AI is the fastest growing trend of any technology – EVER

It used to be all about Mobile First, now it’s about AI-first. Google now calls itself an ‘AI first’ company.

“How do you decipher the truth from puffery? Are there any shortcuts to really understand where AI is best applied in your business?”

I’m not a data scientist, but I spend my days talking to users and buyers of AI technology who are befuddled and increasingly cynical about the hype. Here is my 3-step guide to cutting through the noise.

1. Understand that AI is not the same as Machine Learning

Most products are using standard statistical techniques like regression. Without any machine learning baked into the technology, they are just matching tools. There are efficiency gains, but no ‘smarts’ and no learning in technology.

For example, in recruitment a stock-standard AI product would merely find you people with the same profile as those you have already hired, matching applicant profiles to hired profiles. CV parsers do this kind of thing. Now, that can be helpful to you if all you want to do is a short-cut to the same profile fast, and it can definitely save your recruitment team time.

But unless you know that these characteristics also match performance, you will not make a difference to your organisational outcomes.

If your Sales Director tells you that every new hire over the last year is hitting or exceeding budget, then absolutely keep using that tool. If she tells you that a third or more are underperforming or leaving the business, your AI tool is merely amplifying that bias and doing quantifiable damage to your company’s bottom line.

If you want both efficiency and business bottom-line impact – your AI needs to have machine learning baked in.

Takeaway: If the organisation selling you an AI tool has no Data Scientists, there is no machine learning in the product.

2. No data means no AI

If I imagine a Maslow’s hierarchy of AI it would look like the below:

First up you need to have the data.

About 50% of companies don’t pass this threshold, but assuming you have it the next step is understanding the data context: What is the business problem you are trying to solve?

Next is the housekeeping. This means consolidating, cleaning, categorising and cataloguing the data. And then finally the optimisation is at the top – this is where the magic happens. Optimisation is the last mile and is what gets you to the big savings, but you need everything underneath it in order first.

In recruitment a genuinely smart AI tool with machine learning baked-in works best in these conditions:

1. High volume recruitment where you can gather applicant data fast. Which is also why no one is selling AI tools for exec level recruitment yet.
2. Hiring for roles where you have a way of measuring performance AND that performance needs to be a whole lot better. For example, almost all retail businesses track turnover and usually also whether it’s regrettable or not. If that number is above your industry average, then AI works for you. If you have no way of measuring performance for that role, AI will not give you much return on your investment.
3. Recruitment for low skilled roles or roles where success is not about a specific qualification or vocational skill. It’s about soft skills – communication, learning agility, customer awareness and empathy and so on.

Takeaway: It’s critical to have a solid understanding of what you’re trying to fix, and the means to measure the changes you’re making. Ask yourself why you are considering AI if you can’t quantify the problem, to begin with.

3. Once you know the problem you want to solve expect a long term commitment to fix it

AI is about optimisation. For credit card companies it’s detecting fraud quickly. For online retailers better product recommendations. In recruitment, it’s finding the best new hires in a massive group of rookie players.

In each case, you are optimising for efficiency and accuracy, as the cost of getting it wrong is huge.

It means trusting the technology to find the patterns. You have to suspend theory, and your assumptions, a lot. You feed in a large amount of relevant and unbiased data and the machine learns on its own, finding the patterns. It is looking for the ‘signal in the noise’. Humans are unpredictable and more often than not unreliable.

The current hiring processing by humans is extremely resource-consuming and the result is not always satisfying. Using AI will free up your time if you allow it to, improving efficiency or outcomes, often both. But AI built just off CV data only adds bias and we’ve all see how badly that ends.

Takeaway: Predicting human decision making is not easy and not quick. The only way to get to the ‘answer’ is to start now and expect this to be a journey.

You can try out Sapia’s Chat Interview experience right now, or leave us your details to book a call

If you liked this article, suggested reading: 
A CV tells you nothing
To AI or not to AI

To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.

