AI and Real Estate

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My presentation, delivered at the Future: PropTech Conference in London on the 2nd May, 2018. 15 minutes.

Or if you prefer to read it .......

Ok, so we are going to talk about Use Cases for AI

The what, the so what, and the now what

To start off I’d like to show you what the 1st industrial revolution felt like to Victorians. This is Rain Steam Speed by Turner, painted in 1844, just after the opening of the Great Western Railway.

Steam engines transformed Victorian society as dramatically as AI is going to transform our society. The future feels like it is coming at us fast and everything is a bit of a blur.

But the excitement is palpable - we are lucky to be living in such fast moving times.

And fast moving they really are.

In January last year McKinsey wrote that 49 percent of all activities people are paid for could be automated by currently demonstrated technology.

Not technology from the future, technology that is available today.

The RICS reported that 88% of the core tasks performed by surveyors could be automated over a ten year period.

So... what is happening to generate all this change?

Well the answer starts here. This is Moore’s Law, where essentially computing power doubles every two years, something which has held true for 50 years so far.

So you have 100 thousand transistors on a chip in 1980, 100 million by 2000 and 10 billion by 2016.

This is the archetypal example of exponential growth, the famous hockey stick we hear so much about.

But, compared to the chips that originated in gaming machines but that are now the primary processing units behind AI, this Moore’s Law growth is rather paltry.

Moore’s Law is shown in the two aneamic lines at the bottom. The power of GPU’s has been increasing dramatically faster over the last few years.

Which is why neural network training, which is fundamental to machine learning, became 60 times faster in the three years from 2013 to 2016, with most of that growth in one wonder year, 2015. 

So what does this lead to then?

Well for one thing it means computer vision, face recognition etc has gone from being useless to a utility in just a few years.

With error rates of 28% in 2010 it was a pretty useless technology. By 2017, the error rate was down to 2.2%, far better than the 5% which is how humans perform.

And unlike humans, who can only process a few images at a time, computer vision enables 200,000 faces to be recognised in real time.

Likewise speech recognition has gone from useless to utility. In fact it has done so considerably faster than computer vision.

In 2013, we saw word/error rates of 23% - just four years later this was below 5%, again the human level of achievement.

Which is why we have seen all of a sudden the rise of smart speakers like Alexa, and the growing use of Voice as a search interface

It is what happens when new technologies become utilities

So, ALL businesses can now exploit 6 new technological capabilities, made possible by the growing power of AI.

Many more processes can now be automated

We can understand what is happening in pictures and videos

We can optimise complex systems in a manner that was not possible before

And we can automatically generate voice and textual content

And we can automatically understand people using language and make predictions.

Within the real estate industry we have mapped these new capabilities as applying across 17 areas or workflows.

Much of the day to day requirements of the industry can be improved using AI 

From investment strategy, to asset monitoring, to customer experience to demand optimisation. The possibilities are almost endless.

But, with AI, there is a catch.

Unlike previous technologies, where often the best policy was to let early adopters make all the mistakes and lose a lot of money, with AI you cannot be a fast follower, and ride on the coat-tails of innovators.

You have to be the innovator

Because AI is all about data and the training of systems. Machine learning means just that - the machines learn from experience and that is a step you cannot buy your way around.

Experience matters in AI. And the rewards go to those who have learnt the most. 

So, what then do we do about all of this? How do we apply these powers to real estate?

Well, across real estate there are a number of key things we all have to do.

But really everything boils down to making predictions, and AI is dramatically reducing the cost of prediction. Who will buy, who will sell, what happens if this, what happens if that. What are the chances of etc etc etc

And with better and cheaper predictions, the amount of uncertainty we have to deal with reduces. And less uncertainty is a great enabler. We can take more risks, at less risk.

Here are 9 key Use Case areas for AI in real estate

Computer vision is a powerful weapon. Being able to point a camera at the built environment and to understand what is being seen is a superpower.

But people also like interacting with images. They are comfortable with images. Real estate search using images rather than text will become a big thing.

The internet of things and smart buildings and smart cities, are going to allow us to optimise ‘the space around us’ in unprecedented ways.

Not least of all we are going to be able to optimise our workplaces so that they at last allow us to be as productive as we can be.

All around the world there are now thousands upon thousands of small low altitude satellites and these are allowing us to monitor, inspect and analyse the world in great detail.

We can merge satellite and aerial imagery with other data sets to aid our understanding. From daily monitoring of development sites to checking how full car parks are at shopping centres, there is a lot you can do when you can analyse imagery.

