Employers Say Human Skills Matter Most. Can Your Hiring Process See Them?

Employers often say they want people who can communicate clearly, solve problems, work well with others and adapt when circumstances change. It is hardly surprising. Technical knowledge may enable someone to perform a task, but most jobs also require judgement, collaboration and the ability to respond when the answer is not obvious.

These capabilities should become more valuable as technology takes on more routine work. New research from City & Guilds suggests employers recognise this. Nearly three-quarters of the HR professionals and line managers surveyed agreed that employability skills are now more important than technical skills for success within their organisations.

The same research revealed a potential problem. More than two in five surveyed employers were using AI to screen or filter applicants, while 69% believed AI screening prioritised technical skills over softer, human capabilities. That finding does not independently prove that AI systems are filtering out good candidates, but it does reveal a significant concern among the people responsible for hiring them.

Employers may know what matters while still operating recruitment processes that struggle to recognise it.

Are You Measuring What Matters or What Is Easiest?

Recruitment has always relied on signals. Qualifications suggest knowledge, job titles suggest responsibility, years of experience suggest familiarity and keywords suggest relevance. These signals are easy to capture and compare, which makes them particularly attractive when an employer has hundreds of applications to process.

The difficulty is that what is easiest to measure is not necessarily what matters most. A familiar job title does not tell you how someone responds to uncertainty. A qualification does not tell you whether they can explain a difficult subject to a frustrated customer. A keyword does not demonstrate judgement, curiosity or the ability to build trust with colleagues.

This does not make conventional signals worthless. Technical knowledge and relevant experience still matter in many roles. The problem arises when convenient signals become substitutes for a fuller understanding of what success requires. If a role depends on communication, resilience and collaboration, but the first stage of recruitment examines only qualifications, job titles and technical terminology, the process is poorly aligned with the employer’s stated priorities.

AI can make that process faster and more consistent, but it cannot correct an unclear definition of a strong candidate. If you ask a system to identify familiar experience and technical matches, it may do exactly that. The problem is not necessarily the technology. It may be the question the employer has asked it to answer.

Human Capabilities Need Evidence

Candidates contribute to the problem when they describe themselves as an “excellent communicator”, a “natural leader” or a “strong team player”. These claims are easy to make and difficult to evaluate. When nearly every application contains similar language, employers learn to ignore it.

The answer is not to search for more polished claims. It is to ask for better evidence. Instead of asking whether someone is adaptable, explore a situation in which their priorities changed unexpectedly and how they responded. If communication matters, ask for an example of when they helped someone understand a difficult idea. If collaboration is important, examine what they contributed to a shared outcome and how they handled disagreement.

That evidence may come from conventional employment, but it may also come from voluntary work, caring responsibilities, community activity, education or personal projects. A rigid process can miss valuable capabilities because they were developed somewhere other than the workplace or expressed in language the system was not expecting.

This is particularly important when assessing people whose career paths are less conventional. If employers only recognise evidence presented through familiar job titles and employers, they risk confusing a conventional history with future capability. The purpose of selection should be to understand what someone is likely to contribute, not simply to reward the candidate whose past is easiest to categorise.

Technology Should Improve Judgement

The case for using technology in recruitment is understandable. Employers need to manage application volumes, identify potentially relevant people and avoid wasting the time of candidates and hiring managers. AI can support that work by organising information, identifying possible connections and reducing repetitive administration.

However, efficiency is valuable only when the underlying decision is sound. Processing familiar signals more quickly does not necessarily produce better hiring. It may simply reinforce the assumptions already built into the process.

A better approach begins by defining the opportunity more carefully. What problem does this person need to solve? What contribution will make the greatest difference? Which technical capabilities are genuinely essential, and which can be developed? What human capabilities will determine whether someone succeeds in the organisation?

Technology can then help look for relevant evidence and connections. Its role should be to support better judgement, not to replace judgement or hide the reasoning behind a decision. A useful hiring system should help employers understand why a person may be suitable, what evidence supports that conclusion and what still needs to be explored through conversation.

Test the Process Against Your Priorities

There is a straightforward test employers can apply: does your hiring process allow candidates to demonstrate the qualities you say you value?

If communication matters, where in the process can candidates demonstrate it? If adaptability is essential, what evidence are you inviting them to provide? If collaboration is important, how do you explore the way they work with others? If judgement matters, do you give them a meaningful opportunity to exercise it?

When these qualities appear in company values and job adverts but remain absent from selection, they are aspirations rather than hiring criteria.

The City & Guilds research does not provide a verdict on every AI recruitment system. It does, however, highlight a mismatch worth investigating. Employers report placing increasing value on human capabilities while many also believe that AI screening gives greater weight to technical skills.

That should prompt employers to examine both the technology they use and the instructions, criteria and assumptions surrounding it. The objective is not to choose between technology and human judgement. It is to design a process in which technology helps people make better-informed decisions.

Before making recruitment faster, make sure it is looking for the right things.