AI is flooding your talent pipeline – but not always with the right people

For HR leaders grappling with surging application volumes and mounting pressure to fill frontline roles, the rise of large language models (LLMs) is rapidly reshaping what “good” recruitment looks like.

Jack Malpass, chief operating officer at New Zealand-based hiring platform Weirdly, says the traditional levers HR has relied on – CVs, agency networks and experience-led screening – are no longer fit for purpose in a high-volume, AI-saturated market.

AI has fundamentally shifted the balance of power in early-stage recruitment. Where candidates once struggled to articulate their strengths or tailor applications, they now have instant access to tools that can draft polished cover letters, optimise CVs and submit applications across multiple roles in minutes.

According to Malpass, AI has “changed the game for hiring, particularly with the rapid rise of large language models.” Candidates can now express themselves more clearly, apply more quickly and broaden their job search with far less effort than before.

For employers, however, this has created a paradox. Application volume is increasing, but genuine relevance is often decreasing. AI‑savvy jobseekers are increasingly applying for quantity rather than fit, leading to extremely high application numbers in which a large proportion of candidates do not meet the core requirements of the role. Talent teams can be left wading through stacks of applications that look strong on paper but lack the underlying capability needed to perform in the job.

The impact for HR is significant. Recruitment teams face overwhelming administrative load, and time‑to‑hire can actually increase despite the illusion of a “strong” pipeline. This problem is particularly acute in high-volume environments such as retail, hospitality and customer service, where hundreds of similar roles may be open at once and where the noise generated by AI‑assisted applications is at its loudest.

The LLM era is exposing the limits of CV‑ and relationship‑led hiring. Malpass argues that the rise of generative AI runs directly against traditional agency and corporate models that still lean heavily on CVs and recruiter relationships. That approach can work when organisations are filling a small number of specialist or senior roles, but it becomes unsustainable when they are hiring at scale. Manually scanning CVs, shortlisting by hand and relying on human gatekeepers simply do not keep pace with the volume and speed of today’s applications.

This challenge is amplified by organisational inertia. Many large employers and recruitment firms are slower to adopt new tools, held back by lengthy procurement processes, risk concerns and legacy systems. In practice, that often means AI is being embraced more aggressively by candidates than by the organisations assessing them. The result is a growing asymmetry between how talent presents itself and how it is evaluated.

For HR leaders, the question is no longer whether AI will shape hiring, but whether internal processes, technology and operating models are modern enough to cope with its consequences. Clinging to legacy, CV-centric practices in a candidate‑driven AI environment risks both inefficiency and missed talent.

In frontline hiring, soft skills beat experience

If CVs are losing their value, the obvious question for HR is what should replace them. For Malpass, especially in frontline and high-volume roles, the answer lies in attributes and soft skills rather than in formal experience. He points to his own early career as an illustration. As a teenager working at McDonald’s with no prior experience, he found that simply being reliable, turning up on time and being ready to work were far more important than anything that could be written on a CV. In contrast, some older and more experienced colleagues struggled to meet the basic professional standards that keep a business running from day to day.

From his perspective, the real differentiators in frontline roles are qualities such as how candidates relate to customers, how patient they are under pressure and how consistently they show up and follow through. These attributes – often grouped under the banner of soft skills – are hard to infer from job titles or education history but are central to performance in customer‑facing and operational environments.

In many frontline settings, past experience or educational background is simply not the best indicator of success. These roles tend to be process‑driven and supported by strong internal training programs, meaning that most people can learn the technical elements of the job relatively quickly. What matters most is how they behave once they are in the role: whether they can sustain good customer interactions, whether they work well with colleagues and whether they uphold the standards the organisation relies on. For HR leaders, this calls for a deliberate pivot away from screening based on what a candidate has done on paper and towards systematically assessing how they are likely to behave in real-world situations.

Why the CV is no longer fit for purpose in high-volume hiring

As AI‑generated CVs proliferate, the document itself is losing much of its diagnostic value. Malpass observes that CVs are increasingly starting to look the same, with many drafted or refined by LLMs. Language is smoothed out, key skills are mirrored from job descriptions and experience is framed in ways that precisely echo the employer’s own criteria. On the surface, candidates can appear remarkably similar, even when their real‑world behaviours, values and work ethic are vastly different.

In this environment, it is becoming harder and harder to tell whether someone is a diligent, values‑led performer just by scanning a couple of pages. For high‑volume and frontline roles in particular, Malpass believes that the CV should no longer sit at the heart of the process. He argues that in many cases it should be removed from the initial stages entirely. Rather than asking an AI agent – or a human recruiter – to sift through hundreds of near‑identical documents, he suggests that the first gate in the process should be built around values, culture fit and core behavioural attributes.

This shift could involve structured assessments that probe how candidates think and act, situational judgement exercises that simulate real scenarios from the shop floor, realistic job previews that make expectations explicit, or carefully designed screening questions that surface genuine alignment with organisational values. The key, in his view, is to move the focus from how well a candidate can present a document to who they actually are and how they are likely to show up in the role.

Beyond inefficiency, Malpass argues that a CV‑centric approach raises serious questions of fairness and inclusion. Traditional hiring processes that hinge on CVs and cover letters do not just measure work history or achievement; they also inadvertently test a candidate’s access to support, familiarity with corporate norms, language proficiency and level of comfort with technology.

In reality, a polished CV often reflects a network of advantages. A candidate whose parents work in corporate roles, for example, may have had one‑to‑one help shaping their narrative, structuring their achievements and fine‑tuning the language on each application. Others may have the funds and confidence to pay professional CV writers or leverage high‑end AI tools and devices. By contrast, a first‑generation English speaker or someone from a lower socio‑economic background might have the right underlying attributes and potential, but a far less polished document – and far less ability to exploit AI to bridge that gap.

In Malpass’s view, using the CV as the primary entry point therefore risks reinforcing the very societal challenges many organisations claim they want to address. Candidates who could thrive in frontline roles, bring strong values and contribute meaningfully to culture are often filtered out long before they have the chance to demonstrate what they can do. Shifting to a values‑based assessment of who a person is, rather than how well they can write or generate a CV, represents a fairer and more equitable way to determine whether they are likely to succeed.

For HR and talent leaders, Malpass’s message is that the age of AI‑enhanced candidates demands a . AI has permanently changed how candidates apply and how they present themselves, and organisations cannot rely on the same screening tools and expect different outcomes. CVs and cover letters are no longer reliable or equitable primary filters in frontline recruitment, and persisting with them as the central gate risks both operational strain and reputational damage.

Instead, HR functions need to reorient selection around soft skills, values and behavioural attributes that genuinely predict success on the job. That means investing in assessment methods and technology that look beyond the page, and in processes that enable recruiters and hiring managers to identify potential as well as polish. It also means making sure employers, not just candidates, are harnessing AI in smarter, fairer ways – automating low‑value tasks, augmenting human judgement and building experiences that are both efficient and inclusive.

In a labour market shaped by LLMs and rising expectations of fairness and opportunity, the organisations that thrive will be those willing to retire legacy practices and design recruitment systems that truly see beyond the CV.