The market landscape is changing — and rapidly, due to advancements in artificial intelligence (AI). Cutting-edge AI technology is already starting to make an impact on the way businesses operate.

Did you know? Fewer than 12% of the Fortune 500 from 1985 are still on the list today.

The reason?

Most of these legacy behemoths were unable to evolve fast enough to thrive in the new economy. Those that have survived have made significant technological and ideological changes since 1985.

The AI market is currently in a disillusionment trough as the expectations of AI have outpaced reality. But, by 2022, the employees who have embraced AI will rise to the top — and bring their companies with them.

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Put another way, as MIT President L. Rafael Reif says, “Over the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t”.

Here’s what you need to know about HR analytics:

The Primary Drivers of Market Change

There are three primary drivers forcing the markets to change: Moore’s Law, the War for Talent, and the rise of a younger workforce.

Moore’s Law was established by Gorden E. Moore, who recognized that the number of transistors on a microchip was doubling every two years while the cost of a computer was being halved.

Basically, the law says that technology will continue to become faster, better, and cheaper at an exponential rate — this is particularly true of AI and machine learning.

As anyone who works in HR knows, the war for talent is real, and it’s getting harder. Though companies have always jostled to hire and retain top talent, it’s getting more challenging. It’s a complex, modern war, that requires sophisticated, modern solutions.

Lastly, the expectations of the younger workforce (Millennials and Generations Z) have dramatically changed. These employees have become accustomed to how they use and interact with technology in their everyday life and they expect this to carry over to the workplace.

This scenario is referred to as the consumerization of enterprise software.

The most applicable takeaway is that employees are accustomed to being able to give feedback on their own terms — not the company’s. This requires HR teams to design new systems that enable this.

HR Adoption of AI

Currently, there are two HR trends driving the need to acquire advanced AI systems and work with knowledgeable partners to stay ahead of the curve.

The trends are:

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Because HR is expected to meet certain goals, the role of HR is evolving past gathering data to interpreting it. Unfortunately, HR is typically one of the most under-resourced departments. Despite this, HR managers are expected to provide the same high-quality deliverables as any other department.

Though it’s unlikely you’ll have the money to hire a team of data scientists to revamp your system (or to consult with your existing team), you can start creating a foundation of clean data that will drive you forward into 2022.

When you pair this data with a robust HR AI system, it’s possible to provide high-quality deliverables without needing the budget to hire data scientists — but you need that data!

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Mining All that HR Analytics Data

For any AI system to work, you need data — lots and lots of data. Not just any kind of data; it needs to be meaningful to the problem you are tackling, clean, and ready to be analyzed.

Fundamentally, the more you measure and track data, the more information you’ll have to drive action.

There are two primary types of data:

  • Systems of record: this data tells you about people. Think human resource management systems, talent management systems, learning management systems, and so on.
  • Employee-generated data: this data is collected via direct feedback from employees. Think employee engagement surveys.

Most companies moving toward AI systems are already relying heavily on systems of record. However, by overlaying this data with employee-generated data, you can get far greater insight into the issues you’re attempting to address at your company.

Part of this is identifying where your company is on the data maturity curve. There are four primary stages on this curve:

  • Data Aware: This comprises companies that collection data ad-hoc, are unable to trust their data or reports derived from the data, and lack data and system integration.
  • Data Proficient: This is a situation where a company has started to track KPIs (key performance indicators) but the quality of the data is unstructured and questionable.
  • Data Savvy: By this point, a company is using data to make critical business decisions.
  • Data-Driven: When a company hits this stage, it does not make any decisions without data, which is fine because it has data for nearly every decision it faces.
engagement analytics data maturity curve HR Analytics

No matter where your company is on the data maturity curve, continue focusing on collecting more clean data.

If the foundation of data is strong enough, you can move up to more robust workforce analytics and business intelligence solutions that can leverage cutting-edge technology and tools.

If this sounds like a lot, don’t worry. Every industry is experiencing these growing pains — and only those willing to do so will survive.

Final Thoughts: HR Analytics — Artificial Intelligence for Data-Driven HR

There is no choice to be made about collecting clean data and implementing AI systems that will allow you to make data-driven HR decisions. After all, a failure to do so will either cripple your company in the long term or lead to its death.

Changes in the market are happening at an exponential rate, requiring HR managers to work even harder to stay ahead of the curve — which is no easy task for the perpetually underfunded.

Though adopting powerful AI systems might seem daunting at first, the right partner can help your company evolve.

If you’re in a situation where your hands feel tied, you can still have an enormous, positive impact by starting to collect relevant HR analytics data.

Share what hurdles you’re facing with adopting AI in your HR system in the comment section below!      

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