We hear so much about artificial intelligence (AI) these days, but many leaders are at different levels in terms of understanding what it means for business. What do they need to know?
MICHAEL ZERBS: First of all, I want to be clear about something: AI is already here. You are using AI whenever you type a message on your iPhone and a word gets auto-completed; whenever you type a word into a search engine and it magically completes itself; and whenever you use Google Translate. These are all AI applications, and they share two key characteristics.
In each case, it’s about a machine/agent perceiving its environment. If it were a real, natural intelligence such as you perceiving the environment, that would entail using your eyes and ears; but because it’s a machine doing the perceiving, it uses sensors and algorithms. Once the environment is perceived, the agent takes an action that is orientated towards a distinct goal. That goal could be ‘completing the word that you had in mind’ when you started to type; or it could be ‘autonomously driving a car from point A to point B’ in a reasonable amount of time, without causing an accident. So, the key characteristics of AI are that a machine perceives the environment and then takes actions to optimize a particular goal.
Leaders should also be aware of machine learning — a branch of AI that ‘creates’ intelligence by learning from data. It’s a way to take a lot of data and extract useful information to help us achieve a goal. Computers have become extremely powerful: Things that were once theoretically possible were only possible if you were willing to wait for a very long time; all of a sudden, you can do these things very quickly.
Tiff, the University of Toronto — and the Rotman School in particular — have done some interesting research around the economic consequences of AI. Can you talk a bit about this?
TIFF MACKLEM: What is so interesting about AI is that it’s not an invention like, say, insulin. Insulin was a hugely important innovation that has massively improved the quality of life for diabetics — but it’s not something that you can take and apply to all sorts of things. AI is more of a ‘general purpose technology’: It has wide-ranging applications, and we are only just starting to see what those are.
Like any disruptive technology, it dramatically drops the cost of something — and in our view, that something is prediction. Take the analogy of computers. What they dramatically reduced the cost of was arithmetic — and as a result, things that involved a lot of arithmetic were quickly automated.
We used to take photos on analog cameras using Kodak film, then take the film to Blacks to be developed via a chemical process. One day, someone said, ‘You know what? Given that arithmetic is so cheap now, maybe we could produce photos digitally’. All of a sudden, we were taking more pictures than ever before — and Kodak and Blacks went bankrupt.
As in the case of computers, with AI, the initial applications we are seeing are very obvious things. So, based on your previous patterns, Netflix uses AI to predict which movies you might like to watch, and Amazon uses it to predict which books you might want to buy. These applications are handy — but hardly transformational.
However, we are starting to see more meaningful applications. For example, in healthcare, AI applications are dramatically dropping the cost of diagnosis. Say you notice a new mole on your arm: Is it just a sun spot, or is it a melanoma? You can now take a picture with your phone and an AI algorithm can tell you. That is going to lead to better health outcomes. The way to think about AI is, it’s a very powerful prediction engine — and the uses we’ve seen to date are just the tip of the iceberg.
Tiff, within five years, the Rotman School’s Creative Destruction Lab (CDL) has far exceeded your expectations. Can you give us some background on it, and some idea of what is coming down the pipeline?
TM: Economist Joseph Schumpeter was one of the first to think deeply about the process of innovation. He coined the term ‘creative destruction’ to capture the idea that innovation creates new inventions that improve our lives; but at the same time, it can destroy a lot of value and put people out of work.
We have great science in Canada, but historically, we have done a lousy job of commercializing it and reaping the economic benefits. Too often, the pattern goes something like this: Canadian scientists invent something amazing, and an American entrepreneur develops it into a product and reaps all the economic benefits.
Rotman Professor Ajay Agrawal’s founding vision for CDL was to attract really promising deep science-based ventures, and connect them with some of Canada’s most successful entrepreneurs to resolve the failure in the market for judgment. They come in and volunteer their time, and we connect them to promising science-based ventures.
This formula has been hugely successful: Five years ago, we set a goal of the ventures going through creating $50 million dollars of equity value. Today, we’re closing in on $1.5 billion dollars of equity value created.
Can you describe what these ventures look like?
TM: In the beginning, many ventures used AI to predict some sort of fault or malfunction: Think of possible problems with cars, planes, trains, drones, pipelines or any kind of big machine. How many times have you gone to the airport gate and you hear, ‘We have a mechanical problem; there’s going to be a delay.’ This costs the airlines billions of dollars every year, and it’s a huge inconvenience for travelers. If they could do a better job of predicting these problems, air travel would be much more pleasant and safe, and the airlines would dramatically drop their costs.
At CDL recently, we’ve been seeing all sorts of applications in healthcare, including new types of diagnostics that are up to 100 times cheaper than what we use today, and applications for more personalized medicine. The reality is, people react differently to different drugs and treatments — and AI can predict how, say, a particular cancer patient will react to a certain treatment.
