1 in 700 is not enough!

– How companies really hire based on skills

Three-quarters of companies have announced in recent years that they will evaluate candidates more on the basis of their actual skills and competencies rather than formal qualifications. However, the Harvard study “Skills-Based Hiring: The Long Road from Pronouncements to Practice” shows a clear discrepancy between promises and reality: of all new hires in 2023, less than one in 700 was the result of having degree requirements removed.

This figure is not a setback, but a reflection: it highlights how much potential remains untapped – and how much growth is possible when companies consistently focus on personality and skills.

Research by Harvard Business School and the Burning Glass Institute also impressively shows where this journey can lead. Companies that have already made the transition — the so-called “leaders,” around three percent of the market — are hiring 18% more people without academic degrees in roles that previously required one. Above all, they achieve significantly higher quality hires and higher retention rates. Joseph B. Fuller (HBS) emphasizes that simply changing job ads is not enough. It requires a rethink among hiring managers, in corporate culture, and in hiring processes. The key insight: transformation does not come from better intentions, but from better systems.

Analyze personality early in the process

The first step toward recruiting that reveals a person’s true potential is to use technology-based personality diagnostics early in the process. Whether before, during, or after CV screening, the goal is to identify mindsets, cognitive abilities, character traits, and associated specific skills and development opportunities based on data before human selection decisions are made.

A multi-measure approach that combines several dimensions is particularly effective. 92% of companies that take this approach report higher satisfaction with their hires. This is a clear indication of what is possible when personality is treated not as a soft factor but as a strategic variable.

Understanding outcomes instead of biographies

This change can only succeed if there is a prior definition of what success in a role actually means. Many companies underestimate this phase. The “leader” companies from the study mentioned at the beginning take a different approach: they don’t ask what candidates need to bring to the table, but what they should achieve in the role. This shifts the focus from formal requirements to outcomes. Key questions include:

What thought patterns support long-term performance?
What personality traits promote team dynamics?
What cognitive abilities enable rapid learning?

Those who answer these questions in a structured manner will be able to identify talent that traditional processes would never have captured.

Ensure fairness and objectivity in recruiting

Even with clear success criteria, human bias remains an obstacle. We unconsciously favor people who are similar to us. When companies remove academic filters but do not set objective criteria, bias can actually increase.

Leader companies counter this with consistent structures, including blind recruitment and standardized evaluation grids.

84% of UK companies openly admit that unconscious bias has an effect. But the difference arises when this insight is translated into systems that reveal potential rather than reinforcing subjectivity.

Scaling personality measurement with AI

AI models trained on resumes inevitably reproduce patterns based on the past. Tools based on personality and performance data, on the other hand, open up completely new possibilities. Natural language processing, which analyzes open-ended responses, recognizes patterns of thinking and communication at a depth that traditional assessments cannot capture. For companies with high application volumes – especially in IT, consulting, and financial services – this creates a scalable, objective, and fair way to reveal personality and potential without compromising quality. AI thus becomes not a decision-maker, but an enabler of better human decisions.

Closing the 1-in-700 gap

Implementing this kind of personality- and competency-based talent logic happens in several steps:

Recruiting funnel analyses show where the 1-in-700 gap occurs
Pilot testing for individual roles with assessments and structured interviews
Performance evaluation after 6, 12, and 18 months provides reliable data
Results instead of opinions reduce skepticism about new processes
Validated assessments, trained recruiters, KPIs, and systems ensure sustainable hiring success

Measurable talent advantage

Non-degree hires in roles that previously required a degree achieve on average ten percentage points higher retention and salary increases of 25%. They show above-average motivation because they are given opportunities that would traditionally have been denied to them. For companies, this means better matches, more stable teams, and access to talent that the competition will continue to overlook.

Companies that understand personality as an economic value, recognize potential as a currency for the future, and design their recruiting processes so that impact, rather than biography, is the deciding factor, are very likely to come out ahead in the competition for the best talent very soon. The most successful three percent have shown how it’s done. Now companies must decide whether they want to be among the next 30% who will follow.

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.

Predicting retention: How companies can avoid costly employee turnover

In many organizations, the actual costs of turnover are overlooked in recruitment strategies. While enormous resources are invested in job profiles, hiring funnels, and assessment processes, one crucial question remains unanswered: Will the person hired today still be with the company in 18 months?

