Intelligent automation - Assessing artificial intelligence capabilities potential to complement robotic process automation
Kaarnijoki, Pavel (2019)
Kaarnijoki, Pavel
2019
Tietojohtaminen
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2019-02-06
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201901251169
https://urn.fi/URN:NBN:fi:tty-201901251169
Tiivistelmä
The aim of this study was to find out how artificial intelligence capabilities could complement robotic process automation when automating business processes. The purpose of this thesis was to find out what robotic process automation can do, what limitations does it have, what is artificial intelligence and what kind of different capabilities it has as well as how these capabilities could improve the automation rate of business processes when combined with robotic process automation. In addition, also challenges related to using artificial intelligence was covered.
Theoretical part of the study was based on literature review on the topics of robotic process automation and artificial intelligence, and empirical part of the study consisted of semi-structured theme interviews. For the empirical part two expert interviews were held. One of which covered the topics related to intelligent automation and the other interview focused on artificial intelligence and its capabilities.
The outcome of this study indicated that artificial intelligence capabilities can complement robotic process automation in many ways when automating business processes. Robotic process automation is a valuable and efficient tool to automate processes but there are certain challenges related to it which limit the possible use cases. These challenges relate muchly to requirements to handle structured data and incapability to make complex decisions and handle process-es where there is no clear rules. Artificial intelligence capabilities then often aim to address exactly these challenges. They aim to either extract information from unstructured or from huge amounts of structured input data, or make sense out of the transformed structured data and understand what is happening. However the study also indicated that there are many challenges and considerations related to using artificial intelligence capabilities together with robotic process automation. Most pressing ones were related to data. Use of artificial intelligence requires huge amounts of data and it needs to be of good quality – this isn’t always self-evident with the data organizations have. Also critical challenges related to validation of the data, trusting the results given by artificial intelligence, as well as to GDPR and possible future regulations with artificial intelligence.
Theoretical part of the study was based on literature review on the topics of robotic process automation and artificial intelligence, and empirical part of the study consisted of semi-structured theme interviews. For the empirical part two expert interviews were held. One of which covered the topics related to intelligent automation and the other interview focused on artificial intelligence and its capabilities.
The outcome of this study indicated that artificial intelligence capabilities can complement robotic process automation in many ways when automating business processes. Robotic process automation is a valuable and efficient tool to automate processes but there are certain challenges related to it which limit the possible use cases. These challenges relate muchly to requirements to handle structured data and incapability to make complex decisions and handle process-es where there is no clear rules. Artificial intelligence capabilities then often aim to address exactly these challenges. They aim to either extract information from unstructured or from huge amounts of structured input data, or make sense out of the transformed structured data and understand what is happening. However the study also indicated that there are many challenges and considerations related to using artificial intelligence capabilities together with robotic process automation. Most pressing ones were related to data. Use of artificial intelligence requires huge amounts of data and it needs to be of good quality – this isn’t always self-evident with the data organizations have. Also critical challenges related to validation of the data, trusting the results given by artificial intelligence, as well as to GDPR and possible future regulations with artificial intelligence.