AI is extremely useful in synthesising disparate data sources and finding correlations and causations between them. Automated Valuation Models will become more and more accurate.

And a sidenote on that: As we have said AI can be a winner takes all technology - as with web search we probably only need one automated valuation model in each asset class.

Chatbots are a key AI tool. For anything deeply structured allowing people to text for help or assistance makes a lot of sense. We’ll see a lot of chatbots in property management and customer support.

Location, location, location is of course a real estate mantra but historically we have not really known that much about any given location. AI will allow us to become far better informed about the precise characteristics of locations, and the wants and needs of the people who live, work or shop there. 

Voice as a search interface is now becoming commonplace with the likes of Alexa. Expect to see a lot more people talking to computers rather than tapping away at keyboards.

Natural language processing is all about understanding text, and once that becomes a solved problem, all manner of interactions we currently have with text will be changed. Expect to see a rapid decline in human input into report creation for example. Certainly in a business context most reports can be boiled down to a templated format, and auto generated by linking in various data sources.

And finally marketing. Probably the No1 use case for AI across all businesses. Marketing is all about knowing your customer and AI, in various ways, is going to enable us to be much much better at that. Real estate has a lot to learn here. Those who bother to learn will reap big advantages.

So, nine areas where you will find solid use cases for AI in real estate

Here are 70 companies already ready to help you out.

All of this is very exciting but how do you ensure you remain on the right side of this change, and don’t lose your job to an AI?

Well first off you have to remember that Picasso, of course, was right. Computers ARE useless - they can only give us answers.

We are here to put the questions.

This is Gary Kasparov, the great chess master, and also the first world champion to lose to a computer, in his case IBM’s Deep Blue in 1996.

Since that day it was clear that humans would no longer beat computers at chess.

So Gary started a new form of chess, called Advanced Chess. In this a human, sometimes a pair, plus a computer would take on another computer plus a human or a pair.

What transpired was remarkable. First off a human + a computer always beat the computer on its own but also the team who managed the relationship between human and computer, regardless of strength, mostly came out on top.

As he says “weak human + machine + better process was superior to a strong computer alone and, more remarkable, superior to a strong human + machine + inferior process.”

Understanding how to best leverage the different skill-sets of humans and machines is the killer skill.

This was Steve Jobs at the launch of the iPad 2 in March 2011, and shows his famous marriage of ‘technology and the liberal arts’.

“It is in Apple's DNA that technology alone is not enough—it's technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.”

So, we have four steps to ensure your job is not taken away by an AI

Step 1 - DO NOT stay in a job that is ‘structured, repeatable or predictable’

This is from the Jack Lemmon film, The Apartment in 1960, and in those days offices were essentially Excel - every desk was like a cell in a spreadsheet where the person working at it was doing something ‘structured, repeatable or predictable.

If your job is anything like that, start planning your exit.

If your office is even anything like this definitely leave your company asap.

Step 2 - You need to learn how to think about AI

You need to Understand what AI is good for, and what it is not good for. Understanding where it can be applied is absolutely foundational and will avoid wasting a lot of time.

You need to look for those use cases - the 88% of core tasks from the RICS report, and the 49% McKinsey referenced.

Amongst those you need to consider which ones are commonplace and repeatable within your own business and/or applicable to many of your customers?

Then you need to think about what data you would need to feed your AI initiatives and whether or not you either have this data, or can acquire it in one way or another

You need to consider whether there are clear metrics by which you could judge success? Is success measurable?

And then finally, if you have all of the above, is that something that will create significant value?

If you get to the end of this with all the right answers then you have a product or service worth pursuing.

If you are looking to build AI solutions for customers then your product or service must abide by these rules.

Thirdly in a world of exponential technology you need to become exponentially human. You need to develop all your human skills - of imagination, empathy, design, social intelligence, problem solving, judgement etc - because that is your value.

Do not try and beat the machines at what they are good at - don’t bring a knife to a gunfight. Instead work on how, with your understanding of AI, you can use it to augment your own human skills.

As prediction becomes cheaper, the value of judgment will increase. All this technology is an input: we are responsible for deciding the output we desire. Those with great judgment, who can weigh up upsides and downsides of predictions will see their value rise.

AI will give us more options: how we exploit them will be down to human judgement.

And think ‘education, education, education’ - in a fast changing world education is a marathon, an ongoing process. You will never run out of new skills to learn.

And then finally you need to deeply consider how AI/Data and all technological change is going to change the world.

