By automa­tion, we mean the inde­pen­dent han­dling of var­i­ous tasks or processes by IT solu­tions. The need for human inter­ven­tion is thereby lim­ited as far as pos­si­ble or elim­i­nated com­pletely. Depend­ing on the degree of automa­tion, we speak of par­tially or fully auto­mated solu­tions. Automa­tion serves to accel­er­ate cer­tain work processes, increase pro­duc­tiv­ity and min­i­mize sources of error.

The advan­tages for your com­pany are obvi­ous. But your employ­ees also ben­e­fit from automa­tion solu­tions. Since repet­i­tive man­ual processes are par­tic­u­larly suit­able for automa­tion, your employ­ees are relieved of recur­ring stan­dard tasks. As a result, they gain time and can focus on activ­i­ties that require more atten­tion. And because col­leagues who are chal­lenged more enjoy their work, their work­ing atmos­phere auto­mat­i­cally improves as well.

In prin­ci­ple, automa­tion is an option in numer­ous appli­ca­tion areas in the IT sec­tor. In the Con­tact Cen­ter envi­ron­ment, for exam­ple, the rout­ing of con­tacts and the dis­tri­b­u­tion of work are auto­mated. If, for exam­ple, the machine takes over the query of basic data for each caller and con­nects them quickly and accu­rately with the right con­tact per­son, the ser­vice employ­ees have more time for the qual­i­ta­tive con­sul­ta­tion. The inter­nal trans­fer rate drops, and caller sat­is­fac­tion increases.

Until today, the term “Arti­fi­cial Intel­li­gence” has been asso­ci­ated with all kinds of vague ideas about what AI can and can­not do. Yet the tech­nol­ogy has long been an inte­gral part of our every­day lives. How­ever, we usu­ally asso­ciate AI with com­plex machines or human-​like robots and ignore the fact that the tech­nol­ogy can also be found in small appli­ca­tions and tools. In other words, we use a vari­ety of AI-​based appli­ca­tions and ser­vices with­out even real­iz­ing it. Every day, smart voice assis­tants read all our wishes from our lips, web stores sug­gest suit­able prod­ucts, thanks to sophis­ti­cated algo­rithms, our cars maneu­ver them­selves into the small­est park­ing spaces, and our inquiries on var­i­ous web­sites are answered by friendly chat­bots.

The major­ity of such sup­port­ing sys­tems are mostly rule-​based and are lim­ited to a clearly defined area of appli­ca­tion. How­ever, AI can do much more. So-​called deep learn­ing algo­rithms are able to inde­pen­dently link learned con­tent with new con­tent due to a large infor­ma­tion base and the struc­ture of neural net­works. In addi­tion, they make analo­gies with­out being con­fronted with cer­tain key terms in the con­text of a ques­tion. In other words, they can “think around cor­ners.” The more rel­e­vant data such sys­tems process, the more reli­able they become. In this way, the “learn­ing effect” ide­ally leads to con­tin­u­ous self-​optimization.

Com­pa­nies can ben­e­fit sig­nif­i­cantly from the use of Arti­fi­cial Intel­li­gence. In the Con­tact Cen­ter envi­ron­ment, part of the cus­tomer com­mu­ni­ca­tion can be auto­mated with the help of arti­fi­cially intel­li­gent sys­tems. Thanks to deep learn­ing meth­ods, even rel­a­tively flex­i­ble dia­log guid­ance is pos­si­ble. In pro­duc­tion con­trol, processes can be auto­mated with the help of arti­fi­cially intel­li­gent solu­tions.

Most arti­fi­cially intel­li­gent sys­tems we cur­rently see are based on the prin­ci­ple of machine learn­ing. The aim of this method is to rec­og­nize cor­re­la­tions or pat­terns by intel­li­gently link­ing or sta­tis­ti­cally eval­u­at­ing data in order to derive con­clu­sions or make fore­casts. To do this, the learn­ing machine must be pro­vided with a suf­fi­ciently large data­base. In addi­tion, it must be enabled to process this data by pro­gram­ming spe­cial algo­rithms. This means that human action is nec­es­sary before the machine can develop its Arti­fi­cial Intel­li­gence.

Deep learn­ing is in turn a sub­area or a fur­ther devel­op­ment of machine learn­ing. It is based on arti­fi­cial neural net­works whose struc­ture and func­tion are mod­eled on the neu­rons of the human brain. Thanks to a large infor­ma­tion base and the struc­ture of the neu­ronal net­works, a sys­tem based on Deep Learn­ing should be able to link learned con­tent with new con­tent inde­pen­dently and, in the long term, across dif­fer­ent areas. In this way, new learn­ing processes are to be con­tin­u­ously ini­ti­ated. The goal is that the machine does not sim­ply learn, but that it learns to learn. In the­ory, human inter­ven­tion in the learn­ing process is no longer manda­tory.

For exam­ple, a deep learning-​based voice bot does not need to think through all poten­tial dia­log paths in advance and take into account the entire range of cus­tomer for­mu­la­tions. The bot sim­ply needs a suf­fi­cient data­base to be able to work com­pletely inde­pen­dently.

The con­cern that jobs will be lost as a result of automa­tion is wide­spread. After all, activ­i­ties that were pre­vi­ously per­formed by humans are now being car­ried out by machines. In fact, how­ever, the tech­nol­ogy behind the bots is miles away from mak­ing humans unnec­es­sary. And apart from that, it is not even aimed at replac­ing them com­pletely. In the Con­tact Cen­ter envi­ron­ment, automa­tion is only intended to relieve human employ­ees, but by no means to replace them.

For exam­ple, voice bots are not used to avoid cus­tomer meet­ings with employ­ees, but to pre­pare them in the best pos­si­ble way. As soon as the bot hands over a con­tact to the employee, it pro­vides him or her with the pre­vi­ously queried infor­ma­tion. In prac­tice, it has been shown that chat­bots only develop their full effect in cus­tomer dia­log in inter­ac­tion with human agents. In the ideal case, humans and machines go hand in hand thanks to a sen­si­ble divi­sion of tasks.

Once the con­cerns and reser­va­tions have been over­come, voice and chat­bots enjoy great pop­u­lar­ity with their human col­leagues. No won­der, after all, the machine helpers in the ser­vice cen­ter take over a large part of the tedious and monot­o­nous rou­tine tasks.