Who’s behind that perfect prompt?

How talent stands out and companies recognize who really fits. The application sounds flawless. Every word is perfect. Every phrase hits the right note. And that’s exactly the problem. Since ChatGPT & Co. have reached the application process, documents are becoming increasingly similar.

The recruiting advantage: Why quick decisions bring in better talent

Speed in recruiting is not an operational detail, but a strategic competitive factor. Companies that take too long to make decisions systematically lose the best candidates to more agile competitors. Studies also show that longer processes do not result in better hires. On the contrary.

Current data shows an average misplacement rate of 14%, and as high as 30% in junior programs. With 50 hires per year, this already results in losses well into the six-figure range. Much of this cost could be avoided if recruiting took into account not only the probability of success, but also the probability of termination.

The true cost of employee turnover

The well-known cost factors, such as recruiting expenses, onboarding, and training, are only the tip of the iceberg. In fact, depending on the role, hiring the wrong person can cost between €45,000 and €100,000, and in the case of executives, up to €300,000. The direct costs of searching for and training new employees alone add up to as much as €60,000.

Added to this are hidden costs such as:

Loss of productivity,
demotivated teams,
loss of expertise, and
damage to reputation.

Companies have it in their own hands to avoid these. This is because employees often resign not because of the job itself, but because of a mismatch between expectations and reality – patterns that would have been visible during the recruitment process.

The unsurprising exit

Current data shows that early turnover is rarely a coincidence. 14% of employees leave because the tasks do not meet their expectations, and 17% leave due to a cultural mismatch. The SHRM Global Culture Report confirms that 64% of employees who perceive their corporate culture negatively actively look for another job.

The common denominator: a mismatch between expectations, culture, and personality. These risks are predictable, however, not through intuition, but through data-based personality analysis.

Recognizing invisible red flags

Truly costly mistakes are rarely caused by obvious warning signs, but rather by personality patterns that remain invisible during interviews. AI-based methods based on the Big Five and the Entrepreneurial Capital developed by Zortify make these risks measurable.

Among other things, the Big Five show:

how stable someone remains under pressure (emotional stability and frustration tolerance),
whether the need for autonomy and organizational structure are compatible, and
how well people cope with change, ambiguity, and high complexity.

Entrepreneurial Capital adds to this analysis with future-critical skills like resilience, self-efficacy, agility, and solution orientation – the very factors that determine long-term performance and retention.

In combination, the Big Five and Entrepreneurial Capital reveal the invisible red flags that trigger early attrition: personality and context mismatches, fragile stress profiles, and unrealistic expectations. AI provides a solid basis for decision-making where gut feeling fails.

Predicting retention with AI

Modern NLP-based methods analyze how people think, prioritize, and deal with uncertainty. This results in a reliable prediction for long-term retention.

Companies that use such data can reduce their misplacement rate by 30%. Often, avoiding a single misplacement pays for the entire technology investment.

Typical green flags in applicants include:

A realistic self-assessment,
Value congruence,
A conscious decision to take on the specific role, and
The ability to communicate needs and boundaries.

At the same time, companies benefit from presenting everyday work life in a blunt manner. The best talents prefer realistic descriptions. Surprises on the first day, by contrast, undermine trust and drive turnover.

The business case: Data-driven decisions save a lot of money

For a company with 50 hires, the picture is as follows:

Mismatch rate without data-driven methods: 14%, corresponding to 7 mismatches
Costs: 7 × 45,000 = 315,000 €
With personality analysis: Mismatch rate 10%, corresponding to 5 mismatches
Costs: 5 × 45,000 = 225,000 €
Savings: 90,000 € per year with a technology investment of around 35,000 €
ROI: 2.6x, payback in less than 5 months

In addition to the financial effects, companies benefit from more stable teams, greater knowledge retention, better performance, and employer branding that signals long-term loyalty.

What companies can do straight away

Short term: Analyze past cases of employee turnover: What signs were already visible during the recruitment process?
Medium term: Track retention by hiring channel and personality profile, measure cultural fit and personalize onboarding based on individual profiles.
Strategically: Early implementation of legally compliant, transparent AI systems to minimize bias and hiring risks, especially in light of the EU AI Act.

