This Is What You Need to Know About AI

Recent studies on articifical intelligence-led unemployment certainly make uncomfortable reading. A frequently cited report points out that some 47 percent of jobs in the US will be automated in the near future.[1] Another study suggests that 45 percent of the daily tasks currently done by humans could be automated if current trends continue.[2] It looks like human beings will become the horses of the past. And horses were never given the chance to vote on their future careers.

What AI actually is

In spite of the doomsday views, the reality is that the current capability of artificial intelligence (AI) is actually rather limited. It is important to understand that there isn’t really that much intelligence in AI. Intelligence refers to one’s capacity for logic, understanding, self-awareness, learning, emotional knowledge, planning, creativity and problem-solving. Yet, at the moment, machines can do very few of these things.

The true ability of AI lies in the way that it processes a huge amount of data sources and then, based on this, makes the right guesses in terms of decision output. Machines “learn” over time by repeating the same task and adjust their information input and processing to become at guessing little by little.

It is true that machines these days can read , see images and hear natural speech with unprecedented levels of accuracy. For instance, at Nexus Frontier Tech, our Intelligent Scanner can already achieve 99.2 percent accuracy in reading typed or handwritten texts, and whether they are presented in the digital or physical form. In this way, machines look very capable and smart. Yet, under the hood, the mechanism is not very different from how our brain works (hence the fact that AI development right now is mostly based on artificial neural networks). Our brains work by taking information from various sources, determine how important each piece is, mix them together to form a decision. What machines are doing is just taking in new data, assigning a weighting to the data in terms of importance, and make statistical guesses. They don’t “know” what the input and output really mean.

What AI can actually do

Considering AI “learns” by going through the same routines again and again, what they can actually do is often limited to individual tasks. In other words, they are good at creating pockets of excellence but much less able to engage in full business transformation. AI is, at the moment, nothing much more than an app that supports existing business processes. In this sense, in the current business environment at least, it is not as much about AI as IA – ‘intelligent assets’ or ‘assistance’. It is just helping us to approach business in smarter ways – in particular, dealing with standardised work. Given that AI can only take over a single, narrow aspect of work, ultimately, what it can automate away is often only portions of a job.

It is also important to remember that the success of using machine learning lies squarely in the success of how the human activities surrounding the new technology are organised. Coming up with the processes and workflows, together with putting the right people in charge (someone who can manage both machines and robots), are therefore key to effective AI adoption.

In this respect, it is really unlikely that a vast number of people will be losing their jobs anytime soon. Machines cannot create, market, deliver, feed, clean or fix itself. AI, like other machines, are just tools. And tools need to be used to create value. By people. Lately, there is a Chinese T-shirt manufacturer that signed a memorandum of understanding with the State of Arkansas to hire 400 people by paying them $14 an hour in the company’s new factory. What is their job? They are there to make sure the machines and robots do their jobs properly. Machines, therefore, cannot do much with human beings. And human beings cannot really do much with machines. So, instead of arguing whether machines or AI will eliminate our jobs, it would be much better for us to think about how to be best friends instead of competitors.

[1] Frey, Carl Benedikt and Osborne, Michael. The Future of Employment: How susceptible are jobs to computerisation? Oxford Martin School, 2013. (

[2] Chui, Michael, Manyika, James and Miremadi, Mehdi. How Many of Your Daily Tasks Could Be Automated? Harvard Business Review, December 14, 2015. (

Terence Tse

Professor at ESCP Business School and a co-founder and executive director of Nexus FrontierTech, an AI company. He has worked with more than thirty corporate clients and intergovernmental organisations in advisory and training capacities.

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