More and more employers want people with soft skills, and technology is helping us find them more accurately than ever before.
Artificial intelligence, data analytics, and specialist digital tools are all working together to make candidate evaluations more fair, faster, and easier to scale than ever before.
I have seen how these technologies have changed the way we evaluate soft skills from a process based on intuition to one based on data that helps, not replaces, human judgement.
When I talk about soft skills, I’m not talking about technical talents but personal traits. These traits affect how I talk to clients and coworkers. They are usually transferable and focus on people. They include things like communication, teamwork, problem solving, adaptability, and emotional intelligence.
Stanford Research Centre says that 85% of job success comes from having good soft skills and people skills.
A recent study of 500 UK employers also found that 67% of recruiters care more about soft skills than education when hiring.
This poses a very crucial question. How can employers find these kinds of hard-to-define qualities? This is where AI and other new technologies are making progress.
Here is a more in-depth look at the problem of measuring soft skills and how smart tools are making it easier to get better soft skill insights.
Analytics Based on Data
One of the biggest changes i’ve seen in the wake of the AI boom is this technology’s ability to turn unstructured data into useful information.
For recruiters hoping to better understand soft skills, AI algorithms can look at data from interviews and tests to deliver targeted insights into the abilities of each candidate.
This helps provide more detailed and personalised assessments of candidates’ skills for specific jobs.
Better still, this doesn’t just let you quickly look through bigger groups of candidates. It also makes sure that all applicants are judged fairly using the same standards, which helps cut down on bias and mistakes in the data.
Did you know that AI can also use data from different candidate touchpoints to make soft skill interpretations?
These insights can then be sent back to HR professionals through dedicated cloud-based platforms like PeopleHR Evo. This helps HR professionals get a better picture of candidates’ adaptability and how well they fit into the company’s culture.
This method of measuring soft skills helps companies find the candidates who are most likely to do well in their work environment.
This helps foster a more collaborative work environment, where people can work together more easily, and improves the onboarding process.
Analysing Video Interviews
Artificial intelligence can make video interviews much more useful by giving you more information about a candidate’s soft abilities.
Natural language processing (NLP) is a big part of this. NLP looks at both spoken and written answers to see how clear, coherent, and emotionally charged they are, as well as how well they use inclusive language.
Companies like Unilever have already deployed this technology to analyse video interviews, using natural language processing to identify word patterns that correlate with high-performing candidates.
AI systems can also use voice recognition to look at speech patterns and facial expressions. These signs can show trust, excitement, and honesty.
Some video interview platforms offer these features along with tests of conduct and personality. Gamified assignments allow you test skills like problem-solving, resilience, and teamwork in a way that is more consistent and fair.
Recognising Patterns
Machine learning is another great method for analysing soft skills. As a part of AI, it offers another level of understanding by finding patterns based on past performance and long-term success.
This means building a data-driven model that shows the soft skills and personality qualities of the best workers. Machine learning can use predictive analysis to look at new candidates by collecting performance data and finding patterns that are always the same.
One example of this is Pymetrics. It uses machine learning to quantify cognitive and emotional qualities. This lets behavioural data affect hiring decisions while also reducing the impact of unconscious prejudice.
Accuracy in Soft Skill Analysis
Technology is altering the way companies hire people rapidly and makes the process much more efficient while also helping to decrease unconscious prejudice.
Natural language processing and machine learning are two examples of AI tools that will continue to be very important in the development of soft skill assessments.
But I think these technologies operate best when they help people think instead of replacing it.
People will probably have more of a supervisory role in the future. This will mean looking at numbers, figuring out what AI-generated insights mean, and aligning the strengths of candidates with the needs of each job.
AI will help companies hire people who fit in better with their culture, which will lead to increased employee engagement and lower attrition as hiring practices continue to change. This will make everyone happier and more productive at work.
