How Artificial Intelligence Can Help Curate a Better Employee Experience

While writing this blog post, I found myself jumping onto Instagram. What are my friends up to today? Any funny memes being sent my way? It was a Friday after all, so I thought I’d spend a few minutes scrolling. Then I saw an ad for new running shoes. I almost purchased a pair earlier in the week but got distracted. Here they are again, and this time, in just a few short minutes, I’ve purchased them. They’ll be at my house in 3 days, and I’ll be back to running in comfort. 

Both Instagram and the running shoe company tracked my clicks, amount of time I spent on each page, and curated the items displayed to me based on my past shopping history. They’ve measured many similar experiences to provide a seamless journey and influence my behavior to achieve their goal, sales.

What if companies treated the employee experience similarly? As more work moves to software platforms and tools, we have an opportunity to encourage behavior that improves the employee experience while striving to achieve company goals. Traditionally, this has always been the job of the manager and over recent years, the manager's role has only become more difficult. But technology can help, especially with the advancements in artificial intelligence and machine learning.

The opportunity to enable managers, HR, and People Teams with intelligent insights is a big reason why we’re building Peoplelogic, an intelligent employee experience and organizational success platform. Peoplelogic leverages tool data to organize intelligent insights around employee behavior, attitudes, and collaboration. These insights lead to improved decision-making and ultimately, a more engaged workforce.

In the article below, we’ll explore how companies are already using this type of data and how signals can be used by organizations to make higher quality decisions.

People Analytics, It’s Already Happening 

Many companies are already collecting and organizing their team and employee work data. People analytics is the most common term used for this function, but there are various new emerging categories. Many of them involve active and passive employee listening. This includes the collection and analysis of data from employee feedback, interactions, communications, and sentiments.

With surveys (and passive listening), the goal is to hear what employees are collectively happy or unhappy about. It often involves measuring and anonymizing digital interactions, such as emails, chats, and collaboration platforms. By organizing this employee data, companies can begin to draw meaningful insights into what’s affecting behavior, performance, and engagement levels.

These insights can empower organizations to align their strategies with employee needs, resulting in a more productive and satisfied workforce.  

How Data Can Uncover Otherwise Hidden Signals 

Companies that are using data in their decision-making are outlasting and outperforming their competitors. By gathering and organizing data points from various sources, Peoplelogic presents signals or changes which inform actionable insights. Just as companies analyze consumer behavior to enhance marketing strategies, teams can use signals to make informed decisions that nurture employee engagement, collaboration, performance, and more.

It’s important to note that acting on signals and insights from employee data alone can be dangerous. For individuals unfamiliar with this type of data, it can be easy to rely on these insights and expect it to be infallible. However, the data can lack rich context that only a manager or leader can have to inform the action(s) that should be taken.  

For example, if an employee's engagement levels are decreasing, you probably shouldn't go ask directly how you can increase their engagement. Seeing that signal is an invitation to explore more. A decrease in engagement through employee data leads to questions like, how have the employees' metrics changed over that same time? Did they recently take on more work? Or have they been taking care of their sick child? The data might not tell you each of those data points together.

Although technology is rapidly advancing to combine more data to provide context, a signal of declining engagement can point managers to places they can focus their time and attention. Simply checking in with that individual, even if there’s nothing they can do to help, can increase engagement because it shows employees that they work with people who care.

Platforms For Understanding Employee Needs

By scrutinizing data patterns, organizations gain insights into preferences, pain points, and motivators that shape employee behavior. Identifying trends in communication, collaboration, and interactions help tailor strategies that resonate with employees, thus fostering a sense of belonging and engagement. 

Just as companies use data to encourage specific user behavior in consumer-centric contexts, data signals and insights can nurture desired employee behavior. By finding positive behavior patterns that contribute to engagement, companies can introduce incentives, rewards, and recognition mechanisms that motivate employees to align their actions with organizational goals. 

For example, Instagram uses my behavior in the platform to target me with ads for running clothes and shoes. And they’re very good at it. Imagine being able to target employees and managers with better communication techniques, process improvements, and tailored company training when they need or want it most. 

This is what managers are working towards whether they’re utilizing data or not. They’re trying to influence the behaviors of their workforce in order to achieve goals with efficiency. The idea is to get ahead, explore, and uncover how different processes might be affecting engagement levels of specific employees or teams.  

Enhanced Decision Quality with AI

Finally, as we touched on above, there’s traditionally been a risk of implementing people analytics for organizations that aren’t data literate. This is because most insights and signals still require interpretation. However, with the recent surge in artificial intelligence and machine learning, platforms like Peoplelogic are able to combine various insights and additional context to enhance decision-making. Informed decisions, backed by comprehensive data analysis, mitigate risks associated with trial-and-error approaches.

Data acts as a guiding light, illuminating previously unexplored facets of employee behavior and engagement. With the help of artificial intelligence, companies and platforms like Peoplelogic can make sense of all this data faster, while providing curated insights and actionable recommendations to the organization.  

Does this mean that an AI-driven people analytics platform will take away the job of a manager? That every company will build a perfectly balanced culture between performance and engagement? Of course not! But it does mean that every company can have the opportunity to use data-backed insights and signals to improve their decision making, explore better options, and lead a diverse set of employees effectively through the ever-changing business environment.

Conclusion 

In a world of constant change, companies need to leverage data to measure and enhance engagement levels, aligning employee behavior with organizational objectives. By recognizing that data can provide signals and intelligent insights into team and employee behavior, companies are empowered to make informed decisions that enhance the quality of their strategies. Although there is a lengthy list of buzzwords in this growing area, they represent a transformative approach to workforce management, one that places employee engagement at the heart of organizational success.

As organizations continue to harness the power of data-driven insights to promote positive employee behavior and elevate engagement, the synergy of people analytics and human resources undoubtedly play a pivotal role in shaping the future of work. Through a commitment to understanding employee needs, fostering desired behavior, and embracing an iterative approach to decision-making, companies will be better equipped to cultivate a motivated, engaged, and high-performing workforce.