This study advances understanding of Artificial Intelligence transformation (AIT) barriers that hinder value realization in public organizations. We conducted a 1.5-year case study with a medium-sized Swedish municipality, involving active participation in daily work, development of an AI strategy, interviews, and analysis of four AIT cases. Using the Process, People, Technology (PPT) framework as inspiration, we developed a new AIT framework tailored to public sector transformation. Findings show that AI creates barriers but also offers opportunities, particularly as a driver of innovation and organizational effectiveness. Overcoming knowledge gaps, resource constraints, and integration challenges is critical for sustainable AIT. Furthermore, the results demonstrate that different forms of AI-related value manifest through observable outcomes, including resource gains, organizational adaptations, and the development of new citizen services. The study provides practical examples of AI implementation and illustrates the complex balance between barriers and opportunities in organizational change. The proposed AIT framework contributes both theoretically and practically, offering insights into how municipalities can navigate AI-driven transformation successfully.

Estimating the size and dynamics of the informal economy (IE) remains a persistent challenge, while consistent in its social and economic impact. Multiple studies tackled the temporal dynamics of IE in different timeframes, countries, and parameter spaces, providing ever-increasing accuracy. Nonetheless, these models neglect the spatial component of the IE dynamics. To this end, in this study, we explore the usage of a wide range of models, from linear regression to graph neural networks, in different spatio-temporal settings. Using the data between 2006 and 2023 about Sweden’s 21 regions, we evaluate the performance of these models in IE temporal prediction, given different resolutions of spatial information. Moreover, we evaluated the usefulness of such spatial data on both the regional and country levels. Our results show that machine learning based models consistently outperform both linear regressions and deep networks at the regional level, while deep learning becomes most powerful when predictions are aggregated to the national scale. We further demonstrate that the influence of one region on another declines with geographic distance, and that including data from neighboring regions improves predictive accuracy statistically significantly. However, adding all regions yields only small additional gains, with LR not improving beyond neighbors, indicating a highly non-linear spatial relationship. These findings suggest that informal economic activity in Sweden is best understood through spatially aware, data-driven models, which can capture both local and national dynamics more effectively than traditional approaches.

Technology’s impact on society and organizations is nothing new. The fundamental drivers of societal, economic, and technological growth have always been the drive of innovation. Economists use the term general purpose technologies (GPTs) to describe technological advances that fundamentally reshape industries, daily life, and the way organizations operate. These technologies often trigger or underpin industrial revolutions, which mark broader periods of economic and social transformation. Today, the most significant GPT of our era is artificial intelligence (AI). AI is not just an isolated technological advancement, it serves as a driving force for innovations, opening new possibilities across industries. AI is driving transformations in products, services, innovation processes, business models, and organizational ecosystems, reshaping how organizations compete and create value.

This dissertation explores the role of AI in organizational transformation, referred to in this thesis as AI Transformation (AIT). The study draws on four research articles:

The first article, a literature mapping, sets the foundation by identifying the existing body of knowledge on AIT. It highlights the critical role that AI plays in the reshaping of organizational structures, processes, and strategies. As organizations navigate the complexities of AI integration, understanding the landscape of literature provides valuable insights into the dynamics of this transformation.

The second article aims to advance the understanding of the AIT barrier that hinders value realization for public organizations. The study is based on a case study conducted in a medium-sized municipality in Sweden, providing in-depth information on the barrier to AIT in the public sector. This research introduces a new AIT framework, specifically designed to address the unique complexities of public sector digitalization. While AI facilitates innovation, operation, and organizational efficiency, it also presents barriers that must be overcome if AI is to succeed and transform organizations. In order to maximize AI’s potential and realize long-term value, public organizations must address these barriers effectively.

