New Skills Required to Thrive in the Feeling Economy

As AI has become more powerful and influential, there has been a great deal of consideration given to increasing its implementation in the kinds of skills that can be used to help humans to thrive in the future of their work.

As AI has become more powerful and influential, there has been a great deal of consideration given to increasing its implementation in the kinds of skills that can be used to help humans to thrive in the future of their work. For instance, a few years ago, online learning provider Udemy examined the courses employers were sending employees on to try and understand what were the key areas of concern for organizations today.

The findings were interesting, as while there are numerous headlines written about the shortage in technical skills in areas such as computer programming and data science, Udemy found that the most popular courses were in soft skills, such as creativity and emotional intelligence. (For reference, the top three courses were on conflict management, time management, and stress management.)

Udemy argued that its findings underline the importance of soft skills, even as we live in the midst of our data-driven age where logic and reason seem to be the prevailing nature of all that we do.

The feeling economy

It’s part of a transition to what the University of Maryland’s Roland Rust refers to as the Feeling Economy in his book of the same name. With his co-author Ming-Hui Huang, Rust describes the transition over the last 100 or so years from the Physical Economy, which was based primarily on our physical strength and dexterity, towards a Thinking Economy that is predicated upon logic and data.

It’s a transition that has also been broadly followed by AI, with so-called mechanical AI relatively dumb and not capable of learning much about its environment or the uses humans put it to. In this instance, it’s not hugely distinguishable from traditional IT that is capable of performing rote tasks which don’t require a great deal of what humans would consider thought.

We have then transitioned into thinking AI that can digest huge quantities of data and make objective and fact-based decisions based upon this data. As is often the nature of data, this form of AI often excludes messy emotional concerns, like sentiment, feeling, and attitudes, but is capable of providing high-volume decisions at high velocity.

“Machine learning, neural networks, and deep learning are some of the current major methods by which thinking AI learns and adapts,” Rust explains.

He explains that this is the form of AI that currently dominates the economy, and so potent is the improvement in AI in these thinking tasks that we are on an indelible march towards a Feeling Economy, where both employees and consumers will place renewed emphasis on feelings and emotions.

Emotional support

This has been a transition that we’ve already seen to a large extent in the financial services sector, where the mechanical AI of ATMs was largely predicted to spell the death of the jobs of tellers and other support staff in banks but instead resulted in more tellers being employed than ever before.

Since AI started to take care of lower-order tasks, humans have been able to upskill and support customers in more complex tasks that required the kinds of capabilities that humans are uniquely positioned to provide.

We’re in the midst of a similar development with the provision of chatbot-based customer service today. The technology has become almost ubiquitous in customer service environments, with organizations motivated by claims that chatbots can reduce customer service costs by almost 30 percent. With customers also seeming to like the technology, it can be hugely tempting to rely purely on it and scrap human customer service teams altogether.

As we transition towards a Feeling Economy, however, this would be a mistake, as while chatbots can play an invaluable role in any organization’s customer service, providing round-the-clock support to customers on a growing range of issues, they shouldn’t be regarded as being the only solution you should offer.

Moving up the value chain

Instead, just as the arrival of ATMs heralded a move up the value chain for staff within bank branches, the arrival of chatbots should encourage organizations to invest in existing customer support staff so that they, too, can move up the value chain and start supporting those customers with issues that are ill-suited for the chatbot environment.

These might be, for instance, extremely high-value customers for whom a personal touch is expected or customers who, for whatever reason, are not able to successfully engage with chatbots. It might be for customers with complex problems that fall outside the realm of scenarios the chatbot has been trained to expect.

It’s crucial, therefore, that your customer service channels are joined up. For instance, surveys have suggested that nearly 75 percent of consumers would not return if a chatbot delivered unhelpful advice, and yet it remains rare for organizations to join up their chatbot with offline customer service reps so that they can easily step in if customers have needs that aren’t being met by the chatbot.

Picking moments

The majority of chatbots in operation today are great for resolving relatively straightforward requests and troubleshooting minor complaints from customers. Indeed, in such circumstances, most customers prefer dealing with a chatbot precisely because of its speed and convenience.