Michael, Scotiabank is one of the CDL’s partners, but you are also involved with the Vector Institute and NextAI. Tell us about the strategy for these partnerships.
MZ: In terms of strategy, the main reason for our partnerships is simple: Gaining access to new ideas. Even though many AI ideas don’t directly relate to finance, you can often look at, ‘What are these entrepreneurs and scientists trying to achieve?’ and figure out the ‘finance equivalent’ of that. It could be around finding anomalies, detecting patterns, or just reducing the cost of prediction at some level.
The second point is equally important to us: There is a massive talent shortage right now, in terms of people who understand AI and can apply it. At the scientific level, how do you practically apply deep learning algorithms? And at the business level, once you’ve got the tool, how can you use it in a transformational sense? We thought, we can sit here all day and complain about all the change taking place — or we can team up with great institutions like Rotman and initiatives like NextAI and do something about it. Canada has a great opportunity to be a leader in the AI realm, and we want to be part of that.
SMEs [small-to-medium-sized enterprises] are an important component of our economy. Given that they don’t generate or have access to huge amounts of data, how can they embrace AI?
TM: On the one hand, for companies that have large amounts of data, there are huge economies of scale and network benefits to be had. Just look at Google or Alibaba: These are unbelievably data-intensive companies that are thriving thanks to AI. On the other hand, different types of digital technology are benefiting
SMEs. For example, Cloud computing. At one time, if you had a small business, you had to purchase your own servers, but today, you don’t have to do that — and as a result, these companies can scale themselves much faster than in the past. Also, in a digital world, you often don’t need to build a factory, and you can access global markets directly by selling online.
It’s still early days for AI, but as it becomes more mainstream and gets packaged and sold to businesses, there will be ways for SMEs to leverage it. For example, one of the biggest prediction problems for a small business is, predicting your cash flow, and there is already a company out there building an AI engine to do
that flow for small businesses. That is a well-defined prediction problem: A small business doesn’t have massive amounts of data — but it does have all of its financial information for the life of the business. So, there will be applications for SMEs. Obviously there’s an entry cost, and you need to look at whether you can
partner with new ventures to accelerate your progress; you don’t have to build it all yourself.
We would be remiss if we didn’t touch on human capital. Tiff, what are your thoughts on how AI is going to affect jobs and competencies?
TM: If you believe, as we do, that AI is dramatically dropping the cost of prediction, this means that jobs involving a lot of prediction are going to see declining demand and lower wages. On the other hand, jobs that are complementary to prediction will do well. In the realm of healthcare, if you work as a radiologist, spending most of your time looking at x-rays, very soon, AI is going to be able to read x-rays faster and more reliably. But, if your job is to care for those people, or figure out what treatment they need next — those skills are only going to rise in demand.
I don’t want to minimize the disruptive effects that AI will have on society. If we see really rapid progress, it will have serious implications. We are already seeing this play out in the world: Which two countries have the highest levels of inequality in wealth distribution? The U.S. and the UK; and the consequences of that are the election of President Trump and Brexit. The consequences have nothing to do with the problem, really; but they are features of the anxiety lots of people are feeling. And, we shouldn’t kid ourselves in Canada: We do have a more redistributed tax system, but we’re seeing the same trends and the same anxiety. As a society, we need to manage this a lot better.
Michael, how do you find a balance between innovation and risk management?
MZ: I strongly believe that at the end of the day, automation will always reduce risk, because the majority of risk we see in practice relates to human error — and ultimately, automation reduces human error. The idea of testing and learning is extremely important. By trying out different things early, you will catch mistakes earlier.
Traditionally, organizations — particularly large ones — have a mentality where they try to define everything up front, build it, and roll it out. We just expect that our customers will like it, and that everything will work properly; but usually, there are challenges. It’s much better to get customers in within weeks of starting something to obtain early feedback, and run the first algorithm within weeks of developing the model. The big takeaway for me is that the potential of AI outweighs the risk.
Tiff Macklem is Dean of the Rotman School of Management. He also chairs the board of the Global Risk Institute and Ontario’s Panel on Economic Growth and Prosperity. A member of the Asian Business Leaders Advisory Board, he was formerly the Senior Deputy Governor of the Bank of Canada. Michael Zerbs (Rotman MBA ‘89) is Chief Technology Officer at Scotiabank. Prior to his current role, he was the bank’s Executive Vice President and Co-Head of Information Technology, helping to launch its Digital Factory. This article summarizes a discussion that took place at the Chartered Professional Accountants of Ontario’s Leadership Conference in May 2017. Interview moderator Richard Piticco, CPA, CA, is Vice President, Student Services at CPA Ontario.
This article appeared in the Winter 2018 issue of Rotman Management Magazine. Published by the University of Toronto’s Rotman School of Management, Rotman Management explores themes of interest to leaders, innovators and entrepreneurs. To subscribe: www.rotmanmagazine.ca.
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