According to McKinsey Global Institute’s report published in May 2018, Skill Shift: Automation And The Future Of The Workforce, demand for technical skills such as advanced IT and programming is predicted to more than double by 2030.
Mass changes in industrial skillsets are part of history. Adaptation is part of survival, after all. Yet with the growth of artificial intelligence technologies, basic cognitive skills are simply not enough to survive the challenges that the workplace of the future will face.
As artificial intelligence appears set to reshape business and our very societies, there is both excitement and concern in the industry. Time and time again in times gone by recently, we have seen big-scale, big-data projects fall short of expectations when delivered. Disappointment in big data’s over-hyped transformative insights seemed to cast a shadow over the powers of data and analytics until now, where machine learning seems to be the vital connector.
The artificial intelligence we see applied today – for the most part – has grown from algorithmic patterning developed in the fifties. These days we have vastly superior computing that can process huge sets of data, but that doesn’t make artificial intelligence ‘intelligent’ in any way close to the way we are, as humans.
“Technology is the easy part. The hard part is figuring out the social and institutional structures around the technology.” John Seely Brown, Silicon Valley Guru and former Head of PARC.
Professor of Computer Science at Northwestern University, Krist Hammond defines artificial intelligence as ‘any program which does something that we would normally think of as intelligent in humans’. How the program does it is not the issue, just that is able to do it at all. What Professor Hammond suggests is distinguishing between artificial intelligence for automation, and artificial intelligence for human augmentation – or in other words, making humans smarter and faster.
As Don Norman, the originator of user-centric design recently said, artificial intelligence needs to “accept human behaviour the way it is, not the way we would wish it to be.” He continues to point out that technology can inadvertently result in artificial stupidity if poorly designed, implemented, or adapted to a human context. In the light of this thinking, McKinsey recently reported with some urgency the need to put in place large-scale re-training initiatives for workers now, if they are thought to be most affected by automation (see chart).
Context, Country By Country
Whilst we do not disagree with McKinsey’s extensive research findings, understanding context is of prime importance in the conversation about the skills of our future workforce. Seeing beyond hyped headlines about robots stealing jobs requires business leaders to understand how automation will be introduced and developed within their country’s individual industrial make-up.
For example, the demand for cognitive skills, such as those found in administration, management and manual labour, are predicted to remain the largest skill category for countries where extraction industries are high in production labour. For example, McKinsey’s research indicates that Great Britain’s need for social and emotional skills will overtake those of manual labour – Germany, on the other hand, will require higher cognitive skills in the next ten to fifteen years. Such differences in industry and economy mean each country’s predicted automation capacity hangs on the speed and enthusiasm with which AI technologies are adopted; first by sector, and then by organisation. We would argue first and foremost, that such shifts are more dependent on present day European political environs, than technological advancements alone.
The Next Ten Years
What we think we will see in the next ten to fifteen years in real estate is human judgment deepening and becoming less biased, thanks to algorithms and machine learning. Most of us in real estate still use manual, or at best semi-automated processes in finance, property management, and portfolio management. Spreadsheets abound collating and analysing data for property valuations, cost analysis, lease management and forecasting. Vast swathes of time are spent reading, manipulating and extracting key data which is not only open to error and security breaches, but so dull!
Viewing humans and ‘robots’ as complementing each other, rather than competing with other, is the more astute view. In corporate real estate, for example, the area where we can see robotics and cognitive automation helping professionals is in analysing data with more speed and accuracy. We see it streamlining the management of lease recording, as well as compliance and risk monitoring. As productivity increases, so costs decrease; automation and machine learning technologies are generally more cost effective to implement than the historic practice of offshoring.
However, perhaps most importantly, is the satisfaction that more creative and human-centric work can bring us. Technologies that relieve our staff of repetitive and mundane work, mean that time can be spent doing meaningful work, like evaluating business models or developing new service lines. Never before has the built environment needed us to transform our outlook and develop our skillsets as today; and never before, have there been so many commercial opportunities that need us to show our humanity.