Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A learning perspective on the interdependency between technology-driven and managerial- driven AI-transformation
Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.ORCID iD: 0000-0002-8993-4306
Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.ORCID iD: 0000-0003-3133-1112
Ideation and Research AI Labs, Husqvarna Group.
2022 (English)In: International Conference on Work Integrated Learning: Abstract Book, Trollhättan: University West , 2022, p. 122-124Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Introduction

Moving from manual, to automated, to connected AI operations systems implies a significant transformation in the organisation of work (European Parliament, 2015:8) (Brock & von Wangenheim). To understand these “realistic AI” processes, to build competence for certain tasks. it is crucial to understand what organisational competencies that are needed and how to organize knowledge creation processes in practice (Ellström, 2001) Schön used the concept of “knowing–in-action” is nonreflective and solving most everyday practical problems, here understood as executional learning (Engström & Wikner, 2017). Thus, this knowing, according to Schön (1983), is not enough to meet more complex situations. To be aware of tacit knowledge, we need to distance ourselves and learn to reflect. More complex, uncertain and unclear tasks require “knowing-on-action” and collaboration between several competences to create new knowledge or to reach a new solution here understood as developmental learning (Engström&Wikner).. Anton et al. (2020) state that in many organisations there is a lack of AI-related competencies that prevent development of the full AI potential. For the development of the field, it is important to study the dynamic interplay between advanced technology and the social side of work from a learning and competence perspective. Therefore, this paper aims to explore how industrial organisations understand their competencies in relation to AI transformation from a knowledge creation perspective.

Research method

The study was part of a collaborative research project with an interdisciplinary research team and representatives from five industrial partners. In four-month cycles the industrial partners engaged in “homework” presented, analysed and discussed in common workshops. For this study, the homework was guided by the DIGITAL approach (Brock & von Wangenheim, 2019) and based on the explanatory model (Anton et al., 2020). The industrial partners studied how resources and competencies related to specific organisational tasks in their own organisations could be identified and defined. To aid the data collection (that was done by the industrial partners themselves) a framework capturing Anton et al.’s (2020) 13 dimensions of competencies (Leadership, Communication, Customer-focused decision making, Business development, Data science/STEM, Agile software development, Initiative and engagement, AI technology, Programming, Digital analysis tools, Data and network technology, Digital competencies, and Data management) was used. For each dimension the partners assesses the competence level: Competence central to the process; Competence exists internally; Competence partly exists internally; Competence does not exist internally; Competence can be gained by development internally; Competence needs to be sourced externally. These were in line with Brock & von Wangenheim’s (2019) logic that managers when starting AI project should do “internal resources check”. The data was analysed in four steps. First, focus group data was analysed by the facilitators at each industrial partner. Second, the competence mapping was analysed by the “working groups” at each industrial partner. Third, the transcribed data from the two industrial partners used in this paper were reviewed individually by t he authors. Fourth, the cross-disciplinary group of authors from both academia and industrial partners gathered for a common analysis session. This session primarily focused on the data from the competence mapping but also cross-checked with the input from the cross-functional focus groups to triangulate the outcome. During the common analysis the conceptual framework presented in the discussion section was developed through iterations between the theoretical framework based on the findings by Anton et al. (2020), and the data from the project.

Findings

The preliminary findings show differences among the industrial partners in how they view their own competencies. For some organisations organisational structures are in place, e.g., dedicated AI Labs, where the work with understanding the benefits and usage of the technology is ongoing on a rather advanced level. In other organisations the work has just been initiated. Overall, all representatives stress the importance of top management support and the need for dedicated forums. Among the organisations that have come the farthest in their AI transformation the structure given by the proposed framework is not enough. They emphasise the need to further frame it into also understanding what the competence is associated with and why it is needed. They view the leadership as almost having to have an evangelistic approach to it, where it does not seem to be enough with “only” technical experts. A conceptual framework, consisting of the relationship between the two dimensions: the managerial competencies and the technical competencies, is developed (Figure 1). The managerial competencies dimension concerns organisation and organising. The technical competencies dimension on the other hand captures the complexity level of the technology that is needed, the system of systems. The diagonal illustrates the relationship between these two dimensions, that is, the relation between technological complexity and organisational ability. The lower part of the diagonal captures isolated, simple processes (presumably internal) while the upper part of the diagonal captures integrated, complex processes (presumably primarily related to external parts and/or actors).. For high levels of technical complexity that requires high levels of technical competencies within the organisation the organisation also needs to advance the managerial competencies and the developmental learning processes. However, while in the long-term perspective we suggest that going off the diagonal will be inefficient and ineffective, hence, waste, it might be needed to do that temporarily, as the organisation develops. We believe that this developmentcan be either technology-driven or organisation-driven.

The proposed conceptual framework is intended to help organisations plot their own current position based on the two dimensions and identify what changes are needed to reach the diagonal. It can also be used to define where on the diagonal the organisation ultimately wants to end up. It is not relevant for all companies or even for all sectors overall to be at the top right side. We believe that AI transformation cannot be approached as either technologydriven or managerial-driven, but as an e interdependent process of both dimensions.

Place, publisher, year, edition, pages
Trollhättan: University West , 2022. p. 122-124
Keywords [en]
Artificial intelligence, Transformation processes, Competencies, Work-integrated learning
National Category
Learning Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:hj:diva-59791ISBN: 978-91-89325-30-2 (electronic)OAI: oai:DiVA.org:hj-59791DiVA, id: diva2:1735926
Conference
WIL'22 International Conference on Work Integrated Learning, 7-9 December 2022, University West, Trollhättan, Sweden
Available from: 2023-02-10 Created: 2023-02-10 Last updated: 2023-02-10Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Extended Abstract

Authority records

Engström, AnnikaEdh Mirzaei, Nina

Search in DiVA

By author/editor
Engström, AnnikaEdh Mirzaei, Nina
By organisation
JTH, Supply Chain and Operations Management
LearningProduction Engineering, Human Work Science and Ergonomics

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 172 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf