Disruption. That was the big buzzword of 2016; the year in which we witnessed Brexit, the election of Donald Trump, and the emergence of artificial intelligence (A.I.), all occurring in a relatively short span of time. Chinese astrologers might claim that this was no surprise as 2016 was the year of the Monkey and, in that light, the election of Donald Trump was, well…not all that unpredictable. Disruption was in the cosmic cards, and looking back we could agree, but what are we to do looking forwards?
Between Trump’s executive orders and Theresa May’s renegotiation of the UK’s trade deals with Europe, it is hard to predict what’s in store in 2017. Already firms are looking at making their own Brexit and relocating elsewhere in Europe, potentially resulting in a great number of job losses. The exit of the United States from the TTP has the Asian signatories trying to find a way to ratify the deal before the 2018 deadline and what remains to be seen is if things with the United States will remain business as usual, or if Trump will negotiate new deals with the South East Asian region—potentially even shutting its doors to its allies and trade partners here in the East.
A Whole New World
Political disruption is nothing new, but in a post-globalised world connected by trade and technology, businesses are more exposed and need to plan ahead to deal with the possible changes to come. Some of these changes we are already seeing: the change in attitudes and approaches to work from a tech-savvy, net connected, Millennial generation; the greater need of businesses to interact “personally” via social media; and the effects of big data analytics and artificial intelligence.
Surprisingly the real-estate industry has been one of the slowest adopters of technology and those firms that capture and harness the data available to them and leverage this will be the winners in this highly competitive space.
Both social changes and insights derived from data led analysis are moving developers and planners away from the traditional model of district driven, single-purpose buildings into more multi-functional, symbiotic … I dare say, organic, structures.
The insights derived from big data analytics will allow us to look at more than just where a building will be located and if it meets with regulations. Buildings can be planned to create wind corridors to increase natural cooling rates and reduce power expenditures; positioned so travel to these buildings will affect the flow of traffic, possibly redirecting heavily congested routes into less travelled ones; designed to be self-sustaining – not just having office spaces, residential spaces, and food and entertainment services, and even farming facilities to support the restaurants.
The amount of data needed to have such an integrated facility operate smoothly is astounding. Footfall traffic, noise levels, energy and water usage would all need to be assessed or even simulated, based on existing models of human behaviour. Highly sophisticated tracking and real-time analytical systems are all needed to make this possible. The things AI will allow us to do is extraordinary and will demand multiple levels of input to see the best results.
We have the information, it’s up to us to start being creative and work with the data to prevent repeating the same bad planning mistakes, be this in development or corporate real estate.
The data you choose, is the data you use, and its analysis formulates your muse
Big Data, Big Changes
In the workforce AI is looking to become a major disruptor as well. Within Human Resources AI driven algorithms could be used to assess potential candidates long before a human being even lays eyes on their CV. This is not new. From candidate assessment tests to a search filter on LinkedIn, weeding out candidates via automation is already commonplace. And that’s, quite frankly, the problem.
Despite the level of automation that already exists, recruiters are still reporting that finding quality candidates is a problem. This problem will only get bigger as we move forward into a future disrupted by significant cultural, political and technological change.
We need to be careful when it comes to relying solely on automation, as cautioned by data scientist Cathy O’Neil, author of the book Weapons of Math Destruction. Algorithms can work wonders on routine, predictable, and clearly defined tasks that can be codified, but they’re not so good with the other stuff. The messy, human stuff.
Algorithms can be flawed. As supposedly unbiased and rational as an algorithm is supposed to be, it is only really as unbiased and rational as it is written to be. Unintended bias, illusory correlations and outright ineffective methods of assessment can all be programmed into an algorithm.
Another problem with thinking that AI alone will solve our problems is that systems can be gamed. With data, the outcomes will only be as good as the input—a garbage in, garbage out scenario. And data can be manipulated, doubly so in a world of “alternative facts” and “fake news”.
What makes algorithms and models effective is constant evaluation of the models used. However, in HR this is a tough ask. Given that many HR departments do not even contact candidates that fail to qualify, you can be certain that even if failed candidates later turn out to be star performers elsewhere, the department will never hear of it and the algorithm that weeded them out will never come into question.
One also has to wonder how well a codified system will be able to find candidates for roles that we have yet to envisage, much less understand or codify, in this near, unpredictable future.
There is no doubt that AI and automation will become part and parcel of the toolkit in human resources, but that makes the human element no less valuable. As automation moves toward handling cognitive tasks that are normally handled by a human what we need to do is start being much more cognisant about cognition. We need to constantly evaluate our models of thinking.
Binary assessment methods may currently be a useful tool to speed up the selection process, however, we will need to rethink these methods in the face of increasing AI sophistication. Big data analytics can allow us to take a much more nuanced view of potential hires, just at it allows real-estate developers to take a more nuanced view of property.
We will need to expand our parameters beyond just a candidate’s paper qualifications, track records and references, into their non-business related activities as well. I’m not talking about spying on our candidates. I’m talking about understanding their passions, their motivations, their inner drivers. These things data may be able to collate, but only another human being can understand.
Your next star hire may not be someone who even graduated with the current specified credentials, but is a passionate, self-taught hobbyist who never bothered applying for a position because they just broke down traffic patterns, frequencies, and routes for fun and to get the best parking spot in the building every time.
Automation will allow us to do certain things much faster than before. What we need to work out is how we can do things better than we used to. We need to be smart with the data and challenge our assumptions to create better situations. Just as new technologies allow buildings to move beyond stone and steel and become living, breathing systems, these same technologies, if used creatively, can allow us to make the hiring process much more organic, personal and effective.
2017 is the year of the Fire Rooster. In Chinese astrology the Rooster represents confidence, hard-work, and a pride in perfection. Fire represents warmth, passion, and brilliance. But the fire rooster can also symbolise impatience. If we are impatient and plunge into the promises of the digital revolution without thinking, we may end up getting burnt. But if we take pride in perfecting how we work with this new technology we could facilitate a closer, more connected world, fuelled by passion and brilliance…and wisdom. Not just data.