Organizations in all industries have reached a transition point because of the rapid development of digital technology. Digitalization and AI has therefore become the driving force for transformation within today's organizations to remain competitive in the digital era. The forest industry is no exception. However, digital transformation through AI within organizations is synonymous with high complexity and the forestry industry faces unique challenges to overcome because of the industry's traditional approach and the corporate culture that comes with it. This approach creates challenges for technology to be able to take an active and leading role. Problems that can emerge with digital transformation are one of the most important topics that have been researched for a long time. But few studies have examined digital transformation through AI in the forestry industry. Against this background, the purpose of this study is to analyze the barriers that can inhibit AI-Readiness in the forest industry. To answer our research question, we have used a qualitative case study with semi-structured interviews. The semi-structured interviews are based on the framework Technological Frames which intends to examine interpretation about information technologies. The results shows that barriers that can inhibit AI-Readiness exist which can be linked to organizational culture, user experiences and IT-strategies.

Background: AI in Purchasing and Supply Management (PSM) enhances business operations but faces challenges in adoption due to limited research and AI readiness assessment. Although existing research explores AI's potential, the issue of assessing and achieving AI readiness in PSM remains underexamined. Exploring this gap is crucial to understanding how AI can effectively transform procurement processes and improve strategic operations.  Purpose: This study aims to identify and evaluate the essential capabilities that PSM organizations need to develop for AI readiness, using a dynamic capabilities framework to provide insights for both academia and practitioners.  Method: Grounded theory is applied for its flexibility and constructivist principles, allowing theories to emerge from the data collected through semi- structured interviews, providing a comprehensive understanding of AI readiness in PSM. The primary data consisted of 13 interviews with AI users, implementers, and developers.  Conclusion: Identified capabilities needed for successful AI implementation in PSM, include robust technological infrastructure, effective AI governance, and the importance of communication and continuous learning. The study concludes that AI readiness in PSM requires a holistic strategy and dedicated leadership to align technology, strategic goals, and people.

Livsmedelsindustrin står inför stora utmaningar i framtiden, varav en är att erbjuda och producera livsmedel till en växande population på ett hållbart sätt. Ett sätt att övervinna denna utmaning är att implementera AI-teknik som kan användas för att effektivisera och främja hållbar utveckling inom livsmedelsindustrin. Syftet med studien är därför att kartlägga nivån av AI-readiness hos livsmedelsföretag i Norden, det vill säga undersöka hur redo dessa verksamheter är att implementera AI-teknik. Frågeställningen har besvarats genom en digital enkätundersökning baserat på det teoretiska ramverket TOE - Technology, Organization and Environment. Enkäten besvarades av 59 anonyma respondenter. Svaren kategoriserades baserat på det huvudsakliga verksamhetslandet för respondentens företag, den del av industrin företaget främst är verksamt inom och företagets storlek. Det insamlade datat har analyserats med hjälp av Chi2-test som resulterade i insikten att större företag har högre nivå av AI-readiness än små företag. Resultatet indikerade även på tre förbättringsområden för att öka nivån av AI-readiness. Företag bör i större utsträckning utveckla en strategisk plan för implementering av AI-teknik, säkerställa tillräckligt med mänskliga resurser med rätt kompetens och öka kunskapen om externa regleringar bland sina anställda. Den kunskap som studien genererar kan vara relevant för företag som arbetar med implementering av AI-teknik, för företag verksamma inom livsmedelsindustrin som vill öka sin AI-readiness samt för framtida forskning inom liknande områden. / The food industry faces major challenges in the future, one of which is to provide and produce food for a growing population in a sustainable way. One way to overcome this challenge is to implement AI-technologies that can be used to promote sustainable development in the food industry. The purpose of this study is therefore to map the level of AI-readiness among food companies in the Nordic region, specifically to investigate how ready these businesses are to implement AI-technology. The question has been answered through a digital survey based on the theoretical framework TOE - Technology, Organization and Environment. The survey was answered by 59 anonymous respondents. The answers were categorized based on the main country of operation of the respondent's company, the part of the industry the company is mainly active in and the size of the company. The collected data has been analyzed using Chi2-tests which resulted in the insight that larger companies have a higher level of AI-readiness than smaller companies. The result also indicated three areas of improvement to increase the level of AI-readiness. Companies should to a greater extent develop a strategic plan for the implementation of AI-technology, ensure sufficient human resources with the right skills and increase the knowledge of external regulations among their employees. The knowledge generated by the study can be relevant for companies working on the implementation of AI-technology, for companies operating in the food industry that want to increase their AI-readiness, and for future research in similar areas.

