The Age of Learning

Tim Hogarth
Sep 10, 2018 · 5 min read

One of the great things about being in Innovation and in banking is that it involves lots of new, unusual and exciting things. Almost every day I come to work, I’m involved in something different — every day, I get to learn something new.

Learning is a big part of innovation. We embrace all sorts of new technologies, ideas, companies, and techniques not just because we’re curious — we’re trying to learn. Learning is a vital part of how companies now need to operate given the backdrop of change. Unfortunately, learning at an institutional level is a pretty difficult to do once you enter the working world — and there’s a really good reason. It turns out that it’s actually about how we motivate, reward and structure teams that creates opportunities and encourages learning at this stage.

Scalable efficiency

Around one hundred years ago, the format of the modern corporation emerged. It’s quite obvious now, but large organisations came about as people realised how to exploit economies of scale to gain greater efficiency and then growth. Economists refer to this as “scalable efficiency” — essentially, as one scales up your organisation, you get great efficiencies.

Office buildings, HR departments, executive titles, business processes, expense policies, supply chain management, departmental budgets — these are all very familiar aspects of how most of us work. We’re part of a chain of skillsets that each does our part. The specialisation of each person’s job leads (mostly) to greater levels of efficiency. This is how cars are assembled, a McDonald’s burger is delivered into a customer’s hands, and how websites are built. Makes sense, right?

Where it breaks down

The only real problem — and it’s a big one — with scalable efficiency (outside of diseconomies of scale) is that it doesn’t work well in dynamic environments. It’s ideally suited to static, predictable environments where the rules and roles can be well defined and performed in a highly repeatable manner. Hence, the scalable efficiency models of the 20th Century are vulnerable as we move to a world where change and uncertainty have become far more prevalent.

The root of this problem is that a model built around scalable efficiency inhibits learning. When there’s a new way of doing something or a new technology that might give some benefit, much of the orgnisation’s rules discourage or outright prohibit exploring this avenue. Exceptions to a process tend to get lost, rather than instilled as new learnings. If an individual works out a new way of doing something, it’s uncommon for that approach to be folded into the organisation as long-term learning.

In a world where change is happening very quickly, we absolutely need the capacity to learn — not just as individuals, but as organisations. And it’s very hard.

Scalable Learning

Learning is the most important skill organisations need to gain in the coming decade — and we’re going to need to do some things very differently to achieve it. We’re going to have to reconcile how we build a deep-seated sense of curiosity in our leaders, so they are improving their own education on new technologies and questioning the status quo. We’re going to need to work out how we can step outside of our rigorous processes, but without taking undue risk. We’re going to need to work out how to absorb individual learnings into the organisational memory. We’re going to have to unlearn some things too. And as with all learnings, we’re going to have to accept some missteps, some failures along the way.

This is a very different organisational mindset from what we see today, and may seem incongruous with a large corporation. However, new-age companies are scrambling to move to this kind of model. Just look at Google’s model of “rewarding failure”, or Facebook’s “move fast and break things” — these are both deliberate strategies to build up their institutional learning. LinkedIn and Spotify employ a rapid iteration model focused on exploring new ideas. And Amazon established a culture of “institutionalised Yes” and a ‘bias for action’ to ensure they are bold and try new things.

The shift to this mindset is only going to accelerate as industry moves to an AI-first strategy, given AI is entirely based on the premise of learning. As an engineer at Google recently said, “rules don’t get better over time, but AI does.”

How we’re doing it here at TD

To reinforce the need and importance of scalable learning, TD created an Innovation Centre of Excellence. It sets the bank’s innovation strategy and framework required to grow a learning and experimentation culture. Here are a few ways we’re doing it:

Expanding our lab ecosystem

Beyond the TD Lab in Waterloo — our highest profile and most significant public investment — numerous innovation teams, labs, and partnerships have sprung up across the bank. There is a Digital Design Lab, the UGO experiment, a cybersecurity lab, a Blockchain team, a Payments Innovation team, a real estate lab, as well as our investments in the Rotman FinHub, the Western University Analytics Lab. All of these are very, very different models to doing things compared to the way we’ve done them before. They represent an enterprise-wide desire to tap into new ways of thinking, experimenting and exploring new ideas.

Employ new methodologies

More and more TD teams are using Design Thinking, for example. We’ve had more than 1,000 colleagues take a Design Thinking bootcamp through the TD Lab, and we’re running more human-centric design workshops as well. Our Lab has also begun hosting interactive Machine Learning workshops to help colleagues understand how it functions and apply basic concepts.

Our technology teams have also been embracing Agile principles to improve our speed to market, outcomes that generate real value, and driving predictable results in delivery.

Learn from outside sources

Across the bank, we meet with Fintechs all the time — not only because they might have a product or a service offering that can drive value for us, but because we can learn through partnerships. It’s new thinking, new inspiration, new ways of working.

Colleagues across the bank also regularly attend internally organised events that profile local tech companies in order to educate themselves on different technologies and broaden their awareness of the world around them.

Invest in emerging tech

As we move into a world of Artificial Intelligence, TD’s acquisition of Layer 6 and our investment in world class analytics demonstrates our appreciation of the value of learning is inevitably going to be institutionalised. AI is fundamentally about inferring unknown rules from large data sets — not using the rigour of an established process, rather new insights gleaned from observing the world. AI is effectively learning on a massive scale.

The journey continues

This is but a drop in the ocean of what we will do at TD. Building a culture that celebrates risk-taking and learning is something we’re still evolving.

We’re doing this because we sense that the world is changing around us: over 160 years of banking in branches has now tipped irrevocably to digital. Customers aren’t going to use cheques in the future. Mortgages may give way to other financial products. Banks will probably shift some of their focus towards new adjacent businesses. Our business is a very good one, but one of these days we will need to turn the ship towards different waters.

And before we need to turn, we’d better be able to learn.

TD Lab

TD is a top 10 North American bank, ranking among the world's leading online financial firms. TD Lab experiments with emerging tech to elevate the customer banking experience. Our developers, designers & interns embrace a culture of "learn fast & learn often". (

Tim Hogarth

Written by

TD Lab

TD Lab

TD is a top 10 North American bank, ranking among the world's leading online financial firms. TD Lab experiments with emerging tech to elevate the customer banking experience. Our developers, designers & interns embrace a culture of "learn fast & learn often". (

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