Artificial Intelligence in Internal Business Processes: Mixed-Method Approach to Identify Use-Cases and Evaluate its Value Addition
(2021) INFM10 20211Department 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:
http://lup.lub.lu.se/student-papers/record/9048037
- author
- Åman, Ludvig LU ; Sippel, Alexander LU and Heidenreich, Philip Lucas LU
- supervisor
-
- Gemza Ademaj LU
- organization
- course
- INFM10 20211
- year
- 2021
- 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}}, }