The new recruiting paradox

Too many applications – and still not enough talent

Recruiting has entered a paradox.

On one hand, organisations are overwhelmed by an unprecedented volume of applications. On the other, hiring managers still struggle to find candidates who truly match the role.

For HR leaders and decision-makers, this creates a costly ambiguity: more candidates to review, but less certainty about who will actually succeed in the role.

This article explores why this paradox is emerging – and how organisations can adapt their hiring strategies to make faster and more reliable decisions.

The application flood: when AI multiplies candidate volume

Generative AI has dramatically lowered the barrier to applying for jobs.

Candidates can now generate tailored CVs and cover letters in seconds and apply to dozens – sometimes hundreds – of positions automatically. As a result, application volumes have surged across industries.

Recent data illustrates the scale of the shift:

Job applications on major platforms have increased by more than 45% in the past year, with roughly 11,000 applications submitted every minute globally.
46% of CVs are now generated or significantly assisted by AI tools such as ChatGPT or Gemini.
62% of recruiters report that most applications they receive are not qualified for the role.

What used to be a scarcity problem – not enough candidates – is increasingly becoming a signal-to-noise problem.

For HR teams, this means:

More CVs to review
Less differentiation between applicants
More time spent verifying authenticity

In short, volume has increased, but informational value has decreased.

At the same time, the talent shortage has not disappeared.

Paradoxically, the surge in applications does not mean that organisations suddenly have access to more qualified talent.

Across industries, companies still report significant skill shortages, particularly for leadership roles, technical expertise, and complex knowledge work.

Structural factors driving this scarcity include:

demographic shifts and shrinking workforces in many developed economies,
rapidly evolving skill requirements,
growing demand for digital and AI-related capabilities.

In emerging fields, demand is increasing faster than the supply of talent. For example, research on labour market trends shows that demand for AI-related roles has grown significantly in recent years, while qualified professionals remain scarce.

The result is dual pressure on recruitment teams:

Too many applications to process
Too few truly qualified candidates

This paradox creates a hidden operational risk.

The real business risk: expensive hiring mistakes

When signal quality declines, hiring decisions become more uncertain, increasing the likelihood of costly mis-hires. The financial consequences are substantial:

Mis-hires can incur six-figure indirect costs when considering salary, lost productivity, team disruption, and replacement hiring.
Poor leadership hires can destabilise projects, increase turnover, and damage client relationships.

In environments where candidates appear equally qualified on paper – a situation increasingly common with AI-generated applications – decision-making often shifts towards subjective impressions.

This is where the real risk arises.

Because, the more similar candidates seem at the application stage, the more decision quality relies on the depth and structure of the evaluation process.

Why traditional screening methods are reaching their limits

Traditionally, recruiters have relied on three main signals:

CV experience
Educational background
Interview impressions

However, generative AI has undermined the reliability of the first two. AI-generated CVs can present highly polished narratives, making candidates appear more qualified than their actual experience indicates. Recruiters are increasingly faced with applications that look impressive but reveal little about genuine behavioral patterns or long-term performance potential.

At the same time, interviews alone are often insufficient to resolve this ambiguity.

Interview outcomes can vary widely depending on:

the interviewer,
the questions asked,
unconscious bias,
and the candidate’s level of preparation.

Without structured decision frameworks, hiring panels frequently end up debating perceptions rather than evidence.

A new standard: moving from Screening to Prediction

To navigate this new environment, many organisations are shifting their hiring strategy from simple screening to predictive evaluation.

Instead of asking only, “Does this candidate look qualified?” the more relevant question becomes, “Based on reliable signals, how likely is this candidate to succeed in this role?”

This is where predictive assessment and structured diagnostics are gaining importance. Research shows that AI-supported recruitment tools can improve hiring accuracy by up to 40% and increase candidate matching quality by 67% when used appropriately within the decision process.

However, technology alone is not the solution.

The real value emerges when predictive insights are integrated into a structured decision logic that hiring managers can use consistently.

Turning data into better hiring decisions

Forward-looking organisations are therefore introducing an additional layer of diagnostic insight into their hiring process. Platforms such as Zortify take this approach by combining validated psychometric measurement with AI-supported analysis of open-text responses.

Rather than replacing interviews, this creates a structured evaluation framework that provides decision-makers with insights into:

personality structure
tresilience and entrepreneurial mindset
cultural alignment
potential counterproductive tendencies

These insights serve as evidence-based signals that help hiring managers move beyond CV narratives and interview impressions.

The goal is not to automate decisions, but to enable better-informed decisions.

When predictive diagnostics are integrated early in the process, interviews become more focused, comparisons between candidates become more transparent, and discussions within hiring panels shift from opinion to evidence.

