Building such complex test environments is not simple. It requires resources (data center stuff) and human knowledge. Somebody has to conceptualise this new environment and then somehow build it. Next comes the critical factor: who has got the kind of money to buy this? It cannot be something the local plumber can afford when hiring a new apprentice.
companies.
If a company has built a set of test and situations that can cover an industry, we can see some sort of economy of scale. The price for the service may be within reach of middle-sized
The other end of the scale is where more specialized assessments are build – bespoke development – because the client is big enough to afford it and their situation is unique. How many of those do we find? Probably not a lot either. The next bigger thing is ‘subject matter expertise’. In a lot of instances we might see that trained HR professionals evaluating the test tools and the test results,
based on years of expertise in grading people. Does that still apply here? That is another unknown. I found a company that has been around for some years: SHL. Although this is not a ‘product-punt’ it is worth looking at as an example: SHL specializes in HR and talent management, with more than 45 years of talent expertise. Its solutions support talent acquisition, employee assessment, leadership development, and workforce management. SHL does not only use AI. Its portfolio includes science-backed cognitive, personality, behavioral, skills, simulation, video interview, and assessment-center tools. SHL also combines technology with HR experts, occupational psychologists, and implementation advisers. SHL provides software platforms and pre-configured talent solutions that help organizations assess and select candidates. These combine assessments, interviews, skills data, analytics, and reporting to support more objective hiring decisions. However, the software supports human decision-making—it does not replace the employer’s final decision. I used the SHL chatbot to get these answers. They are a bit ‘stock-standard’ but good enough to make one major conclusion: SHL sees themselves as one of the leaders because they can use their superior HR knowledge and expertise and combine it into workable new technology platforms.
Are there others out there? Hirevue might be a new company in this field, having been around for some 22 years. Wiki: By 2012, Hirevue’s clients included Nike, Starbucks, Walmart, Geico, and Hasbro, among others. In 2012, Forbes staff writer Susan Adams noted that “the typical Hirevue interview lasts 12 minutes and has just four questions, with about three minutes to answer each question. So what is the edge? Wiki: “In 2013, the company began using artificial intelligence to screen interviewees. Both proprietary voice recognition software and licensed facial recognition software are used by Hirevue’s Insights program. The company’s Hirevue Insights program scans applicants’ verbal and facial gestures and cues, matching them with skills employers seek in given roles. Aside from facial gestures, Hirevue also assesses an interviewees’ tone, vocabulary, and “candidate empathy”. All of
these analyzed data points are then used to generate an “employability” score for a candidate, which is ranked versus other applicants. Is this really different from having a person to go through stacks of responses? The interesting part is that the AI tool has no bias (that we know of). Race and background do not matter as our facial responses are rather similar (as far as I know). The fact is: it does help big corporate in a hiring process to get to quality people faster than anything. That at least is what their clients are telling us. Harver is another one. AI says: “Specifically engineered for high-volume recruitment (like airlines, hospitality, and retail). Harver tests rely on realistic job previews to score candidates on how they problem-solve real workplace bottlenecks under pressure. Harver says themselves: “Choosing between the quick hire or the right one? The age-old tradeoff: hire people quickly or find exactly the right fit. We say, why choose? Using Harver’s predictive assessments you can know, within minutes, which of your candidates is a great fit for your role so you can hire talent confidently. “Founded in 2015, Harver empowers talent teams to make better-informed hiring decisions using science and data. Rooted in over 35 years of rich data insights backed by industrial-organizational and cognitive science, Harver enables organizations to hire the right talent in a fast and fundamentally unbiased way. It is possible to use AI tools, but the human element is just as important. And there must be a cost to all of this?