The Pulse of Innovation in HR

A few weeks ago, I had the privilege to attend Sir Ken Robinson’s opening keynote speech – ‘The Pulse of Innovation’ – at HR Tech World Congress in London.

(You might recognise Sir Ken Robinson from his Ted Talk, ‘Do schools kill creativity?’, which has been viewed almost 45 million times so far.)

As expected, Sir Ken’s speech was filled with equal parts of humour, inspiring stories and thought-provoking ideas around creativity and innovation at work.

Sir Ken opened by highlighting that the average lifespan of organisations is now shorter than it ever has been, and he stressed the importance of continuous innovation and adaptation to external factors in order for organisations to survive – quoting the famous example of Kodak as a company that failed to do so.

Given the context of his speech, it came as little surprise that he stressed the importance of HR’s role in facilitating innovation by identifying and refining talent, and he brought forward one key point which I found particularly interesting – human talent is often buried.

Human talent is often buried

Sir Ken’s point is that talent is not something that we can easily identify, it is something that is hidden within individuals, and it is HR’s role to ‘mine’ for that talent.

“Human talent is highly diverse and it’s often buried. Human resources are like natural resources, you have to go and find them, cultivate them, refine them. If you do this you find that people are capable of extraordinary things.” Sir Ken Robinson

Everyone has potential but it can be quite difficult to see it amongst all the noise and stereotypes we bring with us.

To illustrate this point, Sir Ken cited his own experience interviewing Sir Paul McCartney and George Harrison, both members of a band I think you might know the name of.

During the interview, Sir Ken was surprised to find out that neither of these immensely talented musicians was recognised by their music teacher as ‘top of the class’ – yes, they happened to have the same music teacher in school.

This truly highlights the limitations of our ability to be able to determine what talent looks like (the poor music teacher must really have had to re-evaluate his assessment protocol!).

One of the reasons for this is that we are all inherently bias. While this bias is not conscious, it does affect decisions we make every day.

The ability to categorise or stereotype is an important developmental and evolutionary process that helps humans make sense of the world.

Stereotypes help us make judgements quickly without having to source all pieces of information, but it is detrimental when applied to identifying human talent and hiring decisions.

A basic example; in recruitment and talent acquisition, if successful salespeople in our organisation have all previously had red hair, we might decide that we should only hire red-haired sales assistants.

As human beings, when we try to identify what good ‘looks like’ we concentrate on a few aspects of an individual, and may end up ignoring other important factors that lead to success.

This was further highlighted in a recent Harvard Business Review article, where it was found that 40% of individuals in their study of 1,964 ‘high potentials’ (employees in the top 5% of the organisation) were incorrectly classified as belonging in that category.

In other words, almost half of those identified by managers were not high potentials at all.

42% were below average, with 12% actually being in the bottom ranks with regards to leadership effectiveness.

The point clearly illustrated here is the inability of managers to correctly identify high potentials by not concentrating on the right traits and skills of an individual – they are only human after all.

Taking the human [bias] out of hiring – to make it better for the human

Sir Ken Robinson spoke in detail about the success of the Beatles and how it was due to the diversity within their group – something that is almost impossible to achieve when allowing subjectivity to guide hiring decisions.

One way of addressing subjectivity and unconscious biases in the hiring process is to make use of data-driven technologies.

Using data to inform hiring decisions means HR can take into account the traits and skills that actually lead to performance, rather than keep focusing on hiring based on subjective stereotypes of success.

At Sapia, we develop predictive models, powered by artificial intelligence, that can predict the likelihood of candidates performing well in organisations based on their behaviour – not on the stereotype they fit into.

Our algorithms and questions are created so that everyone is given an equal opportunity to succeed and be considered, based on what actually drives performance – regardless of age, gender or nationality.

Through adopting AI and data science in the HR field, we can get one step closer to bias-free hiring and increased diversity within organisations.

Whilst AI does take the human out of some part of the hiring decision, the outcomes ensure the human is at the forefront with more opportunities for all.

If you would like to learn more about how AI can impact hiring outcomes in your organisation, feel free to get in touch with our sales team. You can also try it out here for yourself right now! 

Traditional psychological assessment has reduced the hiring and promotional error rate in modern businesses successfully for decades. They have also been used extensively to identify ‘hidden talent’ or ‘potential’ in people with limited work experience such as graduates, and also applied as a means for identifying future leaders at different levels of seniority, as well as in succession planning.

Psych testing is essentially an old-school form of predictive analytics, but they are limited in insight, providing a test of your ability to do a test. That’s it. Traditional psychological assessments do not link to actual performance in the role, nor do they have any self-learning functionality. There is no performance data that feeds into psychological assessments and therefore they have limited predictive power and no learning capability.

The worst aspect of psych tests is that you need multiple tests to test for multiple attributes. This is because they are just not that smart. This is where innovation necessarily disrupts an old formula. The difference lies in the data – volume and variance. A psych test is usually multi-choice questions repeated in different ways to achieve validity. You and I might pick the same option for each question and the only way to distinguish between you and me is to ask us a lot of questions and hope we pick some that are different to recognise our differences.

Data that comes from free-text answers to open-ended questions is by definition going to be hugely varied. A question like ‘what’s a favourite experience of working in a team’ asks us to each delve into our own personal experience, a behavioural interview question which means our answers will naturally be different. 