There will be winners and there will be losers. Oftentimes each of us will be both. We will lose some things but gain others.

Mostly it is tasks that will be lost rather than entire jobs but there certainly will be many jobs that do become redundant.

There will also be a lot of real estate that becomes redundant: as the work we do changes the nature of the office is going to change fundamentally. Where and how we live as well. And as for retail, well there is much change coming there.

Pay attention to those four rules and your job will not be overtaken by an AI. In fact you will most likely thrive in an AI world.

But either way remember that in business some things don’t change. 

The bear is coming but you do not have to be perfect, just better than your peers.

Tweetstorm on OneMarket - Genius move or value chain disrupter? Both

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Setting the scene: 'Westfield’s $300M PropTech Spinoff Wants To Give Physical Retail More Power And Data Than Amazon' - https://www.bisnow.com/national/news/retail/westfields-300m-proptech-spinoff-wants-to-give-physical-retail-more-power-and-data-than-amazon-87627

A Tweetstorm re the above

OneMarket spin-off from Westfield is fascinating. And seriously techie. But a two edged sword for ‘customers’. Extensive participation WOULD make EVERYONE better off BUT would also make everyone beholden to OneMarket, who would have great pricing power.

Think Facebook level power within the retail sector. They simply know much much more than you do and you cannot not use them. Hence almost no advertisers have left Facebook.

Big question is whether the large incumbents, @BritishLandPLC @Hammersonplc@LS_Retail are large enough to generate this intelligence on their own?

Large datasets are required for AI but the impact flattens beyond a certain point. Are the incumbents big enough?

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Not controlling your own data is the fastest way to fall down the value chain. Incumbent retail landlords IMHO HAVE to go all in on data to stay relevant.

Some go on about being ‘data led’ and then you find out they’ve done a bit of demographic analysis and have footfall stats... you need much more than this.

Who controls your network/ecosystem data will determine who makes the most money. OneMarket could be huge. Just like Rightmove makes the best returns without even being an estate agent.

Might also be why Amazon buy Hammerson, just as Alibaba have bought up shopping centres in China. They have the data and ‘own’ the customer.

For years argued that shopping centres should position themselves as the ‘gateway to a world of wonder’ by implementing deep and extensive curated apps for their centres. Always got kickback that ‘tenants wouldn’t like that’.

But one app that embraces every outlet in a Center, built on deep customer data, would be very powerful. The value proposition to users could be very strong. Instead....

... what you get in most Center apps is 10% off doughnuts or coffee, poor quality images, undifferentiated offers, and zero personalisation of any quality.

It won’t be long before Amazon start shipping us stuff before we’ve been shopping because they ‘know’ what we want. Just return what you don’t want.

What does a shopping Center actually know about their customers? Not nearly enough. If OneMarket flies they could know a very great deal. But....

... who will benefit from that knowledge. Again, think of Facebook. And beware?

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Just a thought:)

Antony

Real Estate as a Service: Are flexible short term leases the new future? 16 Provocations!

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Last week I took part in a round table panel discussing whether short term leases were the new future for real estate. My role was to provoke debate. So below you will find my quick overview of the market and then 16 ‘Provocations’.

One panel member disagreed with ‘at least 10’ of these. IMHO because they were on the wrong side of the trend….

What do you think?

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There appears to be a ‘perfect storm’ building for the traditional real estate industry. 

A world of solid assets, underpinned by long term leases from tenants with strong covenants and long histories, was the norm. Sure there were property cycles, but so long as you did not get your timing terribly wrong, your assets would increase in value, the leaseholder was on the line to fully repair and insure your buildings and in practice you did not even have to engage much with your customers. Once that lease was signed, they were stuck with you. Largely, one could sleep soundly, in the knowledge that tomorrow was going to be much like today.

That world is blowing up, and blowing up fast.

The average age of an S&P 500 company is under 20 years, down from 60 years in the 1950s, 

Today, 40% of the top 20 global companies are tech companies. 100% of the top five.

Ten years ago three of the top five were oil companies with just one tech company, Microsoft

And they do not employ many people - Google, Apple, Facebook only employ 225,000 people. Amazon employs 500,000 but most of those are low paid warehouse jobs.

Between them they are worth nearly $3 Trillion dollars

In January last year McKinsey stated that “Overall, we estimate that 49 percent of the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology.”

In a similar vein last year the RICS published a report saying that “Surveying appears to be an industry in which 88% of the core tasks are ripe for automation to a greater or lesser degree.”