The future of recruiting: clarity in a noisy market

The recruiting landscape will likely remain complex.

Generative AI will continue to increase application volume, while demographic and economic factors will sustain pressure on talent supply. This presents HR leaders with a structural challenge: how to identify real potential in a market full of polished but increasingly similar applications.

Organisations that succeed will not necessarily be those with the fastest hiring processes, but those with the clearest decision standards – standards that allow hiring managers to distinguish between candidates who look strong on paper and those most likely to perform and grow in the role.

In an environment where both candidates and companies use AI, the competitive advantage will belong to organisations that combine human judgement with reliable predictive insights.

In the end, the real goal of recruiting has not changed: not hiring faster, but hiring right.

Prof. Dr. Florian Feltes

Prof. Dr. Florian Feltes is co-founder and co-CEO of zortify and a forerunner in AI-supported HR innovation. Together with his team, he develops intelligent personality diagnostics and helps companies identify the perfect candidates—without expensive assessments and without bias. His vision: a world in which every company can effortlessly form high-performance teams and create work environments that allow human potential to flourish.

Budget killer bad hires

Around 14% of all new hires fail. With 50 hires per year, this can quickly add up to over $300,000 in direct costs. And that’s just the tip of the iceberg. The real damage become apparent later: declining team performance, lost innovation, and a domino effect that drives top performers to competition. Bad hires are like a slow leak in the system.

2025 Check-Out with Marcus & Florian, CEOs of Zortify

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How AI in recruiting went from being a mere tool to a driver of the future. February 2025. While some companies are frantically reviewing their AI-supported recruiting processes to meet the EU AI Act requirements at the last minute, others are remaining calm. They have used the previous months not only to ….

Budget killer bad hires:

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The domino effect: from the wrong hire to crisis

The obvious costs are clear: recruiting expenses, onboarding, months of salary, and a new hiring process. Studies estimate the replacement costs – depending on the role and seniority – at 50% to four times the annual salary.

But even these figures fall short. By the time a bad hire becomes apparent, six to twelve months have often already passed. During this time, the team has to compensate, for example by working longer hours and putting in more effort to coordinate. stress level rise, quality declines, and the risk of exhaustion and burnout increase structurally. The important thing to note is that the damage rarely affects just one person. Bad hires can trigger chain reactions that destabilize entire departments. At this point, at the latest, it becomes clear that is not a “soft” cultural issue, but rather the measurable business of hiring decisions.

Skills-first meets personality-first

Many organizations still focus too much on applicants’ professional qualifications and too little on what really make people successful in everyday life: ownership, integrity, resilience, learning ability, and collaboration. Oxford Economics sums it up succinctly: Managers are hired for their professional qualifications – and fired for their personality.
The solution is not either/or, but a smart combination:

Skills-based to ensure the professional foundation,
Personality-based to increase the likelihood of sustainable performance.

Important: It’s not about testing more, but about making better decisions. The key lies in the predictability of performance.

From gut feeling to measurable results

This is where this AI-supported diagnostics come in: they can significantly increase the quality of professional performance forecasts. Companies report 31% faster hiring times with AI tools, 50% better quality of hire, and with proper implementation – 50-61% less unconscious bias.

How personality-based recruiting works:

NLP instead of multiple choice: AI analyzes open-ended text responses. The results are less socially desirable and closer to actual thinking and communication logic.
Big 5 Model: Scientifically validated, the model measures business-critical characteristics like openness, conscientiousness, extraversion, agreeableness, and emotional stability.
Entrepreneurial Capital: Analysis of urgently needed characteristics
in a rapidly changing environment, such as resilience, self-efficacy, optimism, and agility mindset.
Counterproductive Behavior Tendencies: Measurement of risk factors like
strategic manipulative beahvior and impulsivity.
Cultural fit: Comparison of personality with the values and working methods of the company

Instead of “it’ll do,” organizations receive data-based probabilities of success, enabling them to plan to plan team capacities and implement projects more reliably. The KPIs are clear: shorter time-to-fill, higher offer acceptance rate, better quality of hire, higher retention rate, and shorter time-to-productivity. The latter in particular is often underestimated: suitable employees become productive more quickly.

HR: From process owner to decision-making authority

When recruiting is understood as strategic value creation, the role of HR automatically changes. HR is then no longer the authority that coordinates appointements and guides process steps, but rather the function that sets the decision-making standard. This means that HR is not only responsible for ensuring that hiring takes place, but also that it is of high quality, i.e., transparent and business-relevant. HR ensures governance, fairness, and compliance without losing speed.

More stability, less rework, more leadership capacity

In the long term, this creates a system that not only fills positions faster, but also better. Fewer miscasts mean less friction, more stable teams, and more leadership capacity for value creation instead of damage control.