This formula of using data that is uniquely personalised delivers variance that psych tests just can’t deliver. Ever. When it comes to developing an Ai based assessment the questions that a candidate is asked, and the answers to the questions are suitably diverse, psychologically robust and designed with the same rigour in standardised Psychological assessments.

With the processing power and advances in Natural Language Processing (natural language being the origin of all psych tests) instead of having to force a candidate through multiple tests you can distil many attributes from one test. That test is usually 20 minutes, asks 5 questions, with up to 80 features able to be discovered about that candidate including their critical thinking, their drive, self-awareness, accountability and team orientation, their propensity to stay in a role or not, their HEAXCO traits and their communication skills.

The ability to better understand individuals based on their answers to questions means we can provide accurate and insightful feedback to everyone within a couple of hours. Feedback allows everyone the opportunity to be heard, understood and cared for. This is equity.

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We cover this and so much more in our report: Hiring for Equality. Download the report here.

Previously, I’ve written about how the world of recruitment has evolved.

It seems that using AI could consign fantastical or over-optimised resumes to the dustbin of history, along with the Rolodex and fax machines.

But how do we go about selecting the perfect (or as close to perfect as possible) candidates from AI-created shortlists?

It should be so easy to learn how to conduct an interview that adds the human element to the AI selection. The web is awash with opportunities to earn recruitment qualifications from a variety of bodies, both respected and dubious. There are so many manuals, guides and blog-posts on the best ways of interviewing. People have been interviewing people for hundreds of years.

And yet…

Why are there no perfect interviews?

We’ve all heard about bizarre interview questions (no explanation needed). We’ve felt the pain of people caught up in interview nightmares (from both sides of the desk). And we’ve scratched our heads and noses over the blogs on body language in face-to-face interviews(bias klaxon).

Even without the extremes, people have tales to tell. Did you ever come away from an interview for your ideal job, where something just felt wrong?

It’s clear that adding human interaction to the recruitment process is by no means straightforward. Highlighting these recurring problems doesn’t solve the underlying question, which is:

“We’ve used an algorithm to better identify suitable candidates. How do we ensure that adding the crucial human part of hiring doesn’t re-introduce the very biases that the algorithm filtered out?”

Start by asking better questions

Searching for “Perfect interview Questions” gives 167,000,000 results. Many of them include the Perfect Answers to match. So it’s not simply about asking questions that, once upon a time, were reckoned to extract truthful and useful responses.

Instead we want questions that will make the best of that human interaction, building on and exploring the reasons the algorithm put these candidates on the list. Our questions need to help us achieve the ultimate goal of the interview: finding a candidate who can do the job, fit with the company culture AND stay for a meaningful period of time.
It’s generally agreed that we get better interview answers by asking open questions. I’d expand on that. They should ideally be questions that don’t relate specifically to the candidate’s resume, or only at the highest level, to get an in-depth understanding.

We should try to avoid using leading questions that will give an astute candidate any clues to the answers we’re looking for. And we should probably steer clear of most, if not all, of the questions that appear on those lists of ‘Perfect Interview Questions’, knowing that some candidates will reach for a well-practised ‘Perfect Answer’. We want them to display their understanding of the question and knowledge of the subject matter. Not their ability to recall a pre-rehearsed answer.

And so, we need to remember that we’re looking for the substance of the answers we get, not the candidate’s ability to weave the flimsiest material into an enchanting story.

Five new interview questions to ask your AI-shortlisted candidates

So, here are some possible questions to get you thinking.

1. We use an algorithm as part of our hiring process to ensure we’re not missing the right candidates. Considering everything you know about us and this rlie, why do you think the algorithm has suggested you might be a good hire?
2. If asked, what would your colleagues in your current or previous role say about your work-related exchanges with them?
3. Tell me about any of your previous personal or career achievements that you feel relate to this role
4. We strive to be as diverse and inclusive as possible. Tell me about the places where you’ve most enjoyed working and the communication styles of people you’ve worked best with. Please expand on your reasons
5. Can you recall a time you’ve needed to make a business decision with incomplete information? How did you move forward?

Of course, you’ll need to frame and adjust those questions to match the role and your company.

How AI for HR helps interviews – and interviewers – get closer to perfection

AI equips recruiters with impartial insights that resumes, questionnaires and even personality profiles can’t provide. Well-constructed, supervised algorithms overlook all the biases that every human has. And that can only be a good thing.

Statistically robust AI uses an algorithm, derived from business performance and behavioural science, to shortlist candidates. It can predict which ones will do well, fit well and stay. We can trust it to know what makes a successful employee, for our particular organisation and this specific role. It can tell us to invest effort with the applicants on that shortlist. However unlikely they seem at first glance.

So we can use all of our knowledge and skills to understand a candidate’s suitability and look beyond things that might have previously led us to a rejection.
AI is the recruiter’s friend, not a competitor. It can stop us wasting time chasing candidates who we think will make great hires but instead fail to live up to the expectation. And it can direct us to the hidden gems we might have otherwise overlooked.

Technology like AI for HR is only a threat if you ignore it.

Don’t be that company that still swears by dated processes because that’s the way it’s always been done. The opportunity here is putting technology to work, helping your organisation evolve for the better. The longer the delay, the harder it will be. So don’t be left at the back playing catch-up.

There are very few businesses these days that communicate by fax machines – and that’s for a reason. In a few years, you’ll look back and wonder “Why didn’t we all embrace Artificial Intelligence sooner?”