In the third article, AI Implementation in Manufacturing small medium enterprises (SMEs): A Resource Orchestration Approach, the focus shifts to understanding how AI technologies evolve and stabilize within manufacturing organizations. The study examines how manufacturing SMEs orchestrate resources for AI implementation, drawing on Resource Orchestration (RO) theory and recent research on AI adoption. Using multiple case studies of Swedish manufacturing SMEs, the article explores the approaches SMEs take to implement AI into their operations. The findings indicate that SMEs structure a portfolio of AI resources, emphasizing both acquisition and accumulation. These resources are then bundled into learning and governance capabilities, enabling SMEs to configure AI effectively within their organizations. Through a dynamic process of AI resource orchestration, SMEs leverage AI by mobilizing technologies, coordinating manufacturing processes, and empowering skilled employees.

Finally, the fourth article addresses AI maturity and organizational performance, emphasizing that as organizations progress along their AI maturity journey, they experience enhanced performance outcomes. This study examines the AI maturity levels of Swedish manufacturing SMEs and their impact on organizational performance, particularly in terms of profit and revenue. Using a sample of 246 established SMEs undergoing digital transformation, the study applies descriptive statistics, correlation analysis, and regression modeling to assess these relationships. The results show the multifaceted nature of AI adoption, demonstrating that while AI maturity enhances certain financial outcomes, its impact is shaped by a combination of technological, operational, and financial factors. By identifying these dynamics, this study provides a foundation for future research on the interplay between AI maturity and organizational performance in manufacturing SMEs.

Overall, the dissertation contributes to the field of AIT research and makes four important contributions to the literature on AIT, public sector, and SMEs. It offers a comprehensive overview of the field of AIT, maps the existing literature, and provides a foundation for further research. Furthermore, it advances understanding of the barriers that hinder value realization from AI in public organizations, such as fragmented structures, limited resources, and lack of strategic alignment. It also extends the AIT lens to the private sector, showing how manufacturing SMEs orchestrate resources and integrate AI technologies, but also showing that both sectors are experiencing similar AIT challenges related to leadership, culture, and infrastructure. Additionally, it investigates the relationship between AI maturity and organizational performance and demonstrates that, even though AI maturity can enhance financial outcomes, its effects are affected by underlying financial and organizational conditions.

This dissertation demonstrates that the challenge facing organizations is not simply how they can transform using AI, but about how organizations can prepare structurally, strategically, and culturally to support transformation outcomes across sectors.

Teknologins inverkan på samhället och organisationer är inget nytt. De grundläggande drivkrafterna bakom samhällelig, ekonomisk och teknologisk utveckling har alltid varit innovationens kraft. Ekonomer använder termen ”general purpose technologies” (GPT) för att beskriva teknologiska framsteg som i grunden omformar industrier, vardagsliv och hur organisationer fungerar. Dessa teknologier utlöser ofta, eller ligger till grund för, industriella revolutioner som markerar bredare perioder av ekonomisk och social omvandling. I dag är den mest betydelsefulla GPT i vår tid artificiell intelligens (AI). AI är inte bara ett isolerat teknologiskt framsteg, utan fungerar som en drivkraft för innovationer och öppnar nya möjligheter inom olika branscher. AI driver transformationer av produkter, tjänster, innovationsprocesser, affärsmodeller och organisatoriska ekosystem, och förändrar hur organisationer konkurrerar och skapar värde. Denna avhandling undersöker AI:s roll i organisatorisk transformation, vilket i denna studie benämns som AI-transformation (AIT). Studien bygger på fyra vetenskapliga artiklar:

Den första artikeln, en litteraturkartläggning, lägger grunden genom att identifiera den befintliga kunskapsbasen om AIT. Den framhäver den avgörande roll som AI spelar i omformningen av organisatoriska strukturer, processer och strategier. När organisationer navigerar komplexiteten i AI-integration ger förståelsen av forskningslandskapet värdefulla insikter i denna transformations dynamik.

