As early as 1950, pioneering computer scientist Alan Turing made some predictions about Artificial Intelligence, saying: “I believe that at the end of the century, the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”
Almost seventy years later, while we rarely talk today of machines thinking, we already take many instances of artificial intelligence for granted as we interact with machines every day. What is more, they are going to become increasingly pervasive over the next few years. When we look back, I expect this year to be a tipping point in terms of AI becoming a reality, with businesses successfully leveraging it to derive significant benefits.
Here’s how I believe the industry will continue to advance over the course of 2018…
Ubiquitous virtual assistants
Most of us already frequently interact with artificial intelligence, for example when we call our banks and insurance companies. The chatbots that answer and direct our calls are obviously not human, but I believe that it is soon going to get harder and harder to tell. Their understanding of language is improving all the time – which is helpful for those of us with an accent – and we will increasingly find contact with machines becoming more and more natural. In fact, in just a few years from now, we won’t even notice we are dealing with bots. This is also great news for call center agents who will be freed from repeatedly answering the same basic questions and who will be able to focus on handing more complex issues that will still require the more “human” characteristics of creativity and empathy.
Government health programs will turn to AI
While the medical industry can be described as cautious when it comes to transforming technology systems, the last few years have seen hospitals transform from paper-based to digital systems. Artificial intelligence is being used in many cases to review these newly-digitalized medical records and to support clinical staff by helping predict or highlight life threatening conditions or behaviors – for example at the San Carlos Hospital in Madrid. With medical spending becoming an increasingly contentious topic in most countries, we’ll likely see AI being leveraged to accelerate diagnosis and for preventive medicine – after all it is cheaper to fix many medical problems the earlier they are caught. For example, AI might be deployed to spot and flag anomalies in X-rays for expert review by radiologists, or to spot cancerous cells.
At least two disruptive AI business models will emerge in the next 12 months
Digitalization has transformed many industries – just a few years ago it would have been unthinkable to imagine that Alibaba, the most valuable retailer, has no inventory, or that Airbnb, the world’s largest accommodation provider, owns no real estate. I believe we’re going to see artificial intelligence deliver disruption on a similar scale. My advice is to keep a close eye on the retail industry – which is ripe for the first wave of AI-based disruption. We will see AI taking the place of your spouse and/or friend while you are shopping, making suggestions not just for accessories but advising you on style.
Eighty percent of all larger organizations will investigate AI
There are absolutely no large businesses that would not benefit from the support of AI in some form or another. And this year, I expect to see a marked increase in the number of live implementations. I believe that six out of every 10 enterprises in the European region will move from the theoretical stage to actual proofs of concept. However, once they reach the implementation phase, 100 percent will also encounter a lack of appropriately-skilled employees.
Over the course of 2018, the net effect of AI will be positive for the workforce
This year, we’re going to see the emergence of a whole new market for AI-based jobs. Initially we’ll continue to see demand for individuals with AI skills– but this will be followed by a new wave of less technical jobs – from user experience experts to customer focused copywriters who will draft chatbot scripts. As AI penetrates further into the workplace, its successful adoption will depend on people’s trust in the recommendations given by AI systems. This will give rise to a whole new collection of jobs at the interface between the AI systems and the people who interact with them.
Beyond this year (and possibly the next two), I expect the landscape to change more dramatically, as AI systems increasingly automate many traditional jobs – a trend which will create a growing challenge in the labor market by 2020.
Assembly line workers will increasingly have robotic colleagues
To date, robots have been largely restricted to working inside cages on manufacturing lines. However, the emerging generation of smart autonomous robots can see, touch, and collaborate safely with humans while taking on the heavy lifting of assembly work in addition to boring, routine tasks. At our factory in Augsburg in Germany we have collaborated with robotic expert Kuka to develop a “smart” robot that recognizes when humans are close, ensuring that there is no danger of injury, for example by the sudden, unexpected movement of a robotic arm. We’re going to see this human-robot collaboration increasingly frequently – with humans controlling and monitoring the processes. This is the very first step towards a hybrid work environment where humans and robots will work hand in hand.
All aspects of manufacturing will use AI to some extent by the end of 2018
I believe that all larger manufacturing companies will use AI for at least some part of the value chain, either in logistics, manufacturing or maintenance. In manufacturing, yield is king, so we’re going to see many more implementations of AI at the process control level, using machine learning to better control processes to improve output. We’re also going to see manufacturers deploy AI at the plant level – helping optimize production and to continuously re-plan to accommodate unexpected events such as supplier delays or unplanned machine downtime. And on the subject of downtime, we’re going to see machine learning deployed for predictive maintenance – allowing us to replace systems before failure and to coordinate any downtime, leading to significant cost savings and increased availability.