It’s the year 2022 and Jill is graduating from university in a few months and is just starting to think about her career opportunities. She wasn’t considering your organization, however your artificial recruitment agent, Sam, notices that Jill is graduating soon and identifies her as a prospect.
Sam creates a preliminary competency profile for Jill by capturing and collecting publicly-available data about Jill from her ‘digital footprint’ – including her public profiles and social media posts – and judges that your organization might be a good fit for her. Sam then automatically verifies her qualifications and employment experience with external databases and determines that Jill is a good potential fit for a management trainee role.
Sam identifies from Jill’s online activity that the best time to contact her about the role is on Thursday between 6 and 8 p.m. Sam’s algorithms also judge that Jill’s preference is to receive a personalized message with a virtual reality-based Realistic Job Preview, tailored to outline exactly what her prospective job entails. Sam sends a message to Jill introducing himself, the organization and the role.
Jill goes through the Realistic Job Preview and replies to Sam to say she is very interested in the job. Sam automatically sends another message asking her to complete an assessment experience that is tailored to fill the gaps in Sam’s passively-created competency profile of Jill. This tailored assessment experience replaced your organization’s traditional psychometric tests, but captures the same information as the previous cognitive ability, personality and values-fit tests in a much shorter, visually-appealing, interactive, fair and job-relevant manner. This is no simple multiple-choice, or one-way video-based interview. Sam, via his digital avatar, is dynamically interacting with Jill using sophisticated language, facial recognition and other technology to tailor the assessment experience, on-the-fly, to Jill’s natural responses as the virtual reality-based simulation unfolds. Simultaneously, Jill gets a first-hand look at the context of the role and the everyday situations that she’ll be likely to experience at your organization. At the conclusion of the assessment, Sam provides Jill with some high-level feedback around the parts of the role and aspects of the organization’s culture that are likely to be a great fit for her, as well as information on areas that may be more challenging.
Jill’s complete profile is now compared against those of successful employees, potential team members, potential managers and currently available job opportunities within your business to determine if she is likely to be successful at your organization and, if so, in which role, team and department she will likely have the greatest positive impact. A match is found! And Jill is invited to schedule a live interview with the hiring manager. The tone, style and content of Sam’s automated messages are now tailored to reflect Jill’s personality, attitudes and interests, for maximum impact. For example, Sam detects that work-life balance is important to Jill, so the organization’s flexible work arrangements and generous leave policies are emphasized. Similarly, if Jill had been one of the less successful candidates, Sam would have sent a tailored, tactful and empathic rejection message outlining the reasons why she was unsuccessful, for example because she didn’t have the right fit, specific mix of skills or behaviors required, and offering feedback and suggestions that might help her with other employers in the future.
The questions for the competency-based interview, which Jill will have with her hiring manager, are automatically generated based on her competency profile and tailored to probe around any areas where more information or clarifications about Jill’s suitability are required. When Jill comes in for her interview, she is met by your recruiter, whose name is Sam. Jill remarks on how closely he resembles his avatar.
Fact or fiction?
This scenario is a realistic depiction of how recruitment could be managed in five years time. No longer will organizations have to hope that they are seen by the ‘right’ candidates; you’ll soon have your very own ‘Sams’ proactively identifying and sourcing individuals who are likely to fit the requirements of the role and the values of your organization. This will change the very nature of recruitment, and is already happening in more limited ways with employers automatically scanning the publically available ‘digital footprint’ of candidates for specific skill sets.
Realistic Job Previews will evolve to become more engaging, more immersive and more specific to each available role. Augmented and virtual reality will provide candidates with even more true-to-life experiences. We may have to wait beyond 2022 for the widespread application of virtual reality but it is undoubtedly coming. Voice and facial recognition for analyzing video interviews already exist. So too do ‘mobile-first’ assessments. Because these tend to be short, they have the advantage of ‘reduced drop-out’, which means that candidates are more likely to complete them.
With any assessments, it’s important to look closely at the underpinning science and the evidence that they will actually measure what they’re supposed to measure. Your assessments must be valid and job-relevant. That is, they should predict performance in the role. In addition, they should be free of any bias or adverse impact, to ensure they don’t disadvantage potential applicants or discriminate against any group. Importantly, they should ‘feel’ relevant and appropriate to candidates. Test providers call this ‘face validity’, which simply means that the test should ‘look like’ it measures what it’s supposed to measure. Valid and job relevant tests are also beneficial post-hire, where the data should be used to create personal onboarding and development plans for new recruits when they join.
But what about the artificial agent, Sam? How plausible or likely is that? Well, metaphorical ‘robots’ have been part of recruitment since the 1980s, when the need for experts to review psychometric test results was replaced by automated and easy-to-interpret reports based on a candidate’s responses. Today, intelligent personal assistants, such as Amazon Echo, Apple’s Siri, Google Now, IBM Watson and Microsoft’s Cortana already use artificial intelligence to deliver detailed, contextualized answers to users’ questions. The combination of machine learning and artificial intelligence will make recruiters’ lives easier in the future, as ‘robo-assistants’ will increasingly carry out mundane, transactional processes and repetitive, administrative tasks, such as identifying, pre-screening, sorting, matching, assessing and prioritizing candidates. Algorithms will be created to determine whether the target candidate is likely to want the job you have available – and whether they’d fit your organization’s values and work effectively with the manager and team in which you plan to place them.
All this will free up recruitment teams to concentrate on more strategic areas, such as building and maintaining interpersonal relationships with hiring managers and candidates. Humans will still need to verify a robo-assistant’s choices. The robot will assist by analyzing and presenting data and by communicating frequently with each candidate. Sophisticated artificial intelligence will enable them to dynamically interact and appropriately respond to each individual, creating an extraordinary, candidate-centric experience.
This is important because, as Generation Z joins the workforce, organizations now needs to engage every candidate and provide a recruitment experience that communicates their values and fits their employer brand. Make no mistake: your candidate experience is a sales tool that you should use to convince your applicants to come and work for you – and continue to patronize your organization even if you don’t want to hire them. And, don’t forget, the best candidates will have multiple employment options. You don’t want to lose them with a lackluster experience.
Using artificial intelligence in the assessment process is a balance. Some organizations such as the Japanese electronics giant NEC are already testing the concept of using artificial agents to interview candidates. However, for many employers this will be a bridge too far. In recruitment, your candidates are assessing your organization as much as you’re assessing them. It’s one thing to liaise with an avatar in the early stages of the selection process but the majority of candidates will want to meet someone from the organization before they turn up to work on their first day!
Artificial agents will be given names and characters and they’ll become part of the team. As in the above example with Sam, they could even represent actual members of the recruitment team. This partnership approach between humans and ‘bots’ is the model for recruitment in the future. It works in other industries. For example, an aircraft can be flown on autopilot but a human pilot is still needed to oversee the process.
It’s imperative for recruiters to be actively thinking about the future. We predict that in 2022 recruiters will work closely with robots to successfully meet the talent needs of their organizations. But many of these benefits can already be achieved today, by focusing your selection process on the candidates who have the optimum person-job match. Data-driven decision making and automated assistance can help you now to differentiate your employer brand, provide an engaging candidate experience, improve the efficiency of your selection process and, most importantly, to add real value to your organization by recruiting individuals who will stay longer, be more engaged and who’ll perform better.More about: Predictive Talent Analytics
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