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Why our high volume economic model is in jeopardy

Why our high volume economic model is in jeopardy

Increasing automatisation is being seen as an answer to rising costs and as a route to increased efficiency, not just in the wealth management industry, but across the business sector.

Leo Johnson (pictured), partner for sustainability at PwC and fellow of Smith School of Enterprise and Environment at Oxford, sees automatisation as having unexpected and drastic consequences that could put some of the world’s biggest companies in jeopardy.

‘We’ve had a whole industrial model that has been based on large machines, volume and driving up demand. It is a mass-market, mass-production, mass-consumption model.’

He explained that these companies have typically had very low margins, which are highly vulnerable to technological change.

‘Take electricity. If 10% of people switch from the commercial grid to off grid the margins become so tight that it collapses. [Or] take any short haul airline – how much profits are made? For the whole plane, it is £540 a flight.’

He cited research estimating that 69% of the Indian workforce could potentially be made redundant by automation, creating huge conflict between economic, social and political priorities. 

He points out that the search for operational efficiency could ultimately destroy the demand base of the global economy.

‘If just 10% leave [the workforce] immediately you’ve got a problem with healthcare, pensions, and some of the basics – even Starbucks – all of these high volume models suddenly start to look questionable.’

Closer to home, he pointed to the rise of passive investment and reduction of fees throughout the banking industry as an example of how automatisation is already cutting margins in the investment industry.

‘This revolution is collar-blind, it’s white-collar and it’s blue-collar. If accountants, lawyers, investment managers, etc, start to see algorithms eating away [at their margins], like passive investment strategies, we start to see that [profitability] of 5% becomes 2% and what we see is another unstable business start to be put in jeopardy.’

In terms of businesses that are able to weather this revolution, Johnson thinks the critical question is whether the humans at the heart of the business are indispensable and whether or not the business’ services can be replicated by machine learning. 


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