Using AI to tackle talent shortages

The most costly mistakes in recruiting are not the candidates who drop out of the process, but those who are hired and resign a few months later. Bad hires are not an isolated HR issue, but a strategic business risk in the six-figure range.

Personality-based assessments have thus become a business-critical tool. Organizations that can predict turnover make better decisions – and win in a market where talent is the scarcest resource.

Prof. Dr. Florian Feltes

Who’s behind that perfect prompt?

Predictive hiring: How HR can identify employee engagement during the interview

And why brilliant resumes often lead to expensive hiring mistakes The resume shines, the interview goes smoothly, the references seem flawless. But six months later, it becomes clear that although the new colleague is highly qualified, they have long since quit quietly.

The recruiting advantage: Why quick decisions bring in better talent

Who’s behind that perfect prompt?

How to Hire for Authenticity In Times of GPT Applications

The application sounds flawless. Every word is perfect. Every phrase hits the right note. And that’s exactly the problem. Since ChatGPT & Co. have reached the application process, documents are becoming increasingly similar. Studies show that around half of all applicants already use AI tools to help them. Among students, the figure is as high as 57%.

What began as the democratization of good applications is developing into a new challenge: if everyone’s wording is perfect, how can I still stand out as an applicant? And how can companies recognize who really suits them?

From perfection to uniformity

Resumes are smoothed out and cover letters are optimized, often losing their personal touch in the process. For companies, this means that traditional application documents say less and less about personality, motivation, or potential.

At the same time, applicants are faced with the question of how they can show who they really are when AI helps them do everything “right”?

Because one thing is becoming increasingly important: authenticity. Especially in a job market that is once again becoming more of an employer’s market in many industries, it’s no longer just perfect documents that count. What is needed are people who want to learn, who can adapt, and who deal with their strengths and weaknesses in a reflective manner.

Personality is not expressed in clichés

Psycholinguistic research – such as that conducted by James Pennebaker – shows that our language patterns are as individual as fingerprints. It is not what we say, but how we phrase things that reveals a lot about our way of thinking, decision-making logic, and values. That is why modern recruitment processes are no longer about delivering the perfect cover letter, but about showing how you think, act, and reflect.

Open, situation-specific questions – for example, about real experiences, difficult decisions, or learning moments – create this space for authenticity. This is where human substance outstands the smooth surface of AI. A machine may be able to write convincingly, but it has no real attitude, no conscience, no learning curve.

Smart selection – a win for both sides

Technology can help to reveal these genuine signals – for example, through linguistic pattern recognition and NLP-based analyses. It identifies the essential personality factors for a role, creates an objective basis for evaluation, and facilitates the final assessment by an experienced recruiter.

Old: CV screening – interview – assessment – hiring decision

New: Initial pre-selection – AI assessment – In-depth interview – Data-informed hiring decision

For companies, this means spending less time on superficial CV and oh-so-smooth motivation letter screenings and focusing more on what really matters: potential, learning ability, and cultural fit.

Gen Z, on the other hand, is generally open to such technology-based assessments if they are perceived as fair. At the same time, applicants who come across as honest, reflective, and authentic have a much better chance of standing out from the crowd in a personality-oriented selection process.

Authenticity leads to the perfect fit

Bottom line: The best talents are not those who shine with the most flawless applications, but those who show who they are, what they want to learn, and how they deal with challenges.

When AI standardizes applications, a new level of differentiation emerges, especially in working with Gen Z: authenticity. Applicants can use this level to their advantage. Companies, in turn, have state-of-the-art AI technology at their fingertips to identify the qualities they value most in candidates. In the end, both sides benefit from a perfect match between role and person.

Personality plays the key role in this. And regardless of perfect prompts, it remains unmistakable at its core.

Prof. Dr. Florian Feltes

Predictive hiring: How HR can identify employee engagement during the interview

The recruiting advantage: Why quick decisions bring in better talent

EU AI Act: How smart companies are using regulations to outperform competitors

What HR Must Do Now. The EU AI Act has been in force since August 2024, and further compliance deadlines passed in August of this year. For companies that use AI tools in recruiting and human resources development, this means that the legal gray area is over.