Seeking Augmentation of Knowledge Work through Machine Learning - A Case Study in the Field of Financial Planning

Aug 6, 2026

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Organizations increasingly adopt machine learning to augment knowledge work with data-driven predictions, particularly in forecasting. In a case study from a telecommunications firm’s finance planning and analysis (FP&A) department, the work explores how finance professionals embed ML-generated forecasts within ongoing manual forecasting practices. Using the lens of epistemic cultures, the study treats manual forecasts as epistemic objects that support shared understanding and assumption-making. ML forecasts add new interpretations, but also create challenges in reconciling differences, judging quality, and fully integrating insights into daily decision activities.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

Augmentation promise of AI in knowledge work

Machine learning in forecasting and epistemic objects

Association for Information Systems AIS Electronic Library (AISeL) | ICIS 2024 Proceedings | International Conference on Information Systems (ICIS) | | --- | --- | | December 2024
Seeking Augmentation of Knowledge Work through Machine Learning: A Case Study in the Field of Financial Planning
Miriam Möllers
University of Münster, [miriam.moellers@ercis.uni-muenster.de](miriam.moellers@ercis.uni-muenster.de)
Benedikt Berger
University of Münster, [benedikt.berger@ercis.uni-muenster.de](benedikt.berger@ercis.uni-muenster.de)
Follow this and additional works at: [https://aisel.aisnet.org/icis2024](https://aisel.aisnet.org/icis2024) | | Recommended Citation Möllers, Miriam and Berger, Benedikt, "Seeking Augmentation of Knowledge Work through Machine Learning: A Case Study in the Field of Financial Planning" (2024) . ICIS 2024 Proceedings. 24. [https://aisel.aisnet.org/icis2024/digtech_fow/digtech_fow/24](https://aisel.aisnet.org/icis2024/digtech_fow/digtech_fow/24) This material is brought to you by the International Conference on Information Systems (ICIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ICIS 2024 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). Seeking Augmentation of Knowledge Work through Machine Learning: A Case Study in the Field of Financial Planning Completed Research Paper Miriam Möllers University of Münster Leonardo Campus 3, 48149 Münster, Germany miriam.moellers@ercis.uni[muenster.de](muenster.de) Benedikt Berger University of Münster Leonardo Campus 3, 48149 Münster, Germany benedikt.berger@ercis.uni[muenster.de](muenster.de) Abstract Organizations increasingly use machine learning (ML) techniques to augment knowledge work with data-driven predictions. An example of such knowledge work is forecasting, aresource-intensive and discursive process, in which knowledge workers collaboratively analyze and interpret the business and market data to discuss strategic goals. Through a case study in a financial planning and analysis (FP&A) department of a telecommunications company, we investigate how finance professionals integrate ML forecasts within existing, manual forecasting activities. Using the lens of epistemic cultures, we interpret the manual forecasts as key objects through which finance professionals create a shared understanding and form assumptions about the current and future business influences. The ML forecasts introduce new insights to these discussions, offering different interpretations and meaning. Consequently, the finance professionals struggle to make sense of the differences between the ML and manual forecasts, to evaluate their quality, and thus, to fully integrate them in their activities. Keywords: Augmentation, knowledge work, machine learning, forecasting Introduction To remain competitive in an increasingly data-driven organizational landscape, organizations are gradually implementing systems based on artificial intelligence (AI) to augment human work in various tasks and domains (Jarrahi et al., 2022) . Since these systems complement human capabilities with great accuracy and efficiency in inferring patterns from data and making predictions (Afiouni, 2019), the promise of augmentation is that humans and AI systems perform work more efficiently in combination, or jointly make better informed decisions together (Jarrahi et al., 2022; Raisch & Krakowski, 2021; Shrestha et al., 2019) . In this way, many organizations hope to free employees from time-intensive or repetitive manual tasks related to data collection or analysis (Anthony, 2021) or to reduce human flaws in decision making (Shrestha et al., 2019; van den Broek et al., 2021) . The concept of augmentation through AI systems is particularly prominent in knowledge work fields. As work in these areas relie

FAQs

What problem does the study address about ML in forecasting?

Finance professionals struggle to make sense of differences between ML and manual forecasts, assess their quality, and fully integrate ML outputs into existing forecasting activities.

How does the study frame manual and ML forecasts conceptually?

It uses epistemic cultures to interpret manual forecasts as key epistemic objects through which finance professionals build shared understanding and form assumptions about current and future influences.

Where is the case study conducted and what activity is examined?

The analysis is conducted in a financial planning and analysis (FP&A) department of a telecommunications company, focusing on forecasting work and how ML forecasts are integrated into manual processes.

Aug 6, 2026

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