Note: This article from Adam Rogers originally appeared on Forbes.
This rapid progression is driven by advanced machine learning, systems that continuously consume and apply knowledge to improve accuracy and analysis. AI has already proven its potential for expediency and efficiency, completely redefining the way we live, work, and relate to each other. The prospect of AI actually improving human-to-human interaction, especially within the business environment, is particularly intriguing.
Human innovation paired with technological development is a powerful force. I’ve had a front-row seat to what that can accomplish in the HCM space, where technology has evolved from simply performing basic HR processes, to predicting future outcomes, to fundamentally restructuring how employers manage their people. Smart technologies powered by machine learning, natural language processing (NLP), and distributed data-collection interfaces are poised to completely transform the workplace for HR leaders.
Big Data Fosters Understanding
Running a business requires a substantial amount of data.
The term “Big Data” broadly refers to the staggering volume of information available at any given time. Today’s technology can digest these data mines with superhuman capacity and speed, applying advanced mathematical algorithms to find patterns, trends, and outliers. This insight can transform how managers approach everything from turnover to overtime, and provides an opportunity for leaders to better understand their teams and how to strengthen them.
Automation plays an important role in the data consumption process and is responsible for unparalleled advancements in convenience and productivity. Complex administrative functions have often plagued employers, particularly in HR, and these solutions alleviate the potential for human error while dramatically increasing completion speed. What used to take dozens (if not hundreds) of hours of human analysis is now achieved almost instantaneously.
This laid the groundwork for the game-changing power of prediction. Today’s leading HCM solutions leverage Big Data to forecast everything from performance success to flight risk, and these self-taught systems are incredibly accurate, extremely adaptive, and constantly learning. Rather than simply reporting on past trends, they predict future ones, empowering leaders to drive continuous improvement within their organizations.
Consider retention, for example. Turnover is a top priority for today’s leaders, as it can contribute to increased business errors, negatively impact culture, and cost up to twice an employee’s annual salary, according to Deloitte’s Josh Bersin. By automatically processing and analyzing a wealth of HCM data, predictive AI tools can identify the employees most at risk of leaving and alert their managers to proactively address the situation—before it’s too late.
This brings us to the next frontier for disruption: prescriptive analytics. Once again building on past innovations, prescriptive systems pull from predictive functions to suggest specific, personalized actions at key decision points. These AI-based recommendations can make managers better leaders, applying unbiased data to solve difficult decisions. The prescriptive functions can inform not only who to talk to and why, but how, based on what’s worked well in similar situations. Using the above example, the manager of a high-risk employee may be encouraged to take their employee to lunch, thank them for their continued contributions, and initiate a conversation about their career goals and aspirations. The additional support helps leaders coach and engage their people, leading to improved business outcomes, but the process also relies on the innate human traits of intuition, empathy, and kindness. It’s symbiotic, people-first AI.
Leveraging the Voice of the Employee
According to a 2016 study by The Center for Generational Kinetics, the best way for leaders to improve retention is listening to and addressing employee concerns. This seems basic, but it’s actually quite difficult to scale listening to the voice of the employee (VoE). Organizations have traditionally relied on annual performance reviews to accomplish this goal, but once-a-year discussions don’t always suffice (especially when tied to salary negotiations).
Employee satisfaction surveys are another alternative, but poorly designed (or executed) surveys won’t deliver impactful results. The most effective surveys offer a mix of qualitative and quantitative prompts, but these take significant manpower to analyze.
Fortunately, recent AI developments are solving this dilemma and improving employee-manager relations in the process.
Speaking Our Language
Despite the remarkable advancements AI has made in learning and prediction, users are often frustrated by its failure to understand the true meaning of words. Google Translate can instantly convert text from one language to another, and even basic Web-based chatbots can provide reasonable responses to basic queries. But until recently, these systems relied on literal translations and interpretations. Human language is incredibly complicated, brimming with context and subtleties, making it challenging for machines to decipher.
Enter natural language processing (NLP). This technology has improved incredibly quickly, and NLP solutions are learning to think and speak like humans. It’s the difference between a French 101 student and a native Parisian — the AI is actually speaking our language, not translating it. Machines can now discern emotion recognizing differences between sadness, anger, even sarcasm. It’s a breakthrough with incredible implications.
There are many potential opportunities to leverage NLP in the workplace, such as advanced customer service bots or email summarization software, but employee surveys are an obvious start. By applying NLP and machine learning algorithms to open-ended, text-based surveys, leaders receive feedback on what their employees are saying as well as how they’re actually feeling. Managers can keep a pulse on their team’s health and use these metrics to arrange one-on-one conversations, evaluate opportunities to increase satisfaction, and drive organizational change.
Improving Human Connection
Truly understanding how people are doing their jobs, what they care about, and what inspires them can improve nearly every aspect of the employee experience. Developments in automation, analytics, and NLP are making these insights possible and fostering authentic connection and understanding between managers and their employees.
In this way, AI has accomplished perhaps the last thing we expected: it’s making managing more human.