Companies that modernize their decision-making standards in 2026 will gain an advantage in a working world where adaptability, responsibility, and cultural fit determine competitiveness.

See the use cases

Download the whitepaper “The business impact of fast and precise recruiting decisions”

Prof. Dr. Florian Feltes

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2025 Check-Out with Marcus & Florian, CEOs of Zortify

Bias out. ROI in: The unexpected win of AI regulation

Looking back on 2025: How AI in recruiting went from being a mere tool to a driver of the future

February 2025. While some companies are frantically reviewing their AI-supported recruiting processes to meet the EU AI Act requirements at the last minute, others are remaining calm. They have used the previous months not only to ensure compliance, but also to fundamentally rethink how technology should be used in recruiting.

Twelve months later, it will become clear that these different approaches have divided the industry into those who truly understand AI in recruiting and those who primarily view it as a technical and/or compliance challenge.

2025: The year of coming of age

The beginning of the year was marked by uncertainty: How do we meet the AI Act requirements? Will we have to shut down our systems? How expensive will that be? But over the course of the year, the most successful companies realized that regulation was not the problem, but rather the solution to the real problem: non-transparent, unfair recruiting processes that overlook potential and reproduce bias.

The pioneers of the HR industry saw the requirements of the EU AI Act as an opportunity. They have not only made their recruiting technology compliant, but also fundamentally improved it. Saint Sass, for example, one of Europe’s most sought-after fashion labels, switched to data-based personality analysis during a critical growth phase and was able to fill five key positions in just four weeks, 50% faster than the startup average, with 50% fewer interviews. Or the CHAPTERS Group, an investment company that specifically builds start-up teams. It uses AI-supported assessments to identify who really brings entrepreneurial thinking and action to the table – and achieved a 25% higher interview-to-hire ratio in 2025.

What these success stories have in common is that they have understood that transparency, traceability, and human oversight are not bureaucratic hurdles, but quality characteristics of excellent recruiting.

The learning curve of the HR industry

2025 was also a year of painful lessons. Companies that had implemented AI tools hastily and without careful consideration had experiences they would rather have avoided: algorithms that reinforced existing biases instead of minimizing them. Black box systems that no one in the company really understood. Candidates who felt devalued by non-transparent processes and withdrew their applications.

The industry has learned that not all AI is the same. The crucial difference lies in the underlying technology and philosophy. While CV parsing systems enable historical patterns and, as a result, often discrimination, language-based personality analysis takes a different approach. It does not evaluate which universities are listed on a resume or which company names are impressive, but rather how people communicate, think, and solve problems. It reveals what traditional methods overlook: intrinsic motivation, teamwork skills, willingness to learn, and entrepreneurial thinking.

The ROI of ethical AI

Responsible AI is not only ethically imperative, it also makes good business sense. By 2025, more and more companies will have recognized this. In times when a bad hire can quickly cost six-figure sums and a shortage of skilled workers is becoming a limiting factor for growth, quality of hire is the decisive metric.

Companies that invested in transparent, explainable AI systems in 2025 report measurable improvements: shorter time-to-hire with higher accuracy, lower turnover in the first twelve months, and more diverse teams without pressure to meet quotas. The ROI comes not from maximum automation, but from better decisions.

The companies that understand this are likely to win the war for talent. Not despite the AI Act requirements, but because of them.

Outlook for 2026: Mastering technology and human understanding

What does this mean for 2026? The fundamental question of “Do we use AI in recruiting?” has been answered. The focus is shifting to “How good is our AI?” Quality standards for recruiting technology will become established, not only through regulation, but also through measurable business impact.

2026 will be the year when AI becomes invisible in recruiting, but in the best sense. It will become as commonplace as email, but its quality will be the decisive differentiating factor. The leading companies will be those that think beyond compliance and use AI strategically: for potential prediction instead of CV screening, for team fit analyses instead of isolated individual assessments, for holistic people analytics instead of stand-alone solutions.

Recruiters will continue to evolve into curating decision-makers who combine data-driven insights with human judgment. Empathy, contextual understanding, and strategic thinking will become more important, not less. The best HR departments will be those that master technology and people skills to an equal degree.

The foundation has been laid

2025 was the year that set the course. Companies that are now on the right side of this development – with transparent, fair, effective AI – have a competitive advantage that will be decisive in 2026. While they are already working on the next generation of recruiting innovation, others are still struggling with the basics.

The year AI came of age in recruiting is also the year recruiting became more human. This apparent paradox is the real innovation: technology at the service of better human decisions.

What a wonderful prospect for the coming year.

Prof. Dr. Florian Feltes

1 in 700 is not enough! – How companies really hire based on skills

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