Why are technology advances that are so obvious in hindsight so hard to see at the time they are unfolding?
There is a growing body of research that suggests status quo bias, or ‘knowledge bias’, is to blame. It is why the smartest and most experienced people and companies, and the ones most affected by change, are the least able to see it.
Before the first machine age, factories were typically powered by a steam engine or water wheel. Workers were positioned around this central power source. With the dawn of electrification, factories that simply replaced the power source with electricity saw very little gains in productivity.
In their excellent book, Machine, Platform, Crowd: Harnessing Our Digital Future, Andrew McAfee and Erik Brynjolfsson document the demise of factories that used existing processes with new technologies.
Improvise. Adapt. Overcome.
Factories that simply replaced the power source with electricity failed because they did the exact same thing that had previously succeeded.
Factories that reorganised themselves to optimise the use of the new technology thrived. Individual machines could each have their own supply, and strong gains in productivity were achieved.
A more recent example of a company that failed to adapt is Blockbuster. We know the story well: Netflix disrupted the business model by introducing a subscription-based service, even before it abandoned sending DVDs through the post. This marked the end of late fees. Hallelujah!
Netflix used the data it amassed to recommend content. It then disrupted its own business model to stream content to people’s homes. And then it evolved again, from a content platform to a manufacturer of content.
But Netflix has at times struggled to make a profit. So another example, and a profitable one, is Hema, Alibaba’s take on the supermarket.
Hema is a chain of supermarkets, restaurants and fulfilment centres in Shanghai. Shoppers scan barcodes and check out seamlessly through digital wallet Alipay. The shops are also fulfilment centres. Aisles are kept clear to maximise valuable floor space. Staff put orders in bags that are then hooked onto a conveyer belt system, ferried across the ceiling of the store, dropped in a cooler box, put on the back of a scooter and delivered to shoppers within 30 minutes of an online order.
Alibaba recognised Chinese consumers want to choose their own fresh fish. Tanks can be found at the centre of the stores and shoppers can take their fresh handpicked Alaskan king crab home or to the in-shop restaurant to have it prepared and served onsite.
As we think about the next generation of financial advice and investing, we need to think about what the essential element is for the customer. What is the equivalent of the Shanghai shopper reaching into the tank to choose his or her own Alaskan king crab?
Finding the king crab
At NextWealth, we recently asked investors who pay for financial advice why they pay. In our survey, investors told us they pay for professional financial advice to get better potential investment returns and peace of mind.
You may dispute these results. There is no one typical customer and we all know different people have different priorities. The important thing is to figure out what the essential element is and ensure we keep that. If investors want peace of mind and they are willing to pay for it, the question is how to deliver that peace of mind by making use of emerging technologies that reduce cost and improve the outcome.
Financial planning has become too complicated. In a survey we conducted recently for Fidelity FundsNetwork, 80% of advisers said regulatory change and compliance were among their top three business challenges.
We are excited about how emerging technologies and data analytics tools can help deliver better advice more efficiently. But the regulator is slowing innovation.
Think about what Wealthfront does in the US: ‘You don’t have to tell us. We already know,’ it tells customers. ‘It’s in the data, and we will adjust our advice accordingly’.
Wealthfront has a team of data scientists and actuaries forecasting inflation, social security payments and investment returns. It analyses people’s actual spending behaviour to forecast cashflow needs in retirement. It analyses aggregated data to predict future behaviour and help nudge people to invest more.
Automated tools in the pocket of the adviser could revolutionise the profession and give more people access to quality advice. But the businesses and the regulator need to catch up with advances in technology before we become relics, like those old steam-powered factories.
Heather Hopkins is managing director at NextWealth