Artificial Intelligence (AI) is here to stay and its impact will be felt across society, including in all areas of human performance. I recently attended an HR technology event where AI recruiting software was demonstrated. A number of these tools compared hiring profiles, resumes, and other data, and used machine learning to match people to positions. I asked the question “where do the hiring criteria come from?” and the universal response was – “we rely on the manager to define that”.
In our work developing profiles of critical job capabilities, we find managers often lack a strategic view of the roles they are managing. This prompted me to think more broadly about underlying issues in using automated tools and artificial intelligence in recruiting.
Though recruiting technology is mirroring the trend toward the adoption of AI more broadly, there is much we don’t yet know about how AI can help in recruiting. What key considerations do we already know about employing AI in the recruiting process, and how might they influence how we use it in the future?
Cautions Around Incorporating AI in Recruiting
As you explore using more AI in recruiting, consider this —
Efficiency is no substitute for quality
We know that the cost of a bad hire is huge – from low productivity, to direct costs of replacement hiring, to loss of team cohesion. In its early stages, AI has been used primarily as a resume filter – this is all about efficiency. But filtering out candidates based on limited criteria or a basic algorithm will result in good candidates being ignored. Some filtering may be necessary, but getting to a shortlist based on limited criteria means putting efficiency before quality. Better to invest in a robust hiring process that puts quality first.
Key success factors are often the hardest to measure
Recruiting technology usually uses job-specific skills and knowledge as a key candidate filter. This works if you have a large candidate pool of people who all have the critical capabilities you need, but this is rarely the case. For example, that type of skills matching is very common when recruiting computer programmers, but the best programmers are great at visualizing complex systems and learning new technologies quickly. Therefore, whether a candidate is a fast learner and can handle complexity is more important than his or her expertise in today’s leading technologies – and is difficult to infer from lists of skills and knowledge.
Bias always exists
Computer algorithms hold the promise of eliminating certain kinds of bias, but algorithms reflect the biases of the people who designed them and the data upon which they are based. Here are three truths about high performance:
- There are many ways to be successful
A one size fits all recruiting model won’t get it done. Looking at a current successful employee is just a snapshot of one way to excel in the job. Think about different ways employees could succeed in the role and the range of different strengths that could make some successful. Considering too narrow a range as a first pass will surely limit the pool of capable people.
- Capabilities reside in teams, not just individuals
We do not work alone. Frequently it’s the team that needs the capabilities, not any one individual. Over time, biasing requirements towards closely matching those of existing team members leads to less diverse opinions and expertise. Think about the extent to which you need a wide variety of knowledge and skills vs. deep expertise in a few. If you are doing automated candidate matching, make sure that the algorithms that score different factors take this into account.
- Current capabilities may not be critical to future performance
Past performance may be a good indicator of future performance for a specific job, but if the job changes, it may require capabilities not well represented by your current employees. A focus on performance data is inherently backward-looking, while business strategies and key job requirements are changing more rapidly than ever.
Advice for Moving Forward with AI
There is much potential with artificial intelligence in hiring, no doubt. But there’s a big gap between AI to suggest what else you might buy online based on your purchasing history (Amazon, say) to deciding which potential employee should join your team. Yes, AI tools will become more sophisticated and be able to infer more related capabilities over time, but we are still a long way from having the technology to accurately assess leadership effectiveness, teamwork and influence capabilities by looking at candidate data.
Until that day arrives, here is my advice:
- Think carefully about your hiring criteria and their relative importance. AI is better at assessing requirements that are easiest to define, not those that are most important.
- Ensure your minimum screening criteria really are minimums, and that you give yourself an opportunity to evaluate critical success factors before discarding potentially strong candidates because of one missing learnable skill.
- Supplement AI with other assessment approaches. Most complex success factors are still evaluated through behavioral interviewing, work simulations, and other types of assessment. These are generally conducted as the last step in the recruitment process. Be very careful about eliminating candidates before we have assessed them against the most important criteria.
- Consider closely how roles may need to change in the next couple of years, and what different capabilities may be required to be successful. In this sense, past performance and capabilities may not predict future performance.
At its best, AI can help rapidly source a qualified candidate pool, and allow recruiters to spend more time evaluating the best candidates. It can help eliminate bias and promote gender and racial opportunity. At its worst, it can filter out unique people with exceptional talents that don’t fit a narrowly defined profile, thereby reducing diversity and inclusion of backgrounds and perspectives. It’s our job to steer the recruiting process in the right direction by asking the right questions and correctly using the tools and interpreting the results. Good luck in this brave new world of recruiting.