A major study has validated the ability of AI-powered, chat-based interviews to assess personality traits and job fit.

The analysis of Sapia’s model, which uses text-based communication to interview candidates, has been published in peer-reviewed journal IEEE Access.

The researchers used data from more than 46,000 job applicants who completed an online chat interview and a questionnaire based on the six-factor HEXACO personality model. The HEXACO traits are honesty-humility, emotionality, extraversion, agreeableness, conscientiousness, and openness to experience.

Personality models such as the Big Five and HEXACO are based on the ‘lexical hypothesis’. That is personality characteristics are encoded in language, showing the foundational impact of language in defining identifiable personality traits, the researchers say.

After the applicants’ personality traits were assessed they were asked to provide feedback on the accuracy of how they were described. Also, the researchers found 87.8% of the participants agreed with the description given for each of the six traits.

Behavioural questions

Sapia CEO Barbara Hyman tells Shortlist that in the Sapia interview question, they aim to avoid focusing on hypothetical scenarios that create the potential for candidates to give similar answers to others. Additionally, the interviews are oriented towards behavioral, not situational questions.

Candidates can likely work out what trait is being assessed by each question. However, they can’t “game” their responses with pre-rehearsed scenarios, she says.

Examples of the questions used in the interviews include:

• Which of our values do you really connect with, and why?
• Explain to us the one thing you really want to learn from someone else that you work with and why?
• What is a goal that you’ve set for yourself and what did you learn from achieving it (or not)?
• Sometimes things don’t always go to plan. Describe a time when you failed to meet a deadline or personal commitment. What did you do? How did that make you feel?
• What motivates you? What are you passionate about?
• Not everyone agrees all the time. Have you had a peer, teammate or friend disagree with you? What did you do?
• In sales, thinking fast is critical. What qualifies you for this? Provide an example.
• Would you rather win, or be happy? Explain your answer.
• Tell us about a time when you went above and beyond to do something for someone; how did that make you feel?

Sapia: Making recruitment easy!

Candidates respond to the assessment questions with a noticeable sense of intimacy and authenticity, even including emojis in their answers. “The same way they would respond to a friend”, says Hyman.

Finally, she adds that a text-based approach leaves less room for recruiter partiality compared to CVs, psychometric assessments, and video interviews.

Predicting personality using answers to open-ended interview questions, IEEE, June 2020

Source: Shortlist.net.au | Wednesday 15 July 2020 9:21am

Two recent events have brought HR back to their core role – managing risk.

In Australia, we have seen large numbers of staff getting underpaid in some sectors, such as retail and hospitality. For some of these businesses, it’s precipitated the collapse of the company. For others, it’s impacted their share price as these businesses make provisions for back pay in the hundreds of millions of dollars.

Globally – the coronavirus impacts on HR more than any other function. HR has to lead on managing health and safety for employees, guide organisations on remote working, support the CEO and leaders on their internal communications and public response to coronavirus, and a lot more.

Apart from those current risks garnering a lot of media attention, what business risks really ought to matter to HR? 

Ask a CEO of a sales/service business what they believe carries more risk to their bottom line – increasing turnover coupled with a high cost to hire, or a declining engagement score measured from a survey?

If you are hiring just 100 people a year, you can expect to LOSE 80 DAYS of work capacity to recruitment. Using automation tools like ours reduces that by 80% giving back 504 hours to the business. Click here to see case studies on time savings from using PredictiveHire.

HR managing business risk means tracking metrics like: 

1. Cost to hire – which should be the direct (the recruitment team for example) and indirect costs (the opportunity costs of all the people involved in recruitment)

2. Time to hire for any organisation that relies on frontline staff to deliver value- sales or customer service

3. Turnover especially early churn and non-regrettable churn, which is a good objective measure for quality of hire

4. The percentage of promotions within, only if you use hard objective data to make those decisions

5. Most of all, whether these metrics are going in the right direction quarter on quarter

Suggested Reading:

https://sapia.ai/blog/to-ai-or-not-to-ai/