Business is changing thanks to technology and investors need to pick winners differently

Investors may need a new approach to evaluating business, thanks to new technologies such as AI.

Article updated: 24 May 2019 2:00pm Author: Michael Baxter

PwC has projected that AI will be worth $15.1 trillion to the global economy in 2030. Stop right there! That’s just over $15 TRILLION. In 2017 global GDP was $80 trillion, so you can see, AI’s impact will be immense.

It’s important to understand why.

It is partly about fear. The lessons of one-time market leaders terrifies corporate land. Blockbusters, Kodak, even the likes of Nokia and RIM — no one wants to be ubered.

Technology has lowered barriers to entry and handed opportunities to companies that are either start-ups, or were a few years ago, to break into markets that once were all but impossible to break into.

Take banking as an example, banks like Monzo and Revolut, which merely existed in their founders’ respective heads a few years ago, are breaking the dominance of the banking powers that had seemed unbreakable for so long.

No one is quite sure what shape the threat to their business will take. In order to survive, large companies need to start behaving like start-ups, technology is making this possible.

AI: An unfortunate acronym

When people see those words they think of science fiction, movies by Stephen Spielberg, Schwarzenegger come back from the future. In fact, those who work in business and at the sharp end of implementing technology, prefer the description Machine Learning. Machine learning is about data — by studying data — lots and lots of it — specific machines learn how to do specific tasks. Some machine learning algorithms learn how to recognise shapes — that’s image recognition. Others, study data formed from sound and learn how to draw insights from recordings of hundreds of thousands of phone calls made at a contact centre.

Look at it that way, look at the only version of AI that is out there right now, namely machine learning, and it loses its sense of science fiction.

It is merely the result of a convergence, namely faster processor speeds, the cloud meaning companies can access super powerful computers and algorithms without having to bring them in-house, and the enormous amount of data that is being created by technologies such as the internet of things.

But what machine learning does do is help companies experiment more — test products or ideas, if the tests don’t work, they try something else.

This approach is sometimes described as the lean start-up model. The essence of the idea is to breakdown an idea for a new product or service into simplified versions, which you can develop fast and then test. You simulate what the final product is meant to do, for example, and test it.

In this way, instead of working on a new product for years and in secret — the old way — and then launch it and hope for the best, the product development is broken down into discrete stages, tested at every stage. Monzo and Revolut applied this precise approach.

Monzo for example, began with a simple financial service wrapped around an existing pre-paid card, and presented it to a local market. The feedback enabled them to know what worked and what didn’t, then they added more features and tested it all over again. The bank was founded in 2015 by a technically literate young entrepreneur who had no experience working in banking; today it has around 750,000 customers.

Adapting to change

How do large companies deal with such threats? Answer: by applying the same approach, but creating multiple ideas and testing them in parallel.

For this approach, companies need to be able to respond rapidly to change. Technologies such as the cloud are vital, because you can ramp up or ramp down easily. Machine learning and data is fundamental.

But this technology is creating new challenges — there is the issue of data privacy, risks of cyber crime and the risks of AI interpreting flawed data and drawing undesirable outcomes. There are examples of AI becoming racist thanks to limitations in data. Bias in AI has become a major talking point.

The really forward looking companies are building considerations about privacy, ethics and security into the core of a product. GDPR, the EU’s data protection regulation, for example, introduces a concept called privacy by design. It means new products have privacy considerations built into it at core.

In this era of rapidly changing technology, an era when no company is safe from the next disruptive shock, look for companies that get this: find out about their digital transformation programme, their use of data, approach to privacy, security and ethics.

Apple’s privacy policy starts by stating that Apple sees privacy as a fundamental human right, there are many who see its reputation as a privacy guardian as its key strength. Look for companies that are similarly enlightened.

These views are those of the author alone and do not necessarily reflect the view of The Share Centre, its officers and employees

Michael Baxter portrait photo
Michael Baxter

Economics Commentator

Michael is an economics, investment and technology writer, known for his entertaining style. He has previously been a full-time investor, founder of a technology company which was floated on the NASDAQ, and a director of a PR company specialising in IT.

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