Skip to Content

The impact of AI on emotional intelligence in the workplace

Jonathan Kirk
3 Aug 2022

The application of AI is increasing employee and organizational focus on unique human cognitive capabilities that machines simply cannot master. Emotional intelligence is one such area that AI and machines find hard to emulate – making it an essential skill set in today’s age.

Artificial intelligence (AI) is becoming more prevalent in our lives – both at work and at home. While many traditional job roles within organizations have already been automated, more sophisticated AI and machines are supplementing human intelligence and helping the human workforce to evolve their skills and roles.

One example of this is how AI can also be used to make our workforces more emotionally aware. Potential applications could include:

  • What is the best new role for an individual based on their experience?
  • How can we ensure the most efficient and slick retraining programs?
  • How can we provide a more personalized experienced for our people and customers?

How do we make AI emotionally aware?

Given increasing customer demand for more meaningful and personalized experiences, we can easily see how customer and agent emotions could impact these experiences – considering not only what customers want, but also understanding how they feel in that moment and modify the customer journey based on their feelings.

When we provide a recommendation based on a customer query, we anticipate a set of feelings and thoughts that govern that behavior and the actions we take. And behind these actions are thousands of emotionally aware judgments we make.

Currently, there are two ways we can learn in AI:

  • The first is using known outcomes to train a model that finds patterns and data trends to give the best result (method 1).
  • The second involves observing our environment and making decisions accordingly. The outcomes of these decisions teach us how to make better decisions and so on (method 2). This is the way humans learn, and it’s this flexibility that enables us to respond to new stimuli and make new decisions.

If we use method 1, the AI agent doesn’t understand the emotion of the customer and act accordingly, but applies the emotional intelligence of previous human agents to a similar problem – and, therefore, isn’t emotionally aware. Even if we train the AI to recognize emotions such as anger or happiness, the machine learns our interpretations of these emotions based on our labeling of the emotions and not on the data it is receiving. If we use method 2, until the AI agent has had enough experience to have learnt effectively, it would be like talking to a child.

A better approach is to combine methods 1 and 2 – build an AI agent to use current outcomes and labeled examples, and then as more data is collected, allow the AI agent to learn new patterns on its own. The AI doesn’t need to know which responses are from angry people, it will associate all similar responses together and call it whatever it likes. The AI then offers bespoke solutions based on similar behavior within the profile, learns from the responses, and records feedback to improve the outcomes each time.

How is an emotionally aware AI engine better than what we have today?

AI brings universal benefits such as consistency, repeatability, and scale – but it also enables us to understand empirically the role of emotions in customer interactions.

We can quickly change the offer to the customer if they change their emotion mid correspondence, and we can try out completely novel solutions and observe the emotional response to them. We have built a flexible and stable agent that can handle complex customers and understand how they feel.

Going back to the impact of AI on the workplace, what does a human agent do when replaced by a non-human agent? They can either retrain to manage the AI, spend more time innovating solutions for the business, or be available if the customer wishes to speak to a human agent – all of which are higher-value activities.

Indeed, perhaps the most important part of an AI agent is the ability to know when to revert to a human agent based on how the correspondence is progressing. After all, if the context is highly emotional, people prefer to talk to a human agent.

As a closing thought, perhaps we are simply living through a transition period, which will end when we can no longer discriminate between AI and human agents?

About author

Jonathan Kirk, Data Scientist, I&D Insight Generation, Capgemini’s Insights & Data

Jonathan Kirk

Data Scientist, I&D Insight Generation, Capgemini’s Insights & Data
Jonathan Kirk specialized in behavioral ecology before taking up a career in data science. He has over four years’ experience in both the public and private sector, delivering solutions to solve key business problems using both well-known and bespoke statistical and machine learning techniques.

    Innovation Nation | Summer 2022 edition

    Innovation Nation is much more than a magazine – it’s a zoom on what’s been happening in the last six months across the world of Intelligent Business Operations.