Den andra artikeln syftar till att fördjupa förståelsen av de AIT-hinder som begränsar värdeskapandet i offentliga organisationer. Studien bygger på en fallstudie i en medelstor svensk kommun och ger en djupgående bild av de hinder som påverkar AIT inom den offentliga sektorn. Denna forskning introducerar ett nytt AIT-ramverk, särskilt utformat för att hantera den offentliga sektorns unika digitaliseringsutmaningar. Även om AI kan underlätta innovation, effektivitet och verksamhetsutveckling, innebär den också hinder som måste övervinnas för att AI ska kunna bidra till organisatorisk transformation. För att maximera AI:s potential och uppnå långsiktigt värde måste offentliga organisationer hantera dessa hinder på ett effektivt sätt.

I den tredje artikeln, AI Implementation in Manufacturing SMEs: A Resource Orchestration Approach, ligger fokus på att förstå hur AI-teknologier utvecklas och stabiliseras inom tillverkningsorganisationer. Studien undersöker hur små och medelstora tillverkningsföretag (SMF) orkestrerar sina resurser för AI-implementering, med stöd i Resource Orchestration (RO)-teorin och aktuell forskning om AI-adoption. Genom flera fallstudier av svenska tillverknings-SMF undersöks hur företagen införlivar AI i sina verksamheter. Resultaten visar att SMF strukturerar en portfölj av AI-resurser med betoning på både anskaffning och uppbyggnad. Dessa resurser sammanfogas till lärande- och styrningsförmågor som gör det möjligt för företagen att effektivt konfigurera AI använder SMF AI för att mobilisera teknologier, samordna tillverkningsprocesser och stärka kompetenta medarbetare.

Den fjärde artikeln behandlar AI-mognad och organisatorisk prestation och betonar att när organisationer utvecklas längs sin AI-mognadsresa uppnår de förbättrade resultat. Studien undersöker AI-mognadsnivåerna hos svenska tillverknings-SMF och deras påverkan på organisatorisk prestation, särskilt med avseende på vinst och intäkter. Med ett urval av 246 etablerade SMF som genomgår digital transformation tillämpas deskriptiv statistik, korrelationsanalys och regressionsmodellering för att analysera dessa samband. Resultaten visar den mångfacetterade karaktären av AI-adoption och påvisar att även om AI-mognad förbättrar vissa finansiella utfall, påverkas effekterna av en kombination av teknologiska, operativa och finansiella faktorer. Genom att identifiera dessa dynamiker bidrar studien till en grund för framtida forskning om samspelet mellan AI-mognad och organisatorisk prestation i tillverknings-SMF.

Sammantaget bidrar avhandlingen till forskningsfältet kring AIT och gör fyra viktiga bidrag till litteraturen om AIT, offentlig sektor och små och medelstora företag. Den erbjuder en omfattande översikt över AIT-fältet, kartlägger befintlig forskning och lägger grunden för vidare studier. Vidare fördjupar den förståelsen av de hinder som begränsar värdeskapande från AI i offentliga organisationer, såsom fragmenterade strukturer, begränsade resurser och bristande strategisk samordning. Den utvidgar också AIT-perspektivet till den privata sektorn genom att visa hur tillverknings-SMF orkestrerar resurser och integrerar AI-teknologier, men också att båda sektorerna upplever liknande AIT-utmaningar kopplade till ledarskap, kultur och infrastruktur. Dessutom undersöker avhandlingen sambandet mellan AI-mognad och organisatorisk prestation och visar att även om AI-mognad kan förbättra finansiella resultat, påverkas effekterna av underliggande finansiella och organisatoriska förutsättningar.

Denna avhandling visar att utmaningen för organisationer inte bara handlar om hur de kan transformeras med hjälp av AI, utan om hur de kan förbereda sig strukturellt, strategiskt och kulturellt för att möjliggöra hållbara transformationsresultat i olika sektorer.