Various reports have estimated the % of jobs set to be lost to automation over the next two decades, ranging from 14-47%. Whatever way you cut it a lot of work is going to be automated away.

But that is not really the important bit. Regardless of how many jobs, in their entirely, are lost, the nature of the work we do is set to change fundamentally. Anything ‘structured, repeatable or predictable’ is going to be automated. AI is advancing at such a rapid clip that this is likely to be sooner rather than later.

Seth Godin wrote a blog post entitled ‘Goodbye to the Office’ in 2010 in which he suggested that ‘work was leaving the building’ in that everything we used to need an office to provide us with, in order to be able to do our work, was becoming redundant. We have laptops, the internet, the cloud, connectivity everywhere, supercomputers in our pockets. The only thing left he wrote was ‘I need someplace to go.’

And that is why the genie is out of the bottle:

  • We do not need as much space as we used to
  • We do not need an office to actually do our work
  • The work we do is changing entirely anyway
  • The type of space that we need therefore is entirely different
  • A bland shell is no longer acceptable 

Add on top of this that the majority of occupied units are small (in the City of London 70% are less than 10,000 sq ft and 50% are less than 5,000 sq ft) and you start to see that the Product/Market fit of many of these spaces is breaking down.

In a world where the ‘structured, repeatable and predictable’ work is being done by machines then the work we all need to do, when together, is going to be intensely human, probably involving design, imagination, empathy, social intelligence and the like, and in that case the types of spaces we need are much more varied, softer, more engaging, more collaborative, and more specifically tailored to the exact type of task we need to do. i.e much more the Activity Based Working type of arrangements that we are all becoming familiar with.

The problem therefore, for perhaps at least half the occupiers of office space, is that they:

 A) do not have the skills, the time or frankly the inclination to get to grips with this much more complicated type of workplace and 

B) …even if they did, with half the occupied units less than 5,000 sq ft, they don’t have the space to economically create such a place.

So, the status quo is to remain where they are, taking on a lot of space they either do not need, or that does not help them to be productive. They have expensive employees, needing to do tasks that involve high order human skills and they’ve got them tied to fixed desks, with fixed phones and probably even fixed computers. Is it any wonder that in the Leesman Index surveys only 50% of respondents say their workplace helps them to be productive. Or that, for on average 50% of the time desks are unoccupied.

I call it the 50/50 double fail.

And into this double fail we have seen the rise of the co-working spaces, with the $20 billion monster that is WeWork leading the pack (and today representing London’s largest tenant). But it is of course much more than WeWork, with operators as varied as Fora, Huckletree and The Office Group appealing to different audiences. 

Flexible space may only account for 4% of the total space in central London but it is growing fast, and with roughly 18% of total take up last year being flexible, it does seem like this is an idea whose time has really come.

Perhaps the biggest driver of adoption today is pure familiarity; as more and more people experience office space that is not as dull, dreary and downright enervating as so many of us have endured for decades they realise that ‘it does not have to be this way’. And that feeds back into demand. Want the best staff? Give them the best space.

Critically it is not money that is driving this change, or at least not in a conventional sense. You pay more, sometimes much more, per sq ft for flexible space but the flip side is you are actually buying true #SpaceAsAService - space that provides you with the service you need, as and when you need it. That trade off seems to work for very many people. 

After all: ‘Companies do not want an office, they want a productive workforce.

So, the provocations are:

  1. Flexible, short term space, for much of the market, is the future. The standard, traditional office ‘Product’ is no longer fit for purpose. It no longer has ‘Product/Market’ fit.
  2. The Real Estate business is no longer about Real Estate: the industry has to movefrom selling a Product to delivering a Service
  3. No REIT has a ‘Brand’ designed for this future. they are all Brands aimed at investors, not customers. 
  4. The Real Estate Customer is now every single person who enters into a property - Real Estate is moving from being B2B to B2C.
  5. As with B2C markets, Landlords/Investors will need to be thinking ‘lifetime value of customer’ - how to build a Brand that can support a customer from 25 - 65.
  6. Traditional Real Estate companies know almost nothing about their customers - in the future the best will know a very great deal, and use it to shape personalised #SpaceAsAService for each and every one.
  7. Landlords HAVE to choose a role: A) Outsource ‘Service’ to an operator like WeWork, and leave a lot of money on the table. B) Turn themselves from Product to Service companies: with all the cultural, operational, structural and financial change that entails or C) Partner closely with an ecosystem of providers who can help them build a strong Brand and value proposition
  8. In the future UX = Brand & Brand = Value
  9. Whoever is the curator of the user experience reaps the highest reward
  10. Landlords need to offer more than a ‘Workspace’, they need to offer high quality ‘Workplace’ solutions - ‘sell me a productive workforce, not an office’
  11. Buildings need operators who understand engineering, technology and data and are equally skilled in hospitality and the curation of customer experience. These skills are at the top of the value tree. 
  12. The best, most profitable Landlords of the future will embrace all these skills.
  13. Valuations will be based on rolling income histories - the Operator makes or breaks an asset. 
  14. Bond like valuations based on long leases and covenants will exist, but will bevery low yielding investments as the demand for ‘old school boring income’ will be intense.
  15. The Real Estate market will become barbell shaped. You will either be a winner or a loser, not much will exist in the middle. Traditional landlords, only skilled in physical real estate will not be winners.
  16. Real Estate companies need to become technology companies and treat their buildings like iPhones; marriages of hardware and software, and services.

