Traditional Human Resources recruitment approaches to attract competent and talented employees are no longer effective
As part of my MBA studies, I recently completed a module on Strategic Human Resource Management, where I explored how companies attract and retain top talent. One of my assignments focused on the statement:
“Traditional Human Resources recruitment approaches to attract competent and talented employees are no longer effective.”
This topic resonated with me, as I have seen first-hand how conventional hiring practices struggle to keep up with today’s evolving job market, particularly in the technology sector. While some organisations have embraced AI-powered hiring tools to streamline recruitment, these systems are not without flaws—bias, lack of transparency, and privacy concerns often undermine their effectiveness.
I enjoyed writing this essay and wanted to share these insights with a wider audience. While the essay itself doesn't cover all aspects of the recruitment process or coverage of AI in that process, I think it's an interesting subject and could be expanded to cover many other aspects.
Traditional Human Resources recruitment approaches to attract competent and talented employees are no longer effective
The traditional recruitment sources, such as job boards, recruiters, and traditionalist interviews, have become less efficient in the technology industry, with Strategic Human Resource Management (SHRM) requiring now more robust, proactive and planned recruitment campaigns (Nankervis et al. 2023). In a counter move, some organisations have embraced AI-facilitated video interviews and predictive tools, such as HireVue and DeepSense, to streamline recruitment WSJ (2018). Notwithstanding, such technology is not flawless. Algorithm bias, lack of discretion, and concerns regarding privacy taint its dependability and fairness (Bogen & Rieke 2018). Also, using scientifically unsound AI-powered face reading features leads to disproportionate disadvantages for neurodiverse candidates (Barrett 2017; Rhue 2018). This essay will discuss how traditional and current AI-powered recruitment processes fall short. A balanced model of SHRM must integrate technological innovation with ethical, humane approaches, effective, and sound recruitment practices.
The traditional recruitment processes have not kept pace with the changing technological environment we live in today, where competencies are changing rapidly (Boxall & Purcell, 2022). Human Resource Management (HRM) theories recognise recruitment processes as a proactive activity that is in tune with the long-term organisation strategy (Boxall & Purcell 2022). At the same time, the Resource-Based View (RBV) of HRM looks at the competitive advantages built through talent procurement and maintenance, with Talent being seen as a scarce, valuable and not easily emulated resource (Barney 1991). Traditional recruitment, however, tends to value the speed of filling gaps over talent development for the long-term workforce, which leads to a disconnect between the business aims and the talent competencies.
According to Bersin (2022), 70% of employees worldwide are classified as passive candidates in that they do not seek jobs but can potentially be interested in new ones. In my recruitment experiences across France, America, and the UK, this has been shown to be accurate, with meetups that have proven to be an effective source for attracting high performers who generally do not apply for jobs. Unlike job boards and external recruiters, such events allow companies to introduce their work environment and innovations, providing a more profound and reflective recruitment practice.
In most instances, conventional recruitment cannot accurately assess candidates' competencies and adaptability in real-life settings. I adopted a four-step interviewing mechanism (Figure 1), with the additional feature of having candidates use whatever tools at their disposal to respond to a problem through live coding sessions. To my surprise, most candidates found the correct answer over the web. However, they failed to implement it in practice due to a lack of critical thinking skills. This reflects the difference between theoretical and practical skills that the traditional interview process has issues showing. According to competency recruitment theory, recruitment must specifically target competencies regarding behaviours and critical thinking, not technical competencies (Spencer, L & Spencer, S 1993).
Figure 1: Four-stage interview process
Source: Developed by Philip Davies 2020
In fact, some companies have utilized AI recruitment and filtering algorithms to do away with traditional inefficiencies, in the march of removing bias using unproven science-based technology such as face, voice and language analysis WSJ (2018). AI-facilitated recruitment processes strive to build a less subjective evaluation system by leveraging fact-based information. This approach predicts future success in candidates through the analysis of past recruitment information.
Like all tools, AI is not flawless, and one of its most significant concerns is bias exaggeration, in that AI learns through past experiences, and these experiences will have biases (Dastin 2018). Amazon’s AI recruitment software was disabled when it was realised to have a persistent bias towards male candidates simply because its training datasets had biases (Dastin 2018). Algorithms being trained on past recruitment performance WSJ (2018) is going to be bias due to the unconscious bias that inevitably been included in said performance (Bogen & Rieke 2018). That does not agree with values in SHRM, such as fair, ethical, and meritocratic recruitment (Nankervis et al. 2023).
The same holds for transparency and ethics: both act in disfavour of using such tools. AI tools act in a "black box" with little transparency about how and why a conclusion is drawn (Raghavan et al. 2020). There is a transparency issue and an ethics concern WSJ (2018) in companies employing such tools. AI software observes face, voice, and even direction of eyes, raising concerns about information security and intrusion of privacy with candidates not knowing such tools' use. AI tools reject candidates who do not fit traditional speech behaviour and face types, disproportionately rejecting neurodivergent candidates.