On behalf of the Organizing Committee, it is our pleasure to present the proceedings of the 14th Scandinavian Conference on Artificial Intelligence (SCAI). After a break of almost 10 years, SCAI has been reestablished in a collaboration between the Swedish AI Society (SAIS) and the Norwegian AI Society (NAIS). As its predecessors, SCAI aims to bring together researchers and practitioners from the field of AI to present and discuss ongoing work and future directions. The conference provides a platform for networking among researchers as well as building relationships with practitioners, businesses, and other researchers involved in related fields.

Artificial intelligence (AI) is playing a leading role in the digital transformation of enterprises, particularly in the manufacturing industry where it has been responsible for a profound transformation in key business and production operations. Despite the accelerated growth of AI technologies, knowledge of the implementation of AI by small and medium-sized enterprises (SMEs) remains underexplored. Thus, this study seeks to examine how manufacturing SMEs orchestrate resources for AI implementation. Building on the resource orchestration (RO) theory and recent work on AI implementation, we investigate multiple case studies involving manufacturing SMEs in Sweden operating in the packaging, plastic, and metal sectors. Our findings indicate that SMEs structure a portfolio based on acquiring and accumulating AI resources. AI resources are bundled into learning and governance capabilities to leverage configurations for AI implementation. Through a dynamic process of AI resource orchestration, SMEs effectively leverage AI resources and capabilities by mobilising technologies, coordinating manufacturing processes, and empowering skilled people. This research contributes to existing practice and the academic literature on AI implementation, highlighting how SMEs orchestrate AI resources and capabilities to drive an organisation’s digital transformation whilst creating a competitive advantage.

Software is a key component of most products, services, industrial processes, and back-office functions. Thus, companies may gain an advantage by establishing fast feedback cycles to improve their software.

Continuous software engineering (CI/CD) is being primarily studied as an engineering topic. However, the rest of the organization needs to align and be prepared to utilize the benefits of CI/CD.

In this paper, we explore the overlap between CI/CD and digital transformation (DT). We study literature in both areas to develop a map of conditions, mechanisms, and outcomes. As a result, we present a unified perspective of CI/CD and DT. We found that CI/CD can be seen as an implementation of DT in a software organization. DT perspective can help to guide the adoption of CI/CD from an organizational perspective. © 2022 ACM.

Background: Intelligent software is a significant societal change agent. Recent research indicates that organizations must change to reap the full benefits of AI. We refer to this change as AI transformation (AIT). The key challenge is to determine how to change and which are the consequences of increased AI use.

Aim: The aim of this study is to aggregate the body of knowledge on AIT research.

Method: We perform an systematic mapping study (SMS) and follow Kitchenham’s procedure. We identify 52 studies from Scopus, IEEE, and Science Direct (2010–2020). We use the Mixed-Methods Appraisal Tool (MMAT) to critically assess empirical work.

Results: Work on AIT is mainly qualitative and originates from various disciplines. We are unable to identify any useful definition of AIT. To our knowledge, this is the first SMS that focuses on empirical AIT research. Only a few empirical studies were found in the sample we identified.

Conclusions: We define AIT and propose a research agenda. Despite the popularity and attention related to AI and its effects on organizations, our study reveals that a significant amount of publications on the topic lack proper methodology or empirical data.

Real-world application of data-driven and intelligent systems (AI) is increasing in the private and public sector as well as in society at large. Many organizations transform as a consequence of increased AI implementation. The consequences of such transformations may include new recruitment plans, procurement of additional IT, changes in existing positions and roles, new business models, as well as new policies and regulations. However, it is unclear how this transformation varies across different types of organizations. We study the effects of bottom-up approaches, such as pilot projects and mentoring to specific groups within organizations, and aim to explore how such approaches can complement the top-down approach of strategic AI implementation. Our context is the public sector. Our goal is to acquire an improved understanding of how and when AI transformation occurs in the public sector, which are the consequences, and which strategies are fruitful or detrimental to the organization. We aim to study public sector organizations in Sweden, Norway, New Zealand, Germany, and The Netherlands to learn about potential similarities and differences with regard to AI transformation.