This article was first published in the April 2017 China edition of Accounting and Business magazine.

‘Blockchain will eliminate fraud.’ ‘If you don’t have a plan for the Internet of Things, you’ll be in serious trouble in the next 24 months.’ ‘Every single thing on earth that moves will become measurable by sensors.’ ‘Before, you give me the business and I give you the data. Now, the data is the business.’ 

These are some of the assertions I heard at a closed-door briefing in Hong Kong on the changes that technology is causing in businesses today. How deep and rapid are those changes? Consider that it took 75 years for the telephone to connect 50 million people. In contrast, it took a matter of days last year for the augmented reality game Pokemon Go to amass 50 million players.

Processes like credit scoring are being revolutionised by the analysis of billions of bits of real-time data using artificial intelligence (AI) and machine learning. For example, Chinese company Ant Financial judges creditworthiness not by a person’s financial history, but by what it could learn from his or her phone apps usage and social media and other online presence.  

Insurance firms are drawing on the same information to design coverage customised to the individual’s particular circumstances. Using AI tools, they construct a unique product in terms of time period, location, type of protection and so on. In trade finance, some banks track the movement and ownership of the goods covered by their contracts everywhere on the globe via sensors and blockchain technology.

In theory, all these bits of information can be gathered and analysed to give companies an advance indication of what the GDP data would look like, where interest rates are going to be, foreign exchange trends, commodities supply and demand, consumption patterns, and many other pieces of information essential for accurate planning and forecasting.

Where companies need to spend is on people – in recruiting and training those who can programme AI tools; exercise judgment on which data sources are useful and reliable; conduct data analytics; and recommend actions steps. For today’s  finance professionals, the cost is in the time and effort needed to manage or acquire the skills of the data analyst, blockchain practitioner, computer programmer and other non-traditional specialists.

This assumes that AI, machine learning and other technological advances will complement, not supplant, the finance professional. At the briefing, some expressed strong reservations. ‘Artificial intelligence and machine learning will cause massive unemployment, economic depression, militarisation, long-term sociological and societal consequences,’ warned an investment banker.

There was a noticeable divide between those in their 50s and those who were younger, with discussants in their 30s and 40s saying that there is no point in attempting to roll back the rapid changes. The most pragmatic course, they said, is to manage the transition, starting with corporate boards recruiting younger members conversant with the technology changes.

Change is always wrenching, and the one we are seeing today is going to be more painful than most. The finance professional is probably better positioned than other knowledge workers, although accountant-types focused on transactional work rather than analysis and judgement are in danger. 

The bottom line: change is coming swiftly. Companies and individuals that will survive and thrive are those that are agile, adaptable and open to new skills and mindsets. 

Cesar Bacani is editor-in-chief of CFO Innovation