So, there you go. 16 Provocations. I do hope they 'provoke'!

Antony

Will Artificial Intelligence eliminate Real Estate jobs? Part 2...

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In Part 1 we discussed how parallel memes, that technology is developing at a phenomenal pace and will enable widespread automation, and that humans need to become skilled at augmenting themselves with complimentary technical skills, are the key to answering the question of whether AI will eliminate CRE jobs. Now we will look at how to make sure it is not your job that AI eliminates.

There are four steps.

First, DO NOT stay in a job that involves tasks that are largely ‘structured, repeatable or predictable’. This extends beyond whether they are simply ‘1+1 = 2’ but includes anything where the inputs are the same each time, in general terms even if the specifics vary, and the outputs are also the same. If the task does not follow a pattern and/or requires judgement during the processing then you are probably ok, but if not…… watch out.

Over time AI will move ever higher up the ‘cognitive stack’. Machine Learning means just that; the machine learns through experience, so whilst a decade ago AI largely involved tasks that needed to be explicitly programmed, going forward this is becoming less and less true. Just think of a self driving car. There is no way every possible eventuality could be encoded into a system, so the AI must try and intuit ‘what would a human do’ in any circumstance. They are learning that by analysing millions of hours of driving by real people. 

Secondly you need to learn how to think about AI. In 6 steps.

  1. Understand what AI is good for, and what it is not.
    Knowledge, reasoning, planning, perception and communication are the five key areas that AI researchers have been working on since 1956. Reduced down to an essence you could say AI is about reducing the cost of prediction. What is the probability of X happening?
  2. Look for use cases’: 88% of the core tasks are ripe for automation’ said the AI report from the RICS last year. Over a ten year period admittedly but in some form or other those will be ‘structured, repeatable or predictable’ tasks. Look for the ones with the highest value first, or where your competitors are automating but you are not.
  3. Are these commonplace and repeatable within your own business and/or applicable to many of your customers? This is a numbers game to an extent. Find the tasks you can automate that are most common, or take up the most time. Not automating tasks that can be automated is just a waste of time.
  4. Do you have or can you obtain large quantities of data? AI does require data. Systems need to be trained and then tested, and that requires data to train on and data to test against. Humans learn by experience, so do machines. Data is what enables this.
  5. Are there clear metrics by which you could judge success? Working with data often throws up correlations that are not causations. You need to be able to assign metrics to outputs. You cannot learn if you cannot measure.
  6. If you have all of the above, is that something that will create significant value? Just because you can do something does not mean you should. Does the effort involved in creating this AI have significant value? If not, probably best to think of an easier solution. AI is not a trivial pursuit, so make sure it is worthwhile.

Thirdly in a world of exponential technology you need to become exponentially human. You need to develop all your human skills - of imagination, empathy, design, social intelligence, problem solving, judgement etc - because that is your value.

Do not try and beat the machines at what they are good at - don’t bring a knife to a gunfight. Instead work on how, with your understanding of AI, you can use it to augment your own human skills.

As prediction becomes cheaper, the value of judgment will increase. All this technology is an input: we are responsible for deciding the output we desire. Those with great judgment, who can weigh up upsides and downsides of predictions will see their value rise.

AI will give us more options: how we exploit them will be down to human judgement.

And think ‘education, education, education’ - in a fast changing world education is a marathon, an ongoing process. You will never run out of new skills to learn.

And then finally you need to deeply consider how AI/Data and all technological change is going to change the world.

There will be winners and there will be losers. Oftentimes each of us will be both. We will lose some things but gain others.

