The Exponential Impact of Machine Learning on Recruiting and Talent Development

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There’s a chance you may be rolling your eyes at the idea of another pie-in-the-sky post on how AI and machine learning will disrupt our jobs, industries, and societies. Certainly the excitement – and the hype – has been intense, yet with good reason.

In working with our team and customers at Ascendify, I’ve witnessed the genuinely dramatic impact machine learning can have, specifically on companies’ talent-related efforts. From where I’m sitting, the hype doesn’t actually seem so off-base. The results we’re seeing are not only having an impact on business metrics, they’re immensely encouraging in a broader sense. Unlike the many “robots are taking over the world” prophecies out there, this piece is about how with rich data, smart algorithms and predictive analytics, we can make huge, positive leaps in talent acquisition and development — with benefits for businesses, candidates, and employees alike.

It’s something to be excited about. Let’s walk through the fundamentals and implications of using machine learning to both find and hire the right people, and then using it to develop and support the employees you already have. I hope it sparks some inspiration and triggers some action items — and I’d love to hear about your ideas and experiences as well.

Collecting and Connecting Data

Business intelligence and augmented decision-making hinge on data, so collecting intel at the right points and in a meaningful way is foundational to realizing the opportunity of machine learning. What does that look like? These are some of the key data points we help our customers gather:

  • Individual employees’ skills, strengths, interests, and goals
  • Overall strengths and skill levels of whole teams and departments
  • What projects employees have been a part of, and the results
  • What learning and leadership activities employees have undertaken
  • Peer feedback on, and manager recommendations for, each employee
  • Individuals’ career progression

All of this is stored, sorted, and matched, and is the learning fuel that “feeds” the machine learning algorithms.

An Infinite Learning Loop That Gets Smarter as It Goes

The more data that pass through the algorithms, the better they become at predicting outcomes. Each new data point adds to a repository of ground-truth examples the algorithm can reference to make a prediction. When applied to the types of data mentioned above, businesses can use machine learning algorithms to determine what you could call “predictive success metrics”: a set of skills, strengths, experiences, and qualities common among the most successful employees in a company.

You see where this is going.

Smarter Talent Acquisition

Now, instead of gut instinct, you can make data-driven hiring decisions based on a candidate’s match with your success metrics — and therefore, their likelihood to be a top performer at your company. By completing a capability assessment as part of the application process, applicants are intelligently evaluated against your best-fit profile, arming you with intel before interviews even begin — and thereby boosting quality of hire.

But that’s just the first improvement. As candidates become new hires and then experienced employees, you’re now funneling new data into the system. Your algorithms soon will be able to reveal which sources drive the best candidates, and which recruiting events and activities have the most impact.

You’ll also be able to leverage smart data to create custom learning and training programs for new hires who showed skill gaps in the recruiting process.  

Intelligent People Management

Think of all this as “machine learning meets coaching”: With info on each employees’ strengths, areas of growth, goals, and feedback from their peers and managers, you can easily connect them with helpful learning assets, specific stretch projects and applicable leadership programs — all instantly and intelligently surfaced via machine learning. You can readily support their professional development and provide an internal path for career advancement, while filling team skill gaps and retaining experienced employees.

You can also get much more proactive with people planning: Machine learning will identify strengths and gaps at the team and organizational levels, and recommend whom to hire (or train), how long it will take and more. It can also instantly and constantly assess both internal and external candidates for succession.

There are even more ways machine learning can help with talent, yet by now the point is probably clear: Bringing talent and intelligence together has exponential results. Machine learning supports a smarter hire, a more motivated workforce and a far more agile enterprise.

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