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To make the most of AI you need to think in terms of
what computers are good at and what human skills can

be used to leverage those capabilities. We need each
other, to be the best we can be, at what we are good at.

College in 1956) is now possible. And critically, because We need each other, to be the best we can be, at what we
of the way AI works (essentially recursively—the more you are good at.
know, the more you can know; and the faster you go the
faster you’ll be able to go), over the last five years in partic- Once your thinking is clear about what AI is good at you
ular the pace of development has actually gotten faster. need to look for use cases. What tasks do you or your
company perform that are ‘Structured, Repeatable or
Which means that, today, AI is very good at: Predictable’? Last year the RICS released a report saying
• Understanding what is happening in pictures and that 88% of the tasks surveyors perform are vulnerable to
videos being automated within 10 years. In 2016, McKinsey said
that 49% of ALL tasks people are paid to perform globally
• Understanding people using language are capable of being automated by ‘currently demonstrable

• Creating content (auto generated commentary, technology’. So the answer is probably ‘quite a few’.
extracting data from reports, news etc.)
So list them out; what do you do that follows a pattern in
• Automating processes
some way? Either because they absolutely do involve linear
• Optimizing complex systems processes, or because ‘with these inputs you know you can

• Making predictions produce those outputs’. You’ll find workflows or processes
from across the entire lifecycle of real estate, from planning, to
Essentially, anything that is ‘Structured, Repeatable or design, construction, leasing, occupation and management and
Predictable’ can (and most probably will) be automated. on to portfolio analysis and investment sales or acquisition.
Think of all of this as pattern recognition and you’ll get to
grips with what is possible. What imagery or video content do you deal with, or what move-
ments around space would be useful to track? What custom-
In terms of what AI cannot do, the key point to understand ers do you interact with and how much do you know about
is that computers operate within very specific boundaries. them? What reports do you have to generate and how are they
Google’s Alpha Go (that beat grandmaster Lee Sedol at the put together; could these be automated? How do you handle
famously complex and complicated Chinese board game Go) questions from customers; do these follow a pattern? Etc.
would be completely useless if you used it to play chess.
Once prepared, you can move to the third stage. Take your
It is true that Machine and Deep Learning—both of which list and give each item two scores of 1 to 10. First, how
are subsets of AI, and are differentiated by being systems common is this task in your business; and secondly, how
that are not explicitly programmed to do A then B then C or common is it outside of your business, either amongst
D if X = Y—can to an extent ‘think for themselves’, but they customers or competitors? You are looking for tasks that
can only do this within very narrow boundaries. Computers are repeated over and over again. What are the primary
are great at optimizing complex systems because that ‘Jobs to be Done’ in your area of real estate? This is a proxy
plays entirely to their strengths; but ask a computer to for market size in terms of the potential AI service that
create something completely new that turns out to be addresses each task. We’re looking to use AI to help us do
complex and it will fail. tasks much faster than now, or much cheaper, or more
accurately—so it is sensible to look for those tasks we, or
Even in an ‘AI first’ world, as Google boss Sundar Pichai our customers, have to do a lot of.
said we were entering last year, a human ‘augmented’ by
a computer will trump any computer on its own. By the Now we need to look at data. How much of it about each
same token, a human/computer pairing will also trump a task do we have? Here you should think of the four V’s:
human on their own. To make the most of AI you need to Volume, Variety, Velocity and Veracity. How much data do
think in terms of what computers are good at and what we have, from how many different sources, how fast is
human skills can be used to leverage those capabilities. Continued on page 67

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