It can be difficult, when assailed daily by news of populism, terrorism and cyber hacking, to look to anything beyond the next crisis. Yet business leaders need to focus on the future. What, for example, does it mean for employers that by 2027, Africa’s population will have grown by a third and Europe’s will have flatlined? How will companies cope when governments expect them to gather more staff data and play ever larger roles in enforcing tax laws?
In Ten Years Out, four senior FT journalists outline what they see as the biggest challenges that no chief executive will be able to ignore. They also provide some tips on how companies can best prepare themselves for the changes that are coming.
Gideon Rachman, chief foreign affairs commentator, writes about migration, while James Kynge, emerging markets editor, points out that China will become dominant not just economically but in technology.
Vanessa Houlder, tax correspondent, points to a structural change in the role of the corporation: from private business to public enforcer. Finally, Richard Waters, West Coast editor, thinks that artificial intelligence applications will be as ubiquitous as electricity is today, but he warns of ethical dilemmas for businesses.
For the US and China, the next decade holds the promise, as the Chinese phrase has it, that “heaven and earth will change places”: the world’s rising superpower may overtake the incumbent as the biggest economy in the world.
There are already signs of a shift in the balance of power. It is likely that, at current rates of growth, China by 2026 will eclipse the US in terms of gross domestic product, according to the Economist Intelligence Unit; other sources expect the transition to occur several years before that. China is already challenging the US as a hub for global technological innovation, particularly in areas such as big data and renewable energy.

Watching the news on a mobile phone at a recycling station in Shanghai
As these changes come to pass, they will undoubtedly influence political alliances and the stewardship of the global economy. For many businesses around the world, however, the impact is likely to be much more direct.
Geographically, this could mean that places such as Shenzhen, on China’s border with Hong Kong, and Hangzhou — the city near Shanghai where Alibaba, the ecommerce giant, was founded — challenge Silicon Valley as the global centre of cutting-edge tech.
Big shifts are already under way. China is catching up with the US in its creation of unicorns, or start-ups valued at $1bn or more. In June 2016, one-third of the world’s 262 unicorns were Chinese, representing 43 per cent of the $883bn worldwide valuation attached to these companies, according to the McKinsey Global Institute, the consultancy’s think-tank.

If the value of funds being channelled into “big data” innovation in China is any indication, this trend is set to continue. As of last year, the world’s second-largest economy ranked in the top three countries for venture capital investment in some particularly competitive fields of digital technology, including virtual reality, autonomous vehicles, 3D printing, robotics, drones and artificial intelligence, according to a McKinsey report.
Thus, in 10 years’ time, China may have ridden its big-data revolution to pull ahead of its competitors in the west. Trade and investment friction will probably intensify as China competes more directly with US and European technology and brand leaders.
These trends in China are driven not just by money — Chinese consumers are more at ease with tech than anywhere else. The value of their mobile payments outstripped those of Americans by about 10 to one last year, and China’s ecommerce market is about double the size of that in the US.
Companies such as Alibaba — which in terms of stock market capitalisation is closing in on Amazon — are well into a global acquisition spree powered by the huge profits they are making in the domestic market. China’s tech giant Tencent, internet company Baidu and others similarly are eyeing the world’s opportunities hungrily.
In 10 years’ time, the spotlight is set to fall unsparingly on the inequalities in access for investors. China’s economy remains much less open to overseas direct investors — particularly in mergers and acquisitions — than Europe and the US are to their Chinese counterparts.
James Kynge on how China is disrupting global industries through innovation
The risk to US and European companies comes not only from the evolving technological disruptions but also from the possibility that businesspeople may fail to understand the competitive risks of the speed and scale of the changes under way in China.
These sources of tension, if unchecked by deft diplomacy, could combine with geopolitical competition to stoke an atmosphere of great acrimony as China slowly displaces the US as the world’s most powerful nation. James Kynge
Artificial intelligence will soon be like electricity — a resource that almost any digital application or service will be able to “plug into”, giving it extra powers.
That is according to Andrew Ng, the Stanford University professor who was a pioneer of deep learning, a technique behind today’s most advanced image and language recognition systems. The current uses of AI, which include machine learning and predictive algorithms, point to what is to come.
Mr Ng’s analogy hints at the factors that will make AI transformative for many businesses over the next 10 years. It will be powerful and adaptable, operating in the background and often unseen. Its effects will be pervasive, heightening the effectiveness of many different applications. It will also mark a generational change in technology — and business — as significant as the move from steam to electric power.
Richard Waters on how businesses will have to learn to work with AI
Even small improvements can produce big financial returns. The relevance engines used in many online services, for instance, predict what types of content or advertising users might find most engaging. Increasing the chances of them clicking on a link has a direct impact on revenue.
Entirely new AI-powered applications are starting to appear. Some of the biggest advances have come in image recognition systems, which have reached human levels of reliability. That should make it possible to take over tasks such as analysing medical images.

An employee uses a facial recognition device at a factory in Shanghai
Language has proved more difficult to crack. Many experts in the field believe, however, that recent breakthroughs point to the same kind of advances already made with computer vision, and that machines will soon be able to “understand” language as well as humans.
These techniques promise to enhance many jobs and make workers more productive. By using machines that can trawl through and understand much larger bodies of information than humans could ever manage, it should become possible to pull out the most relevant material or present preliminary analyses for workers to take further.
Such AI techniques might also cause more disruptive change to entire industries. Autonomous vehicles, for instance, rely on technologies such as image recognition to make sense of what is going on around them, replicating the capabilities of a human driver. Some big carmakers predict they will have driverless vehicles on public roads early in the next decade, with potentially far-reaching consequences for businesses and workers that rely on transport or logistics.

Waymo's self-driving car technology on a road test at its facility in California
Deep-learning systems, though, are the ultimate black boxes. Even the engineers who are building them cannot tell precisely why they are reaching particular conclusions from the data they are fed. That raises profound questions about how reliable — and fair — the systems’ recommendations and predictions will be.

The most striking study to date of the potential bias in AI was published by ProPublica, a US non-profit producer of investigative journalism. It concluded that the software used by judges in some US states to predict the risk that a prisoner would reoffend — and therefore whether the judge should grant bail — displayed racial bias.
ProPublica’s conclusions have been disputed by the software developer and by some academics. They are, however, a clear warning to all companies that shift parts of their decision-making to machines in the coming years, that the results from such systems will be only as unbiased as the data they learn from. Richard Waters
Writers: Gideon Rachman, James Kynge, Vanessa Houlder, Richard Waters
Editor: Emma Boyde
Picture editor: Michael Crabtree
Video: Petros Gioumpasis, The Producer's Loft Studio
Video editing: James Sandy, Oliver McGuirk
Production editor: George Kyriakos
Sub-editor: Philip Parrish
Photography: Bloomberg, Getty Images, Reuters
(c) Financial Times 2017