Artificial intelligence is a complex and widespread technology that is increasingly implemented in today’s businesses. As with implementation in general, organizations are faced with different types of barriers when implementing AI. This paper has explored barriers a public organization faces in its AI implementation efforts. We have generated new knowledge and expanded on prior research on AI readiness. A case study was chosen as the research method, paired with semi-structured interviews and documentation to gather the empirical data. The result of the study highlights the specific barriers faced by the public organization. Additionally, we formed a new perspective called the AI resourcing perspective from the data and prior research, which later turned into an AI resourcing model. The model serves as a practical tool to support organizations on their journeys toward making AI a resource.

Background - As a result of the latest advances in artificial intelligence (AI), the world ofbusiness is facing a major transformation where basic organizational principlesare redefined initiating a new era. It is predicted that AI in the coming decadeswill make a significant imprint and organizations aiming to stay at the forefrontcannot afford not to change. AI adoption can bring great benefits to organizationswhere a crucial factor is to establish AI readiness. However, as in any change,different perceptions are raised among employees which can either hinder orfoster organizational AI readiness, placing leaders in a crucial position. Purpose - The purpose of this study is to investigate how managers can foster organizationalAI readiness by understanding distinctive features of employee AI attitudes. Byidentifying how employees develop change attitudes towards AI, the opportunityto explore how managers should respond to these attitudes in order to achieve AIreadiness opens. Method - To gain a greater understanding of the phenomenon managing AI attitudes and tofulfil the purpose of the study, a mix of a qualitative and quantitative researchmethodology was used. The empirical data were abductively collected through asingle case study via a survey containing 80 respondents and through a focusgroup including six participants holding different roles affected by an AIimplementation. The empirical data were processed using thematic analysis andfurther analysed through systematic combining. Conclusions - The conclusions in this study confirm already existing theory. It also expands itas the phenomenon managing attitudes towards AI change was placed in a newcontext. The research results indicate that employees’ change attitudes towardsAI are affected by the organizational AI maturity, personal interest, and personaland organizational AI knowledge. They also indicate that employees develop theirchange attitudes towards AI depending on how managers handle or not handletheir attitudes. Finally, four dimensions along which leaders should manageemployee change attitudes to promote AI readiness were elaborated.

AI's ability to process large data sets and make intelligent decisions has revolutionized the industrial landscape and opened new avenues for innovations that can contribute to a more sustainable future. It plays a pivotal role as a catalyst for digital transformation, particularly in sectors such as energy production, where it offers significant potential to promote sustainable development. This study researches the factors that condition artificial intelligence (AI) implementation in the energy sector based on the Technological-Organizational-Environmental (TOE) framework. It employs a deductive approach to identify AI implementation's technological, organizational, and environmental factors through a qualitative case study approach involving an energy company in Sweden. Our findings indicate that technological readiness and organizational alignment are essential, with differences in environmental pressures further affecting AI readiness. The study concludes that successful AI implementation depends on robust technological structure and organizational readiness, with varying environmental factors regulating these conditions. This research contributes to the literature by emphasizing the critical role of AI readiness in optimizing operations and driving innovation within the energy sector, accentuating the need for companies to enhance their AI capabilities to manage integration challenges effectively. This study enriches the TOE framework by introducing an additional dimension to the environmental factor, incorporating Network considerations where boundaries are undefined due to digitalization.

