Increasing productivity with AI powered knowledge work automation : case M-Files
Isotalo, Akseli (2025)
Katso/
Sisältö avataan julkiseksi: 09.04.2026
Pro gradu -tutkielma
2025
School of Business and Management, Kauppatieteet
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025040122750
https://urn.fi/URN:NBN:fi-fe2025040122750
Tiivistelmä
This thesis explores the impact of AI-powered Knowledge Work Automation on knowledge work productivity, with a specific focus on the knowledge intensive services. In the empirical part, the research focuses on the use of the M-Files Knowledge Work Automation platform and its effects on knowledge work productivity. The research highlights the growing importance of knowledge work in modern business, and the necessity for effective automation to enhance productivity.
The thesis begins by outlining the research objectives, which include understanding how knowledge work productivity is determined and identifying practical means of increasing it through AI-powered automation. The study aims to address the research questions about A) the definition of knowledge work productivity, B) the methods to increase it using AI-powered automation, and C) the tangible and intangible effects of integrating such automation platform to business processes.
The research methodology involves a case study of the M-Files platform and the integrated GenAI assistant M-Files Aino. M-Files is a document management system that leverages AI to automate knowledge work tasks. M-Files knowledge work automation helps automate routine tasks to save time, streamline processes, reduce errors, and add information security.
The study found that the integration of generative AI in document management can lead to significant improvements in efficiency and overall productivity in knowledge-intensive services. The key findings indicate that automating the frequent manual tasks typical for knowledge work will allow knowledge workers focus on more valuable and complex work and produce more value in less time. This not only increases individual productivity but contributes to the overall efficiency of the organization. In order for knowledge-intensive organizations to successfully operate in the changing business environment, they need to integrate AI-powered knowledge work automation into their operations.
The thesis begins by outlining the research objectives, which include understanding how knowledge work productivity is determined and identifying practical means of increasing it through AI-powered automation. The study aims to address the research questions about A) the definition of knowledge work productivity, B) the methods to increase it using AI-powered automation, and C) the tangible and intangible effects of integrating such automation platform to business processes.
The research methodology involves a case study of the M-Files platform and the integrated GenAI assistant M-Files Aino. M-Files is a document management system that leverages AI to automate knowledge work tasks. M-Files knowledge work automation helps automate routine tasks to save time, streamline processes, reduce errors, and add information security.
The study found that the integration of generative AI in document management can lead to significant improvements in efficiency and overall productivity in knowledge-intensive services. The key findings indicate that automating the frequent manual tasks typical for knowledge work will allow knowledge workers focus on more valuable and complex work and produce more value in less time. This not only increases individual productivity but contributes to the overall efficiency of the organization. In order for knowledge-intensive organizations to successfully operate in the changing business environment, they need to integrate AI-powered knowledge work automation into their operations.