Leveraging AI to Boost Workforce Engagement and Motivation

There are two sides of human performance in your workforce that you can use data, analytics and artificial intelligence to understand. One is the competency side, and the other is how willing your employees are to engage and use those competencies. But how can you encourage your employees to embrace AI to boost their performance?

Employee engagement and motivation are critical elements for your organization’s success. Highly engaged teams are more productive, innovative and resilient. Motivated employees help create a positive and competitive work culture, the kind you need to breed creativity, innovation and long-term growth. So many of the measures of motivation and engagement are qualitative and subjective, making it a great place for AI to help you derive insights into your employees and create strategies for boosting performance.

Let’s talk about how it can do that.

Real-time insights and enhanced interaction

Prior to the explosion of large language models (LLMs) with user friendly interfaces, most sentiment analysis tools were designed for static analysis - extracting sentiment, keyword frequency, topic modelling from large text and so forth. While these were effective, they often required significant work and expertise to interpret and bring into proper context, and were limited to what had happened, not what was happening or what would happen.

On the other hand, LLMs are interactive and conversational, making them adaptable in real time to a vast array of business inquiries. You can use them to interact dynamically with data models to get tailored responses and brainstorming when you ask questions.

Of course, LLMs and other AI tools have their drawbacks. However, while complex language features – such as irony, sarcasm and slang – pose challenges for traditional text analytics, many of the best available LLMs are trained on vast datasets rich in linguistic diversity. As a result, these models can effectively interpret tone, context and slang, providing responses that feel more appropriate. This makes them powerful tools for real-time engagement and insights, but also valuable for capturing all the responses you get from employees and analysing their evolving needs in a way traditional text analytic tools could never achieve.

Real-time sentiment analysis for employee feedback

Let’s apply this enhanced text capability to employee feedback. AI sentiment analysis can detect nuances in language, interact with employees and provide insights in real time.

Through the analysis of words and phrases conveying frustration, satisfaction, enthusiasm or stress, you can get both a high-level view of employee morale across teams and get immediate insights helping pinpoint and address potential issues before they escalate. There are available tools trained to differentiate between someone making a statement to blow off steam versus someone following through on action. This will then allow you to see if someone is venting or making an effort to leave the company or potentially something more drastic.

Personalized insights through predictive analytics

With the help of AI, we can use historical data on employee behaviours – such as attendance, performance metrics, engagement survey results, chat interactions and more – to identify patterns indicating employee disengagement. By understanding the capabilities and preferences of each employee, we can generate a task list for managers of personalized intervention options.

If an employee’s behaviour shows a current or projected decline in engagement, you will need to make a call on if and when you should engage in a one-on-one conversation. This way, you can better understand their needs and decide whether you should lead off with growth opportunities, quality of life incentives or resources to help with work-life rebalancing. Used in this way, AI provides a nuanced, employee-centred approach to engagement, and lets you get ahead with an intervention before your employee begins to disengage.

Chat interaction for continuous feedback loops

No one likes annual long drawn-out surveys, especially when analysing and acting on the results takes too long. Pulse surveys have become popular for this reason, and now intelligent virtual agents are becoming popular for real-time and continuous feedback potential as a result.

By using an AI-powered tool, you will be provided with rapid results to engage with your employees with issues at hand during their current mindset. An immediate discussion can be had and feedback can be better given to reach a more solid solution.

Instead of providing a pulse survey, a virtual agent could periodically ask employees different questions about how they’re feeling about their workload, team dynamics or career growth. They can prompt employees to provide feedback at their convenience, with an easy chat interface lowering barriers to engagement. The subsequent insights allow you to respond to any emerging issues or insights immediately, and you can push out information through these agents about how you actioned employee feedback as well.

Identifying motivation drivers with AI

Motivation is complex. There are multiple theories describing how people work, but in the end, we are individuals, and our reasons for doing things are driven by complex, individual-specific factors which vary broadly across teams and departments. However, advanced analytics, intelligent or otherwise, lets you analyse large numbers of data points to identify factors contributing most to motivation within specific populations.

By getting a general understanding of the things that are most likely to drive certain groups, you can craft strategies for those groups aligning incentives with their intrinsic and extrinsic motivators. Intrinsic motivation is particularly complex and multi-dimensional and require assessments of your work environment and employee experience. We look at measures of autonomy, job satisfaction, meaningful work and opportunities for skill development. Extrinsic motivators are simpler to understand, as you are likely to have a lot of data on how different groups have performed if you’ve applied rewards like a bonus, promotion or recognition, or punishments like developmental counselling or reprimands. Still, by putting all of those together, you can get an understanding of the things that make people want to work and can look at whether or not those are being applied in the best way.

AI-powered recognition systems can help with this by tracking employee achievements and behaviours, prompting your leadership teams to acknowledge these achievements, and suggest rewards based on insights as to what things are most likely to motivate a particular employee or group. You can customize these systems to align with your company values, goals and employee preferences, and even personalize them to offer things to an employee they are most likely to value.

Recognizing individual variations in motivation

Different people value different things. Not everyone responds the same way to the idea of a bonus, time off, developmental opportunities or those amazing snacks and coffees you want to offer in your break area. Any plan you have to measure and monitor motivation and engagement should be highly individualized to be effective. This has proven difficult on large teams, where leaders didn’t have access or flexibility to get to know their teams well.

That’s where AI and analytics come into play. These can help you get to know people, making the interactions you do have with them more meaningful. They can also help you synchronize motivation across these large teams. Employees with diverse skills, abilities, knowledge, preferences, passions, values and motivations all want different things, but those things have to be applied in a way that makes the team work together.

Are incentives motivating people to contribute or are they creating friction? As you’re using these tools, it’s helpful to have a solid change management plan and communication plan, so that people understand exactly why their teammates are receiving particular treatment, and a plan to monitor the motivation, job satisfaction and other metrics of the team members.

Balancing and motivating your team

As we wrap up our exploration of motivation and the factors that influence it, think about team sports. My favourite example is American football, a sport hinging on both individual motivation and team engagement to achieve victory. Each player’s skills and drives, the coach’s leadership and the collective spirit of the team all drive success. However, each one is motivated differently – some by the thrill of competition, some by team loyalty, some by the personal goal of excellence, some by fame and fortune – but all those things need to be applied in a way that keeps the team cohesive and working together.

Bringing motivation and engagement back together as a team in your workplace is crucial, and it’s part of creating that company culture that keeps people engaged and satisfied. AI can help you do that, but it can’t do it for you. By thinking through how you can use intelligent tools and models at each step of this process, you and your AI partners can create more opportunities for engagement and to leverage the collected insights into strategies helping you adapt and win in real-time.