Ashutosh Garg, CEO and co-founder of Eightfold, is reimagining talent management from top to toe using machine intelligence. We sat down with Ashu to understand how the workplace is changing, and how artificial intelligence is helping to create a better and fairer job market, bringing benefits to employers and employees alike.

In the age of AI and data, that should change. It should be more of a data-driven process, instead of everyone relying on a few relationships here and there. I really wanted to see what we could do to change employment in society - because employment is the backbone of society. That is what inspired eightfold.

So now, as business leaders, we need to change our thinking. In this global world, we can’t limit ourselves to a few connections, but give opportunity to a broader set of candidates. As an example, if you’re in the US, you might not be able to easily hire people out of Stanford or MIT because of the competition. But guess what - there are plenty other colleges in the world that have great graduates you can choose from. You may not be able to hire people who know the latest and greatest in data science or machine learning, but what you can do is hire people who can very quickly acquire those skills.

Another example, since people are thinking of switching jobs every 2-4 years, as a leader you can change your mindset and encourage your employees to look for opportunities within your own company. At the same time, change your expectations: You can’t expect people to stay with the company for the rest of their lives, but what you can expect is to have a life-long relationship, so that at every turn in their career they’re thinking of coming back to you.

The way we are reimagining things is, instead of asking the hiring manager to find people who are already using the skill, we’re asking for people who have the potential to learn it.

That’s the fundamental difference: Knowing what they can do, not what they’ve already done.

Using machine learning, we can make estimates on how a person’s skill set is likely to change over time: What is the next skill they are likely to acquire? What is the next tool they are likely to learn? Then you can match them with a job opportunity based on predictive analytics, instead of only limiting yourself to the people who know this skill already.

This is similar to how colleges do admissions - they are trying to pick people who have the potential to be good at something, often based on how diverse their previous experience is: The more different things they have done already, the higher the chances they are able to acquire new skills quickly. Somehow, this mindset of looking at a person’s potential and likely next steps hasn’t made its way into businesses.

• Watson is IBM’s main research lab in Westchester, New York; Almaden is their Silicon Valley branch.

Where it also stems from is I saw working at Google - and Google has published this analysis - is that there is no correlation between interview performance and job performance. Why? Some candidates of mine are phenomenally smart: Ask them any question, they will know the answer. But guess what - they just don’t want to do the work. And other candidates of mine might not be able to answer the same interview question, but they can do what is needed to get to an answer.

Psychometric tests and other point-in-time assessments are trying to evaluate you at a given moment. The problem with this is that they don’t do a good job at predicting what you can do, because they give too much importance to that moment. The way we have approached it is that, instead of doing that point-in-time decision, we collect and analyse as much information as we can about what this person has done over the years.

A simple example of how machine learning and AI help us is if you look at my profile to evaluate me for a job. Anything in my profile that does not matter for a job, should not be there, and AI can help to filter out those elements. The eightfold system can anonymise any information that gives away my age, gender, ethnicity, education, previous companies, and skills that are not relevant to the role. These would only add unnecessary bias to the hiring decision.

What this has done is reduce bias for our customers. What we saw is that before using our platform, there was almost 50% bias in respect to one gender.

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At vyn, we believe in simplifying work-life using augmented intelligence - the combination of human insight and artificial intelligence. If you enjoyed this article, you may also like our series on Demystifying Data Science, where vyn advisor Mayank Sharma explains the recent boom in data science, how businesses are using it, and pitfalls to avoid. You can learn more about Eightfold on their website.

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