Artificial intelligence (AI) technologies are developing rapidly and cause radical changes in organizations, companies, society, and individual levels. Managers are facing new challenges that they might not be prepared for. In this work, we seek to explore managerial challenges experienced while implementing and using AI technologies from the researchers’ perspective. Moreover, we explore how appropriate ethical deliberations should be applied when using big data concerning AI and the meaning of understanding or defining it. We describe qualitative research, the triangulation that includes related literature, in-depth interviews with researchers working on related topics from various fields, and a focus group discussion. Our findings show that AI algorithms are not universal, objective, or neutral and therefore researchers believe, it requires managers to have a solid understanding of the complexity of AI technologies and the nature of big data. Those are necessary to develop sufficient purchase capabilities and apply appropriate ethical considerations. Based on our results, we believe researchers are aware that those issues should be handled, but so far have too little attention. Therefore, we suggest further discussion and encourage research in this field.

Titel: Beredskap för AI implementering: En fallstudie om beredskapsfaktorer för AI implementeringar på svenska IT-företag Forskningsfråga: Vilka beredskapsfaktorer krävs för implementering av AI på svenska IT-företag? Syfte: Undersöka vilka beredskapsfaktorer svenska IT-företag behöver för att skapa en lyckad AI implementering. Genomförande: Studien genomfördes som en fallstudie. En litteraturstudie utfördes för att sammanfatta tillgänglig och relevant vetenskaplig forskning inom området och fungerade som forskningsöversikt till studien. Datainsamlingen bestod av åtta semistrukturerade intervjuer med respondenter från sju olika företag. Material från litteraturstudie jämförs med insamlade och bearbetade data från respondenterna för att kunna hitta likheter och skillnader för att på så sätt kunna besvara studiens frågeställning. Metod: Studien har en induktiv ansats med en kvalitativ inriktning som karaktäriseras av analytiska tolkningar utifrån litteratur och intervjuer. Resultat: Utifrån analysen, en jämförelse av tidigare litteratur och insamlade data, nåddes resultat i form av identifierade beredskapsfaktorer som företag borde prioritera för att lyckas med sin AI implementering. Beredskapsfaktorer som identifierats är data, kunskap, syfte, involvering av anställda, resurser samt etik.Slutsats: Studiens slutsats visar på att beredskapsfaktorerna som tagits fram kan hjälpa företag att bättre förbereda sig inför en AI implementering och därigenom också öka möjligheterna för att åstadkomma en lyckad AI implementering på svenska IT-företag. / Title: Readiness for AI Implementation: A case study on readiness factors for AI implementations in Swedish IT companies search Question: Which readiness factors are required for an implementation of AI in Swedish IT companies? Purpose: Investigate which readiness factors Swedish IT companies need to create a successful AI implementation. Implementation: The study was performed as a case study. A literature study was conducted to summarize available and relevant research in the research area and served as a research overview for the study. The data collection consisted of eight semistructured interviews with respondents from seven different IT companies. Data from the literature study were compared with data collected and processed from the respondents, in order to find similarities and differences to answer the study's research question. Method: The study had an inductive approach with a qualitative focus, characterized by analytical interpretations based on literature and interviews. Findings: Based on the analysis, a comparison of previous literature and collected data, readiness factors required for AI implementations were identified. Readiness factors identified were data, knowledge, purpose, employee involvement, resources and ethics of which companies should prioritize to succeed with their AI implementation. Conclusion: The conclusion of the study indicates that the readiness can help companies to better prepare for an AI implementation and thereby also increase the opportunities for achieving a successful AI implementation in Swedish IT companies.

