Applying for a job can be a soul-destroying process. For the past few years, the world’s biggest firms have been using AI recruitment software to filter job applications and streamline the process. Existing applicant tracking systems (ATS) typically scan applications for keywords that the employer has selected. Any CVs that don’t fit the bill are instantly rejected; those that pass are stored and indexed for a human recruiter to look through.

If you are job-hunting you should assume that your application will be screened by an algorithm, and maybe rejected, before a human ever looks at it. But such systems are hardly foolproof. They don’t just frustrate people looking for jobs with non-traditional CVs – they are also far from ideal for employers, because the computer is literal and doesn’t do judgement. For example, excessively rigorous screening can mean relevant sections of a CV will be passed over if they contain words and phrases only slightly different from the employer’s preselected ones.

Moves are afoot to make the recruitment algorithms more sophisticated. The biggest name in online recruitment is LinkedIn, which can now sift millions of LinkedIn users for matching talents, then display a list of candidates at other companies with like-for-like skills and experience. Employers will be able to narrow down huge lists of existing contacts to find those with the skills appropriate for a recently advertised job.

The app will also indicate how often a prospective candidate has interacted with the company’s posts on LinkedIn and show whether he or she is genuinely looking for a new job. A “something new” button on LinkedIn profiles can tell employers – except, of course, their own – that they have itchy feet.

Connectifier, another piece of software crawls a range of websites and candidates’ profiles, as well as CVs, to build up a picture of skills and expertise down to the finest detail – things that a recruiter might have a hard time taking into account, for instance, does someone have a lot of friends in a certain location or at a certain company, or harvest data from a site like GitHub, where programmers share and discuss code.

Then there is Reveal, a small Danish start-up touting “a machine learning engine for your recruitment”. The software has been trained on professional vocabulary relating to job descriptions and is able to analyse databases of CVs, looking for candidates who might fit a post. The firm’s algorithm uses statistical models that look at the distribution of words. It understands that “software engineer” or “software developer” are very similar roles, for example. It can detect patterns, and help companies with large numbers of CVs on file to make the most of that data and identify good candidates as soon as new roles come up. The system can even predict how interested a candidate might be in a job change from their current position. This is done by assessing how many previous jobs candidates have listed on their CV and noting how frequently they have moved from role to role.

The fact is, however, that when you put rubbish in, what you will get out is – rubbish. Companies solicit far too many applications, wasting the time both of applicants and staff, and probably missing a talented person who may not be, say, a softwate super-star, but can think laterally and use his or her intelligence and personality to good effect. Potentially good employees are being overlooked as companies do a second-rate job at deciding what they really want – a collegial hard worker with a brain – relying on key words that leave out a host of other useful attributes of the good employee. Personality counts.You are looking for like souls with whom you will be spending a great deal of time. Automation has a place in recruitment, but has to be used intelligently.