If there is one emerging theme from recent client conversations, it’s this: What is the role of AI for the future of employee surveys? Can AI truly revolutionize employee listening programs and if so, how? While this debate is just getting started, it’s clear that AI capabilities are already at work, and future possibilities are rapidly approaching.
Historically, organizations have introduced new technologies to their workforce, yet with AI, adoption is taking more of a “bottom up” approach, with employees using various tools in their jobs. In fact, researchers at MIT have found that while 60% of business leaders expect Generative AI to disrupt their industry over the next five years, and 76% have explored using Gen AI in some way, only 9% had already widely adopted the technology within their organization. Employee listening programs represent an area ripe for AI deployment, helping organizations scale their analysis and action planning efforts.
Machine learning already plays a crucial role in employee surveys by analyzing vast amounts of data and providing valuable insights. Machine learning algorithms can detect patterns, trends, and correlations in employee feedback, enabling organizations to gain a deeper understanding of their workforce. These algorithms learn from historical data and can continuously improve their accuracy over time, making them an invaluable tool in employee survey analysis.
One of the main advantages of machine learning in employee surveys is its ability to work at scale. Take OrgVitality’s Action Prioritization tool, for example. Traditionally, consultants would make recommendations of where to take action on a manual basis – this is realistic to provide a leader or a few leaders with deep recommendations, but is often not realistic to support each manager with results. Yet the Action Prioritization Tool, which was validated against OrgVitality’s team of consultants, automatically suggests three items that are most likely to have the greatest impact on the manager’s piece of the organization. This algorithm is customized for each client based on which items are most actionable for a manager, and which topics are especially critical for success in that organization.
Additionally, machine learning technology has proven to be useful with comment analysis. The OrgVitality UQ, for example, is an AI-powered algorithm that instantly rates comments on how actionable they are, enabling leaders to focus on the most useful comments. This automation allows HR professionals and leadership to focus more on taking action, rather than spending excessive time on manual comment summaries.
While still at its infancy, AI solutions offer numerous ways to enhance employee listening programs. By harnessing the power of AI, organizations can gain deeper insights into employee feedback, improve decision-making, and enhance action planning.
Currently, there is a lot of interest in a ChatGpt-like function embedded in portals to answer any manager questions or generate text for presentations or team meetings. This includes the public OpenAI version as well as company specific tools, which may be developed internally for security purposes. While helpful, it’s important to ensure accuracy of the responses, something most tools currently can’t provide. AI may also prove helpful in explaining charts, graphs, or heatmaps to managers.
Another capability that is garnering interest is furthering comment analysis. Often, survey practitioners want to use Large Language Models (LLMs) to summarize survey comments. This can be possible, but is an area to use caution, only entering verbatim comments when security is certain. Additionally, the purpose must be clear. LLMs can summarize comments, but they cannot assign scores to comments, such as sentiment or topics for sorting or analysis. They can however provide helpful narrative overviews, which may be useful for users with large volumes of comments to review.
The future of AI-driven employee surveys is promising, with several exciting trends on the horizon. As AI continues to advance, organizations can expect to see further improvements in employee listening programs. As with all new technologies, it’s important to start with the problem you are solving for. As we continue to develop our AI tools, we discuss with our clients what their biggest pain points are, to ensure that we’re developing solutions that fit their needs. Overall, our goal is to help clients use AI and GenAI to free them up from tactical work or information searches in order to free them up for more value add, knowledge centric work.
Overall, the future of AI-driven employee listening programs has great potential. By thoughtfully exploring AI technologies, organizations can create more effective and tailored employee listening programs, leading to meaningful improvements and organizational success.