AI sounds like hopes and dreams, full of unicorn visions and living robots, and presumably a thing to replace human’s vacancies. This technology is blamed disrupts in a major scale, leaving unemployment as the result to blame.
Although we continuously received various headline news about technology mistaken usages like crashes of driverless cars or the guessing of the end career of psychologists, many of us still believe that this buzziest buzzword in 2016 is a noun to replace human resources in all-around industries. We’ve come to guess how human career would be in a world full of sophisticated technology outputs.
As physics invents lightning rod to tackle unwanted sudden burn for human, AI creates machine learning to evade faults in scrutinizing a huge amount of data a human has a bare minimum to cope with. AI is not an output, nor a product variation to sell; it exists as a science, a way to solve impractical issues, so does along with physics and math.
AI references a machine that, thanks to its coding, might imprint human pattern and absorbed as a behavior to copy. Technically, this science helps human do repetitive activities and inquires, to consequently lets human do works with a higher level of emotion involved. Yes, AI, moreover chatbot, is on its way to having a sense of emotional approach toward its end-user. Yet even after decades of development, it still needs human to comply in such embodiment to the system. Machines might get the point but not with the underlying context beneath.
Human has a lot more peculiar and complicated nerves to connect upon a sequence of communication, and that what makes all of the conversations happened sense. The ability AI has affects the least to substitute human’s job; emotional relation is what makes human felt livelier and more engaging. The most common example of this is how customer service works ten years ago versus what is currently happening in the field.
Customers have to accept the fact that the company they relied on is the center of their universe, hence, nothing they could do to express complains or ask questions but to merely do two available options; send an email that possibly replied three or more days after or call the expensive cost dial.
What understandable reason lies behind in this action? 1) we know it took time to inspect every incoming email and listen carefully to each customer’s mumbles, or 2) the customer service might be busy. Both possibilities include imaginable repetitive inquiries.
Now, instead, armored by AI-powered omnichannel, companies realized they need to be all over the place and lets the available options for customers to connect varies. Chatbot helps the customer service officers to handle a more complicated rudimentary, so no customer’s need is skipped.
But yes, when it comes to adapt to new technology, some has to learn the system from zero; 1) the customer service needs to get used to the comprehensive interface an omnichannel may have and co-opted the following up standard operation procedures; what case to be transferred? When is the moment they can considerably well perform? Who to answer such specific questions? How to deal with a double ticket?
Or 2) someone, in any previous background, is in the order to finally learn the machine to train the bot, in which activities include are put down FAQs, inserting knowledge, and set according to answers. Training bot needs commitment to do, as companies might possibly change information to the customers, or company is simply knowing any possible inquiries that would be asked to the bot.
That means, jobs are unlikely taken away from the human, but the skillset shift implied along the way of its adaptation toward this technology might be a point to be focused on this discussion. This is the thin line between being replaced and being prepared.
The example above is the closest case of human and AI dynamics in customer service ecosystem, as we 3Dolphins provide a system that roots on the belief of collaboration; technology creates new opportunities for the human to practice a simpler way of working on copious repetitive disquisitions and move on to a deeper case that involves more emotion.
Being chef is a job found after the invention of fire, and well-done meat is something to eat rather than the raw ones; being bot trainer, or working on an omnichannel based customer service, are jobs found after the invention of AI, and a faster response is something to be served for the customer experience. The invention makes human uncannily found out the best in them.
On deploying a bot, for example, human can take part to train, explain, and sustain the bot;
- Train: human inserting the knowledge
- Explain: human putting down the necessary responsible for the bot, and defining the moment a human should take part in.
- Sustain: bot help human in examining similar patterns based on initially inputted knowledge.
This is what we call collaboration.
Oh, and about this, we blogged about supervised learning as an example of the coping mechanism human can do in applying bot to their system. On the eagle eye, businesses might have to reshape the vision inside out. This is the moment where stakeholders to connect the dots and resketch the roadmap. Fusion talents are the upcoming currency of human resource with no one can forecast this vast trend. As uncertain as it is, companies are expected to gather new random fusion talents made up of this technology.