The popularity of Artificial Intelligence (AI) technologies in various industries is increasing now more than ever before due to the ability of improving efficiency, enhancing decision-making and automating workflows. This demands that organizations need to be prepared to adopt these technologies to keep their competitive advantage and utilize the benefits in today's fast-paced business environment. There is a lack of guidance for organizations to adopt AI and further research of the organizational readiness factors is therefore needed to make sure the adoption of it is successful. The purpose of this research was to expand the knowledge of key factors that matter when organizations in the software industry want to create the best conditions before adopting the AI technologies in their business processes. The main contexts and factors were investigated with the technology-organizational-environmental (TOE) framework in synthesis with the technological readiness index (TRI) to get the perspective of both readiness and adoption. To answer the research questions that originated from the purpose, a qualitative research method was chosen where semi-structured interviews were conducted with managers with knowledge and experience in the field, as part of the empirical findings process. The most important contributing factor for readiness was communication, and the most obstructing factor was the discomfort of technological innovations such as AI. The main factors for a successful adoption were found to be the availability of slack resources and skilled labor and that the conditions of AI readiness are dealt with before adoption. The factor that could be classified as a main hindering factor in the adoption process was found to be a shortage of skilled labor in the market, with the right kind of knowledge and experience. / Populariteten för teknologier inom artificiell intelligens (AI) ökar nu mer än någonsin tidigare i olika branscher på grund av förmågan att förbättra effektiviteten, förbättra beslutsfattandet och möjligheten att automatisera arbetsflöden. Detta kräver att organisationer måste vara beredda att använda dessa teknologier för att behålla sina konkurrensfördelar och utnyttja fördelarna i dagens affärsmiljö där beslut fattas fort. Det finns dock en brist på vägledning för organisationer att ta till sig AI och ytterligare forskning om organisatoriska beredskaps faktorer behövs därför för att säkerställa att implementeringen av dessa teknologier blir framgångsrik. Syftet med denna forskning var att utöka kunskapen om nyckelfaktorer som verkligen betyder något när organisationer inom mjukvaruindustrin vill skapa de bästa förutsättningarna innan de tar till sig AI-teknologierna i sina affärsprocesser. De huvudsakliga sammanhangen och faktorerna undersöktes med hjälp av technology-organizational-environmental (TOE) ramverket i syntes med technological readiness index (TRI) för att få perspektiv på både beredskap och implementering av AI. För att besvara forskningsfrågorna valdes en kvalitativ forskningsmetod med semistrukturerade intervjuer för att samla in empirisk data. Dessa intervjuer genomfördes med chefer inom mjukvaruindustrin som hade erfarenhet kring implementering av AI. Den viktigaste bidragande faktorn för beredskapen var kommunikation, och den mest hindrande faktorn var obehaget för innovationer som AI. De huvudsakliga faktorerna för en framgångsrik implementering visade sig vara tillgången på överskotts resurser och kvalificerad arbetskraft och att villkoren för AI-beredskap hanteras innan implementering. Den faktor som kunde klassificeras som en huvudsaklig hämmande faktor i implementeringsprocessen visade sig vara brist på kvalificerad arbetskraft på marknaden, med rätt sorts kunskap och erfarenhet.

This thesis explores the challenges microbreweries face when implementing ArtificialIntelligence (AI) technologies and undergoing digital transformation, specifically focusingon the interplay between AI-readiness and institutional logics. By examining the AIreadiness framework proposed by Holmström (2022), the study aims to understand howmicrobreweries manage multiple logics in response to institutional complexity triggered byAI-initiatives. Data collection for this thesis was conducted through semi-structuredinterviews with employees of three different microbreweries in Sweden. These participantswere selected using purposive sampling to obtain a diverse range of perspectives on AIimplementation within the industry. The study demonstrates the importance of continuousadaptation and evaluation in integrating AI technologies into existing practices. It concludesthat organizations need to balance digital transformation with potential conflicts ininstitutional logics and develop strategies to integrate new technologies without disturbingtheir stability. For microbreweries, fostering open communication, collaboration, and ashared understanding of goals can mitigate potential barriers. Ultimately, recognizing andresolving conflicts allows organizations to leverage the transformative potential of AItechnologies and thrive in a competitive, technologically driven landscape.