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Artificial Intelligence in Internal Business Processes: Mixed-Method Approach to Identify Use-Cases and Evaluate its Value Addition

Åman, Ludvig LU ; Sippel, Alexander LU and Heidenreich, Philip Lucas LU (2021) INFM10 20211
Department of Informatics
Abstract
Artificial Intelligence (AI) has over the past two decades evolved from something theoretical into something more practical and applicable. The technological advancements of the modern era have enabled us to utilize more advanced systems to facilitate various tasks. AI more specifically, has been integrated into many of the systems we use in both everyday lives, as well as in business systems. This thesis set out to analyse the possibilities of AI enabled process improvement systems, to assist businesses in optimising their internal processes by creating and adopting suggestions and improvements. After a thorough literature review of the existing AI systems, the authors conducted a survey, to analyse where different corporations implement... (More)
Artificial Intelligence (AI) has over the past two decades evolved from something theoretical into something more practical and applicable. The technological advancements of the modern era have enabled us to utilize more advanced systems to facilitate various tasks. AI more specifically, has been integrated into many of the systems we use in both everyday lives, as well as in business systems. This thesis set out to analyse the possibilities of AI enabled process improvement systems, to assist businesses in optimising their internal processes by creating and adopting suggestions and improvements. After a thorough literature review of the existing AI systems, the authors conducted a survey, to analyse where different corporations implement AI. Further, based on the empirical data of the survey, interviews with participants from the survey that recognised the usage of AI in internal business process optimisation were conducted. The result of the study indicates that implementing AI in process optimisation is a more difficult task than anticipated. Some of the obstacles that have yet to be overcome, are: hardware maturity and limitation, ethical considerations, lack in expertise, and economical resources. Conclusions show that AI for process improvement has yet to reach its full potential. (Less)
Please use this url to cite or link to this publication:
author
Åman, Ludvig LU ; Sippel, Alexander LU and Heidenreich, Philip Lucas LU
supervisor
organization
course
INFM10 20211
year
type
H1 - Master's Degree (One Year)
subject
keywords
process improvement, artificial intelligence, business process development, digitalisation, business value, automation
report number
INF21-059
language
English
id
9048037
date added to LUP
2021-06-21 12:24:43
date last changed
2021-06-21 12:24:43
@misc{9048037,
  abstract     = {{Artificial Intelligence (AI) has over the past two decades evolved from something theoretical into something more practical and applicable. The technological advancements of the modern era have enabled us to utilize more advanced systems to facilitate various tasks. AI more specifically, has been integrated into many of the systems we use in both everyday lives, as well as in business systems. This thesis set out to analyse the possibilities of AI enabled process improvement systems, to assist businesses in optimising their internal processes by creating and adopting suggestions and improvements. After a thorough literature review of the existing AI systems, the authors conducted a survey, to analyse where different corporations implement AI. Further, based on the empirical data of the survey, interviews with participants from the survey that recognised the usage of AI in internal business process optimisation were conducted. The result of the study indicates that implementing AI in process optimisation is a more difficult task than anticipated. Some of the obstacles that have yet to be overcome, are: hardware maturity and limitation, ethical considerations, lack in expertise, and economical resources. Conclusions show that AI for process improvement has yet to reach its full potential.}},
  author       = {{Åman, Ludvig and Sippel, Alexander and Heidenreich, Philip Lucas}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Artificial Intelligence in Internal Business Processes: Mixed-Method Approach to Identify Use-Cases and Evaluate its Value Addition}},
  year         = {{2021}},
}