By Jill Strange
Imagine the creation of personally tailored learning and development plans generated by behind-the-scenes big data processes, or nuanced, real-time employee engagement analysis. These are just a couple of examples of the ways in which artificial intelligence (AI) has already started to impact human resources (HR) and recruiting.
With the advent of any new technology, we are just beginning to uncover creative use cases that can provide meaningful business impact. Just five years ago, these initial examples might have been considered fantastical. Yet, with the advent of innovative text analysis algorithms, groundbreaking classification methodologies in machine learning and the maturation of big data analytics, AI is primed to revolutionize HR in unimaginable ways.
Today, AI-infused tools and applications are accessible enough to be integrated into a technology stack relatively quickly, with the ability to experiment almost instantly. But will it be as easy as treating the algorithms as a black box and sticking them on a Microsoft PowerPoint slide the next day, just in time for that crucial executive meeting? Not by a long shot.
Don’t get me wrong, despite my skepticism, AI will eventually help companies advance in the areas previously noted, as well as many areas that are yet to even be approached. But like any other technology and analytics trend, there will be winners and losers.
The winners will be those companies that take the time and effort to understand the nuances of the tools, algorithms, and underlying data architectures creating the magic.
Winning companies will also realize that the use of AI is journey, not a destination, and that as they begin to experiment with its use they will undoubtedly uncover numerous pitfalls that require adjustments in their approach.
Termination and Performance Prediction
Machine learning is perhaps the most successful branch of AI from an industry standpoint. A major part of that success is in encapsulating the final model, as well as its training data, testing procedures, and estimated parameters into a black box that simply works. Want to know which candidates are most likely to be successful for your open position? You have a group of employees whose termination potential you’d like to measure over the next one to two years? Just feed some information to the classifier and let it spit out predictions like the Oracle at Delphi.
Predictive talent analytics and employee flight risk models will revolutionize how HR approaches workforce planning. The fact that you can robotically feed data to a classifier and get useful, insightful results a sizable percentage of the time is as good as magic to me. However, with the real world being a complex place, plenty of human intervention will be required for a flight risk model to work under a diverse set of scenarios.
For example, in the case of the termination prediction model, do you know how your data was generated? Which distributions describe each variable? Do latent variables exist which may have biased your data? Is your model biasing your predictions? If so, how?
Winners will anticipate these questions and design a scalable, multi-stage data pipeline around their model, allowing them to detect patterns and anomalies in the dataset at any stage. Those that do will generate insightful results for their organization and increase the effectiveness of their recruiting and talent management processes.
Learning management systems and coaching modules have been used for years by the HR industry to provide career pathing and development for an employee to help them excel in their current role and aspire towards a promotion. In this case, AI can leverage increasingly mature big data technologies to crunch massive diverse data sets, such as terabytes of resumes, performance reviews, and tons of historical data to unveil a personalized learning and coaching module optimized for a role and experience level. This seems pretty automatic, right?
Sadly, even in this scenario, winners will understand that they need to employ a top-notch development team that takes the time to craft a highly organized and layered data architecture. Data lakes, in particular, illustrate the thin line between a successful data architecture and an unsuccessful one. While the ability of a company to gather its data sources into one place is an accomplishment worth celebrating, the next step in the process is even more critical—labeling and organizing your data. This seemingly simple process turns out to be crucial for any enterprise to run a successful big data operation and will be vital to those organizations looking to revolutionize employee development processes through optimized learning.
Sentiment Analysis for Employee Engagement
Sentiment analysis techniques have been used in recent years to uncover positive and negative emotions and biases in everything ranging from tweets to Yelp reviews. While several companies have ventured into this space, in the coming years we will see the rise of its application at a broader level in the HR space to measure the feelings and engagement levels of employees. Does the employee like the company? How about the job itself? Is the employee upset about a lack of opportunity?
How does sentiment analysis for employee engagement work? Essentially, in a given employee’s response, substantive words in a response are mapped to a lexicon and words are given a positive or negative score. Some scoring mechanisms are simplified, assigning simple + or – rating to a word. Others are more layered, assigning a range of positive and negative scores (-5 to +5).
In practice, sentiment analysis to measure employee engagement can be quite effective. But what is less publicized are the ways in which biased, or even blatantly incorrect results can be generated. Instead of blindly mapping words to sentiments, winners understand that capturing as much fine-grained context in the language as possible will lead to better, more accurate insight into employee engagement levels.
A Balanced Approach
As systems develop and more data becomes available, AI will continue to impact HR in numerous ways. Taking the time to understand the benefits and pitfalls to various approaches is every bit as important as building the right algorithms and underlying data architectures. But, the end result will reap numerous benefits for companies willing to put in the effort. The journey to effectively using AI in HR practices is a long road, but exhaustion is the price of being a winner.
Jill Strange is the vice president of Science Applications at Infor. She has over 10 years of experience in successful human resources, talent assessment and acquisition, and talent management in industry, government, and military organizations. Prior to working at Infor, Strange worked in the human capital management departments in both Wolters Kluwer and APTMetrics, as well as was an adjust professor at the University of Tulsa, teaching on subjects like job analysis and performance management. Strange earned her BS in psychology from Southern Methodist University and her PhD in Industrial and Organizational Psychology from the University of Oklahoma.