Artificial intelligence and human resources may seem like strange allies at first glance. Artificial intelligence (AI) calls to mind words such as “cold,” “robotic,” and “automated,” while human resources (HR) is, for all intents and purposes, focused on people and relationships. Even the names themselves seem hopelessly at odds: AI is artificial; HR is human. This cursory lens may be an underlying reason why HR has been one of the last functions to adopt AI technologies, while nearly every other department is in the midst of an AI frenzy.
The truth is that AI and HR are actually a far better-suited match than their competing labels would suggest. In fact, a growing body of research indicates that AI can actually make organizations more human. Harnessing artificially intelligent technology, organizations are improving transparency, delivering personalized coaching at scale, and helping leaders to more deeply understand the health of their workforces. All told, AI is giving HR, leaders, and managers the resources to dramatically improve the employee experience.
Without AI: Employee voice is lost
The most prime example comes in the form of the employee feedback process. Gathering, understanding, and responding to the employee voice is arguably the most “human” endeavor an organization can take on. However, as employee feedback is overwhelmingly complex and rife with emotion, most organizations today fail to effectively harness their people’s opinions.
The traditional and most common approach relies on long, arduous employee engagement surveys that neither inspire employees to respond nor motivate managers to take action. Analyzed manually, team-level information is slow to make it to those who can create change (taking weeks or months to interpret), and, once received, is difficult to parse and act on. Leaders and managers struggle to understand the most important areas to improve. Meanwhile, data loses both urgency and relevancy as time passes. Actions lose meaning. The employee voice goes unresponded to.
As a result, employees feel unheard, under-valued, and decidedly out-of-touch with the leaders and managers who proclaim that the organization’s people are its number one priority. Translation: The traditional approach is far from “human.”
With AI: Turning feedback data into sustainable change
Automation has dramatically improved the employee engagement survey process. More and more organizations are eschewing the traditional approach described above in favor of frequent pulse surveys and technology-enabled dashboards that provide managers and leaders with team-level data in real time. Even before AI is added, this improvement alone leads to more relevant, actionable data that spurs more effective change.
What AI has enabled is more than faster results: AI means leaders and managers can receive personalized, predictive insights that allow them to not only respond more effectively to employee concerns, but to also preempt difficult situations as negative sentiment begins to stir. Using millions of data points, this technology can pinpoint major problems on the horizon, surface emotions and recommendations, and spur meaningful, change-oriented conversations that were never before possible at scale.
Traditionally, engagement data has been a snapshot in time — a measurement of the current state and, in some cases, the change over time. AI moves beyond past and current states to provide insights into likely events in the future.
Artificial intelligence and machine learning can monitor and analyze millions of data points, finding patterns and discrepancies across engagement scores and trends over time, as well as disparate sources of data. This technology then surfaces predictions about future engagement and other key outcomes like turnover, performance, quality, and more. Further, the system learns over time from each organization and team, making more personalized and accurate predictions, so teams can have more effective and personal conversations about the future, rather than the past.
Harnessing the narrative
Open-ended commentary is the pinnacle of humanity when it comes to employee feedback — comments house the nuance in the employee voice, including emotion, suggestions and ideas, and hidden connections that scores fail to encapsulate. Yet, with sometimes tens or even hundreds of thousands of comments shared in each survey, it’s impossible for leaders to take in and decipher the complex story that’s being told.
AI changes that. Natural language processing can sift through hundreds of thousands of open-ended comments, connecting qualitative and quantitative data to tell a visual story. Loads of disconnected feedback suddenly turns into valuable narratives, surfacing trouble areas and hotspots that would otherwise remain elusive.
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The NLP engine has been trained on millions of employee responses, enabling the system to infer sentiment and categorize underlying patterns into topics. These topics, along with the visually-surfaced connections between them and the associated positive or negative sentiment, provide the basis for the story of a workplace. The algorithms and foundational taxonomy continually learn as new data enters the system, becoming more and more accurate — and customized to each organization — as time goes on.
American Banker described how First Horizon National Corp., which owns First Tennessee bank and other financial firms, began using AI to analyze its annual employee sentiment survey. “It’s been critical in how we are revising our performance management philosophy,” explained Mario Brown, vice president, manager of leader assessment and development for First Horizon.
Using AI to give disparate employee feedback critical context gives leaders and managers the basis for taking effective action, while removing much of the potential for human bias and error. In essence, when applied to commentary, AI connects the dots between what your employees say, how they feel about it, and how it relates to engagement and other outcomes. It synthesizes comment data to instantly pinpoint the issues your employees care about most and to show how sentiment and importance evolve over time. This information leads managers and leaders quickly down the path to improvement and spurs meaningful conversations about change.
Unlocking human-oriented HR — with algorithms
With clearer and deeper information about how their employees think and feel, leaders and managers are able to make better decisions that impact the experiences of their people.
With less time spent analyzing and interpreting data, HR teams are freed to spend more time coaching and developing leaders, managers, and employees, strengthening relationships, and implementing strategic and creative programs. These new technologies, while powered by algorithms, ultimately create a more people-centric and human workplace.
What’s more: improving organizations’ abilities to understand and respond to employee feedback is just one example of AI making the workplace more human. Instead of worrying about the robot revolution, it’s time to consider how these technologies can revolutionize HR.