Over the years of hiring people and through the network of contacts I have in the industry, I have found that many people are reluctant about the use of AI-powered tools for interviews, with people suggesting they believe they would still be biased, worry about the science/reasoning training behind the models used within these tools. One such case was an engineer I hired in the UK who is neurodivergent and believes using these tools would mean he is never employed. Most people I have come across want to work for companies where they feel they can fit in, make a difference, and feel part of the team. They believe this technology is impersonal and unfair, making it hard, if not impossible, for them to showcase their capabilities. This contradicts Human Capital Theory, as Becker (1964) suggests that recruitment aims to maximise one’s contribution and sort candidates based on meaningful assessments, while shallow AI-made statistics show the opposite approach.
Attracting high performers while building a strong company brand requires a strong recruitment drive that goes beyond mere job filling. A recruitment journey that helps build a company brand notably helps in competitive sectors such as technology (Maurer 2019). At the same time, AI recruitment tools that have been discussed so far allow for the overreliance on impersonal video interviews while discouraging high performers from wanting to apply. On the other hand, a more immersive approach, such as technical problem-solving interviews with real-time feedback, helps build said company brand/profile (Nankervis et al. 2023).
To achieve best practice recruitment processes while traditional and AI-powered processes are flawed, companies must take a balanced approach combining technology with face-to-face contact, leveraging AI for improving processes with ongoing human supervision with more transparency. Networking events, open-source activities, or other industry meetups can be used to engage passive candidates instead of relying exclusively on job boards. Having an organised format for interviewing, such as my four-step model, also allows for the best evaluation of problem-solving and technical skills.
Armstrong & Taylor (2023) suggested that workforce planning in line with long-term business objectives where skill development is a priority instead of just filling vacancies is more critical. Beer et al. (1984), acknowledge employees as stakeholders whose morale and growth affect corporate success, and emphasize the need for aligning HR practices with the promotion of company-oriented goals in relation to framework ideas like the Harvard Framework for HRM. Traditional job board postings or reliance on external recruiters tend to focus on filling vacancies rather than actively building a sustainable talent pipeline. This short-term thinking overlooks a fundamental SHRM principle—preparing a workforce capable of adapting to evolving business needs (Boxall & Purcell, 2022). When recruitment is purely transactional, it neglects the long-term cultural and strategic alignment necessary for building a strong talent pool.
Similarly, the Resource-Based View (RBV) argues that employees are a unique asset that can provide long-term competitive value—but only when hiring strategies align with overarching business objectives and adapt to changing skill demands (Barney, 1991).
Traditional resume filtering and general competency questions have a poor record in choosing complex technical and interpersonal competencies for success in today's economy. In contrast, HRM structures such as High-Performance Work Systems (HPWS) endorse a whole-person recruitment practice that seeks technical expertise, collaboration, and innovation (Gowan et al. 2022). HRM literature increasingly highlights learning agility and continuous upskilling as key predictors of success—traits that traditional, rigid interview formats often fail to assess (Nankervis et al. 2023). This issue becomes especially evident when candidates perform well in scripted interview questions but struggle in real-world problem-solving scenarios—a challenge frequently observed in live coding assessments.
Additionally, traditional recruitment’s inability to assess cultural fit and engage passive candidates places organisations at a disadvantage in competitive industries. Modern HRM trends emphasise the importance of employer branding and candidate experience (CIPD, 2022). Conventional recruitment, which over-relies on job postings and basic interviews, seldom strengthens an employer's brand—especially in an era where top talent expects a personalised, transparent, and tech-enhanced hiring experience (Bersin, 2022).
While AI-powered tools enhance efficiency and data-driven decision-making, they often replicate existing biases if not carefully monitored—clashing with the fairness and strategic alignment central to SHRM principles (Bogen & Rieke, 2018). This underscores the need for a hybrid hiring model that blends human-centric strategies—such as industry networking events, targeted meetups, and structured competency-based assessments—with the operational efficiencies of AI. By integrating these elements into strategic workforce planning, recruitment transcends a transactional process and becomes a future-focused HR strategy. Companies that adopt this model can move beyond the limitations of traditional hiring, champion ethical and inclusive recruitment, and build an agile, high-performing talent pipeline essential for long-term success.
This essay has established that traditional recruitment processes and AI recruitment tools both fall short of meeting the strategic needs of the technology industry. Traditional hiring, which relies on job boards, recruiters, and conventional interviews, prioritises speed over strategic alignment, making it ineffective at engaging passive candidates or accurately assessing real-world competencies (Nankervis et al. 2023; Boxall & Purcell 2022). Meanwhile, AI-driven hiring tools such as HireVue and DeepSense present ethical concerns, including algorithmic bias, lack of transparency, and flawed facial analysis, disproportionately disadvantage diverse candidates (Bogen & Rieke 2018; Barrett 2017; Rhue 2018).
To overcome these problems, the SHRM model should be balanced with integration with technological innovation and human-focused recruitment strategies. Organisations use structured interviews, live problem-solving exercises, and industry relationships to establish fair, efficient, and strategically driven hiring processes that promote diversity and inclusion while attracting the best talent and creating a competitive workforce.
References
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