3 Reasons AI Cannot Replace the Need for Human Agents

Are machines truly capable of replacing humans? With the latest advancements in artificial intelligence (AI), industry leaders are starting to ask what this will mean for the business process outsourcing sector – an industry traditionally centered around human connection. When AI was in its infancy, this question was easily dismissed. But, with AI’s increasing sophistication - the ability to make its own art, to write essays, to create fake media that is convincingly realistic -doomsday predictions are becoming more real. If AI is now capable of displaying skills and aptitudes once thought of as exclusively ‘human’, does that mean it can finally replace the need for human agents? Here are 3 reasons that’s not likely to happen, yet.  

AI Still Lacks Common Sense  

One notable example of AI’s limitations is its inability to use common sense, an area in which even AI experts say the technology is lacking. In one such case, a Silicon Valley entrepreneur famously asked GPT-3, “Which is heavier, a pencil or a toaster?” The AI replied, “A pencil”, something even a human child wouldn’t say. Likely because, as it combed the web, its main source of data, the AI came across no mention about the relative weights of pencils versus toasters. Therein lies the limitation. The reason GPT-3 was not able to make a reasonable assumption when faced with incomplete information was that it lacks a basic understanding of how the world works. 

 On the job, agents often encounter novel situations, unanticipated questions, and must call on their own base of general knowledge to solve a problem. The reason that humans do not get as easily confounded as machines when in unfamiliar terrain is that we can draw from a broad background of knowledge gained through years of personal experience, and trial and error. We’ve developed mental representations of people, places, and objects, how they behave and their limitations, instead of relying solely on a statistical relationship drawn from raw data. This narrow focus makes machines more prone to basic errors a human would likely not make.  

AI Has Problems Assessing Cause & Effect 

While deep learning algorithms can identify patterns much more quickly than humans, they lack an understanding of causation, or cause and effect. AI programs, for instance, can be taught to analyze weather patterns to predict the weather in real-time. They can ascertain that where there are clouds, rain usually follows, because they excel at detecting associations between two things. But they don’t necessarily understand the cause-and-effect principle, that rain comes from clouds. They simply know that the pattern indicates a correlation between the two, not causation. 

Imagine if an AI were trying to determine causality in a car accident claim. While a human agent could examine a photo of a toppled truck on a snowy freeway and deduce that the crash was likely caused by dangerous road conditions, an AI would have difficulty understanding the cause-and-effect relationship between the visible snow on the road and the wrecked vehicle. In fact, a new experiment by MIT proved the exact limitation of machine learning. An AI program was shown a virtual world filled with moving objects, while the MIT team fed it some basic information about the scene. When asked to identify the color of certain objects, it had an accuracy of 90%. But when asked the more complex question of “What caused the ball to collide with the cube?”, it answered correctly only 10% of the time.  

AI Isn’t Equipped with Empathy 

Though AI has become increasingly good at mimicking humans by detecting emotions via voice pattern recognition and mimicking human responses through sophisticated speech analytics software , one thing these technologies still lack is the capacity to empathize with human emotions and to build deep authentic connections with customers. Think about a billing agent learning that the reason a customer has fallen behind on their payments is due to an illness or tragic accident. An AI would be rational and would likely not have the emotional sensitivity nor the nimble, creative thinking to turn to their manager to see if a payment arrangement could be made. And think of what that customer would say about a company that shows that level of kindness. 

Though AI engineers are currently trying to add this situational awareness so that AI’s can detect and handle human emotions much as an agent would, that ability to understand someone else’s feelings, which we call empathy, is still not there. That is because much of what determines a person’s emotional response is subtle, hidden, and requires the ability to deeply understand complex human behavior. This is a skill that can only be gained through repeated socialization, human experience, and the ability to feel those same emotions, something which machines are currently incapable of doing.  

AI Cannot Replace the Need for Human Agents but It Can Enhance Them 

While AI technologies can lead to new efficiencies and cost-savings, and can certainly alleviate agents from routine, time-sapping tasks, we believe that contact center jobs will always be needed.  

Human relationships are still the foundation upon which authentic brand connection is built. The best results come from that perfect balance between the latest tech and human skill, when technology complements and enhances the natural abilities of people. itel’s Data Science & Innovation team can certainly help you optimize your CX technology stack, while our skilled operations teams can source the right agents to deliver that memorable on-brand experience.  

While the outsourcing industry may look different once the full potential of AI is unleashed, it is doubtful that agent roles will completely disappear. Agents may no longer collect or enter data, make basic service changes, or answer simple questions. In the future, those mundane tasks may be left to AI. However, agents could have much more exciting jobs where they get to take on a consultant role, delivering a highly personalized, high-touch service tailored to each customer. And so far, this kind of loyalty-building experience can only be offered through caring and skilled customer service representatives, not machines.  

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