Mostly it is tasks that will be lost rather than entire jobs but there certainly will be many jobs that do become redundant.

There will also be a lot of real estate that becomes redundant: as the work we do changes the nature of the office is going to change fundamentally. Where and how we live as well. And as for retail, well there is much change coming there.

Pay attention to those four rules and your job will not be overtaken by an AI. In fact you will most likely thrive in an AI world. Either way though it is always wise to remember that it is not perfection you are seeking to attain: what you need to ensure is simply that you are better than your peers.

The AI juggernaut is coming, just make sure it is not you it flattens.

Antony

Will Artificial Intelligence eliminate Real Estate jobs? Part 1...

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Vicky Pollard from the comedy classic Little Britain was famous for saying, very fast,“Yeah, but, no, but, yeah, but, no, but…”, and that is exactly the answer to the question of whether AI will eliminate real estate jobs. AI is unlikely to eliminate many entire jobs, but it is highly likely to eliminate a whole raft of ‘jobs to be done’ from the day to day lives of us humans.

In early 2017 McKinsey wrote this: “Overall, we estimate that 49 percent of the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology.” And in July 17, in a report, published by the RICS no less, it was stated that ‘The Impact of Emerging Technologies on the Surveying Profession’, the RICS “Surveying appears to be an industry in which 88% of the core tasks are ripe for automation to a greater or lesser degree.”

What is going on?

Artificial Intelligence may have been first discussed way back in 1956 but it is only in the last few years that it has really morphed from the world of science fiction to become a mainstream technology. A confluence of improved algorithms, the recent availability of vast datasets and exponential increases in computing power have seen to that. Computer Vision (the ability of a camera to understand what it is looking at) and Voice Recognition have both gone from useless to utility in a matter of years. For example, the best voice recognition system had a ‘word/error’ rate of 23% in 2013; today that is less than 5%, which is superior to humans. In just three years, from 2013 - 2016, the speed neural networks could be trained (critical to AI) increased by 60 times.

In 2016 Google’s Deepmind program AlphaGo beat the world champion GO player Lee Sedol. In 2017 the next iteration AlphaGo Zero beat the original 100-0, after just 3 days practice. It then beat Stockfish, the No1 Chess playing software, after only 4 hours practice.

Be under no illusion, as Sundar Pichai CEO of Google has said, we have now moved to an AI first world. And this matters hugely to the everyone in Real Estate. In our industry what really matters, above all else, is what our customers want and need to do within the spaces and places we create. Everyone has a ‘job to be done’ and it is that that AI is fundamentally changing. The bottom line is that anything ‘Structured, Repeatable or Predictable’ will be automated. Not might be, will be. The incentive to do so is too great to be ignored: regardless of whether that suits us as individuals. This is the 49% of activities that McKinsey refer to, and the 88% from the RICS report. The only difference is that McKinsey are referring to today whereas the RICS is taking a 10 year view.

So, if anything ‘Structured, Repeatable or Predictable’ is going to be automated, what does that mean for us? What are we going to do? The answer lies in what I like to think of as ‘New Work’ and that is anything that involves design, imagination, inspiration, creation, empathising, intuition, innovation, collaboration and social intelligence. These are the foundational human skills that, luckily for us, are the antithesis of what computers are good out.

The key to avoid your job being eliminated by AI is how good you are at marrying your intrinsic human skills with the extraordinary processing power of the machines we now have at our disposal. Ex world champion chess player Gary Kasparov started an alternative chess format called Advanced Chess, where one or two humans, assisted by a chess computer, play a similar team. What he discovered running various tournaments vindicated his hypothesis that a human paired with a computer would beat a computer on its own. But what he also found was more telling: a “weak human + machine + better process was superior to a strong computer alone and, more remarkable, superior to a strong human + machine + inferior process.”. Ultimately it was the skill of the human who could best understand how to marry human + computer that came out top. It is the creativity in defining the ‘process of augmentation’ that really matters.

Steve Jobs was famously not a techie; he studied calligraphy at college. So it is perhaps not surprising that he understood this need for humans and computers to work alongside each other. At the launch of the iPad 2 in 2011 he said this: “It is in Apple's DNA that technology alone is not enough—it's technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.”

These parallel memes, that technology is developing at a phenomenal pace and will enable widespread automation, and that humans need to become skilled at augmenting themselves with complimentary technical skills, are the key to answering the question of whether AI will eliminate CRE jobs. How to do this will be the subject of part 2 of this post.

Antony