In the settlements of the Wiener Wohnen, after all, Europe’s largest municipal property management, the plump life takes place. 550,000 residents of the Austrian capital live in one of the apartments. 200 employees of a call center take care of their worries and needs around the clock. You know the whole range of stressed tenants, for example because the neighbor is constantly grilling or the smell of cannabis penetrates through the parquet floor.
In February, however, a tenant had a very special problem: the dog yelped when you “go for a walk” with him, he reported to the call center agent, who recorded the message in writing as usual. Within seconds, he had a solution: the tenant should clean the dog’s paws – he was in pain because of the road salt. The employee did not come up with this not very obvious idea himself. Artificial intelligence (AI) helped him.
Wiener Wohnen uses software from the company Deepsearch, also from Vienna. Natural Language Understanding (NLU) is the process that Deepsearch and other companies use. “Our software analyzes in a semantic way what a text is about,” says founder Roland Fleischhacker.
The keywords dog, walking, yelping and the fact that it was February put the system on the right track. And the problem could be solved quickly. Without NLU, according to Fleischhacker’s conviction, a long conversation would have ensued.
Unlike industry, the service sector has only a low degree of automation. But that will change in the coming years. There is a shortage of staff, especially in call centers. If you have to deal with angry customers every day, you need a thick coat.
Employees change, the know-how of AI remains
Call centers have turnover rates of around 30 percent. The operators train the employees, and as soon as they are saddled, many have already left. With them, experience also disappears. Know-how, on the other hand, which is in AI, remains in the company. Fleischhacker therefore says: “We take cognitive processes off the employees.“
At Wiener Wohnen, it used to take around three and a half weeks for a new call center employee to be trained; now four days are enough for that, says Stefan Wanner, Head of Call Services at Wiener Wohnen.
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The now 60-year-old meat hacker studied electrical engineering in Vienna and Munich in the 1980s. He then founded Austria’s first SAP consulting firm. Over the past ten years, he has created a so-called knowledge graph with his comrades-in-arms and his own capital: the basis of deepsearch and a kind of fishing net that links aspects of knowledge. Meanwhile, the company has 20 employees, Fleischhacker does not reveal anything about the turnover.
NLU sounds a bit like science fiction, but has come so far into reality that it has created its own industry. Small companies such as Deepsearch work with the technology, but also corporations such as Microsoft (Luis), IBM (Watson) and Google (Dialogflow).
Fleischhacker says that you know the tech companies worldwide, but they are not really good in the field of NLU. For laymen, such statements are hardly verifiable: is it self-promotion of an entrepreneur or a fact? At least the renowned consulting firm Gartner ranks Deepsearch among the top five global providers in the field of NLU. The tech giants are not in it.
The consulting firm Gartner ranks its company Deepsearch among the five leading providers of natural language understanding worldwide.
The Austrians have already built up a small customer base; in addition to Wiener Wohnen, these include Deutsche Bahn or The network partners, an organization of 130 German energy suppliers.
Quirky things sometimes happen at these companies as well. What should I do if a railway customer complains with the confusing message that he ordered an edamame salad in the on-board bistro, but the cheese was missing in it? In no time, the AI directs the call center employee to the menu and the right section. Edamame is a soybean dish and has nothing to do with the Dutch Edam cheese.
Many companies keep silent about the use of AI
As funny as individual examples may seem, ultimately companies always strive to speed up processes with AI from Deepsearch and other providers. The network partners from Germany, for example, have been using the Austrian software since the beginning of the year to process customers’ e-mails. They are automatically read and redirected to the right place.
Employees need a few minutes for this process, the AI does it in milliseconds. The hit rate of the software is not one hundred percent, but people also make mistakes.
At the same time, companies that use algorithms in customer contact or internally find it difficult to admit this: they fear the accusation that AI only serves the purpose of saving personnel. A large German pharmaceutical company is a customer of Deepsearch, says founder Fleischhacker, but does not want to make this public.
If AI works, it helps to reduce costs. However, the companies vehemently deny that this is associated with job cuts. “Rather, we are aiming for better advice,” says Stefan Wanner from Wiener Wohnen.
Today’s customers are spoiled, explains Claudio Latorre, Head of digital Products at Die Netzwerkpartner. They always expected a response as quickly as possible – AI helps companies to meet this requirement. “We want to relieve customer service of routine work.“
As a result, a similar development would take place in the service sector as in the past decades in industry. But this also means that traditional jobs will definitely disappear, even if the companies don’t like to talk about it.
And, as in the manufacturing sector, companies will drive automation. The call center employees who work with Deepsearch and other providers still record the concerns of the customers in writing. At Wiener Wohnen, however, “speech-to-text” is also on the future agenda, says Wanner. So what callers say would be automatically turned into text. However, this requires the consent of the customers in order to comply with data protection.
Is it a little science fiction? No, says Fleischhacker. We can only talk about this if AI also has a consciousness. “We are not that far yet, and we may never get there.“
