, corroborating earlier studies that highlight reductions in recruitment costs and time-to-hire (El Ouakili, 2025). However, efficiency in this context transcends metrics. It involves qualitative improvements in decision-making and organisational communication, aligning with STS theory (Mumford, 2006), which posits that technological progress is meaningful only when integrated with the human and organisational subsystems that support it. The transition to paperless, standardised recruitment workflows and the enforcement of procedural compliance, as seen in Firm A, epitomise how technology can restructure social processes by creating accountability and data coherence. These results reaffirm Thomas's (2024) argument that digital transformation's real impact lies in organisational redesign and behavioural adaptation rather than in the technology itself.

Despite these positive outcomes, the findings reveal persistent limitations in AI's analytical capabilities, particularly in qualitative and contextual assessments. The inability of AI systems to evaluate cultural fit, identified by participants as the most critical determinant of long-term retention, reflects a limitation well-documented in literature (Mori et al., 2025). Participants' emphasis on cultural compatibility as a factor “AI cannot measure” reinforces that effective hiring extends beyond data patterns to relational and affective dimensions. In Qatar's collectivist and relationship-oriented business culture (Budhwar and Mellahi, 2016), such qualitative dimensions are indispensable. Consequently, human evaluators remain essential in ensuring alignment between organisational values, interpersonal dynamics and candidate potential.

The findings further illustrate how AI-driven objectivity coexists with continuing human subjectivity. Participants viewed AI as a useful tool for mitigating human bias by standardising screening criteria. However, the same systems were configured to filter candidates based on gender, language, or nationality when these were deemed job-relevant. While participants rationalised such filters as practical necessities, they raised ethical concerns surrounding algorithmic fairness and inclusivity (Raghavan et al., 2020; Horodyski, 2023). This duality highlights what Leicht-Deobald et al. (2019) term the “standardisation paradox,” where efforts to enhance fairness through automation may inadvertently embed bias if human-defined parameters remain unexamined. The issue is especially salient in multicultural labour markets like Qatar's, where cultural norms and localisation policies intersect with global standards of non-discrimination. Thus, AI's promise of impartiality is contingent on transparent configuration and responsible governance, a finding consistent with Papagiannidis et al. (2025) and Fenwick et al. (2024), who advocate for human-in-the-loop systems that combine algorithmic rigour with contextual oversight.

Resistance to change emerged as another key dimension of AI implementation. Employees initially expressed apprehension that automation might devalue human expertise or threaten job security. Overcoming such resistance requires deliberate change management, continuous training and stakeholder engagement. These findings echo Weber et al. (2023), who emphasised that organisational readiness, user competence and trust are pivotal to realising AI's potential. In both firms, gradual integration and customisation were critical to acceptance. Configuring AI systems to align with local workflows and managerial preferences enhanced usability and relevance, supporting Li et al. (2023)'s view that adaptable system design is essential for successful adoption. This also aligns with Venkatesh et al.’s (2016) context-sensitive technology adoption framework, demonstrating that perceived usefulness is mediated by organisational culture, user familiarity and leadership support.

The hybrid recruitment model observed in this study, where AI handles preliminary screening and humans conduct final evaluations, represents a pragmatic response to the automation–augmentation paradox (Raisch and Krakowski, 2021). Participants recognised that AI's strengths in speed and consistency complement human recruiters' nuanced judgement, creating a symbiotic relationship between machine precision and human discernment. Rather than displacing HR professionals, AI redefines their roles, shifting focus toward strategic and relational dimensions of talent acquisition. This echoes Karaboga and Vardarlier's (2020) argument that the future of recruitment depends on the effective orchestration of both human and technological contributions.

This study makes three significant theoretical contributions. First, it advances STS theory by demonstrating that AI adoption in HRM constitutes a fundamental reconfiguration of organisational relational architecture, not merely technical integration. Where Mumford (2006) posited alignment between technical and social systems, this study reveals how AI necessitates renegotiation of trust boundaries, decision authority and accountability. In relationship-oriented cultures, technological efficiency is subordinate to relational legitimacy, thus challenging techno-centric adoption models.

Second, the study introduces “contextual algorithmic governance” to technology acceptance literature. Unlike UTAUT's focus on individual adoption determinants, our findings reveal that AI legitimacy depends on alignment with institutional logics, particularly how algorithmic decision-making accommodates nationalisation policies (Qatarisation), cultural norms around trust and local regulatory frameworks. This extends technology adoption theory by demonstrating that acceptance is mediated by socio-institutional compatibility, not only perceived usefulness.

Third, the research refines automation-augmentation discourse by demonstrating that augmentation is a cultural necessity in collectivist contexts, not a design choice. Where Western literature frames augmentation versus automation as strategic options (Raisch and Krakowski, 2021), this study reveals that in relationship-oriented cultures, AI without human oversight lacks legitimacy regardless of technical capabilities. This culturally-contingent constraint represents a novel boundary condition for automation theories.

Organisations should treat AI as an augmentative technology, preserving human judgement for final decisions (Raisch and Krakowski, 2021). Customisation should align systems with local workflows and values. Investment in training and communication mitigates resistance, while ethical governance ensures fairness (Papagiannidis et al., 2025). AI's commercial value lies not solely in cost savings but in enabling HR professionals to focus on strategic activities, including relationship building, employer branding, talent development and driving competitive advantage.

In Qatar, where Qatarisation mandates prioritise national employment, AI recruitment must support nationalisation objectives. Policymakers should establish frameworks requiring compliance through audits. Building public trust requires transparency, mandating disclosure of AI use and rejection explanations. Educational initiatives are essential at the university level and through public campaigns. For wider GCC contexts, responsible AI adoption requires governance aligning innovation with societal values and policy objectives. This study focused on two large Qatari firms, potentially limiting generalisability. Longitudinal studies would trace AI system evolution. Future research should compare experiences across cultural settings and investigate candidate perspectives. As AI systems grow autonomous, inquiry into ethical boundaries, data privacy and accountability is needed.