Artificial intelligence: Why machines won’t make us unemployed.

When it comes to the potential of artificial intelligence (AI), many think in big terms: “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don't think AI will transform in the next several years,” said computer scientist Andrew Ng, for example. Ng headed AI research projects at the world’s largest search engines – Baidu and Google – and established an AI investment fund.

But there is a difference to earlier technical revolutions. Fear of job losses is now also affecting well-qualified knowledge workers. For example, almost half of the financial executives polled (47 percent) were anxious about job losses due to artificial intelligence. This was one of the findings from the survey “European Payment Practices” 2019. Financial services provider EOS had polled 3,400 experts in 17 countries and asked, among other things, what influence AI would have on receivables management in their companies.

“Those who immediately associate AI with ‘man versus machine’ often lack the necessary background information. Awareness-raising is the only way to counter this,” says Joachim Göller, Head of the Center of Analytics of the EOS Group. He and his team are working on AI solutions that help EOS with receivables management. “With the support of artificial intelligence, people will drastically reduce the error rate in receivables management. In any case, this is also what 30 percent of the financial executives from the survey think.”

By using AI properly, a company will ideally become more competitive and can allow its staff to take on more interesting tasks. At EOS, for example, collection teams use artificial intelligence for routine cases, so that they can focus on those customers whose cases are more complex. Other sectors are also relieving their specialist personnel of standard processes. Finnish software company Basware, for example, has developed a virtual assistant that answers everyday queries in the procurement department.  And Swedish bank SEB reduces the workload of its IT support with Ipsoft’s smart virtual assistant Amelia.

“First of all the company has to produce an overview: Where exactly can AI be used to automate simple tasks? Where can it make complex processes more user-friendly?”, says Goossens. It is at this stage that many companies realize that they lack the most important basis for introducing self-learning systems, i.e. the necessary quality and volume of data to feed the algorithms. “Generally, most companies don’t have data engineers,” says Goossens – i.e. those experts that ensure that all data are clearly structured and stored in one place. This is a challenge for companies where departments are still working with different IT systems and separate databases.