The purpous of this qualitative study is to spotlight some of the followers/employess technostress concerns associated with future AI implementation in one of the Swedish public centers. The methodology used in this research is a deductive approach and primary data was collected by conducting nine semi-structured interviews with followers /mployess working at a social care center in Växjö Municipality, Sweden. The findings support that transformational leadership style can be utilized to address followers´technostress fears in the understudy social care center through enhancing followers engagement. Conclusively, transformational leadership style interaction with followers enables leader to enhance followers engagement and mitigate technostress factors generated by AI implementation.

ABSTRACT Date: 2023-05-31 Level: Bachelor/Master thesis in Business Administration, 15 cr Institution: School of Business, Society, and Engineering, Mälardalens University Authors:  Axel Bodén   Gustav Dahlstedt   (99/08/12)   (99/03/01) Title: Harnessing the Power: Exploring Opportunities and Challenges for Successful AI Adoption in Organizations Supervisor: Ali Farashah Keywords: Artificial intelligence, Management, Adoption, AI Implementation Research Questions: 1. What are the key challenges and opportunities faced by organizations when adopting Artificial Intelligence technologies? 2. How can they effectively navigate this process to achieve positive outcomes? Purpose  The purpose of this article is to examine and explore the different opportunities and challenges that organizations face during the implementation phase of artificial intelligence (AI) whilst also exploring how organizations can navigate through the potential challenges. Furthermore, this article aims to answer the two research questions that are (1) “What are the key challenges and opportunities faced by organizations when adopting Artificial Intelligence technologies?” and   (2) “How can they effectively navigate this process to achieve positive outcomes?”. AI has recently attracted a large amount of attention with the release of chatbots like ChatGPT, many now perceive the immense usefulness of AI and dream of the opportunities that come with it. However, few understand that AI is more than just chatbots, and even fewer understand or know how to implement it in their organizations, therefore, this article aims to provide necessary research that can provide businesses with knowledge of how to successfully implement AI. Method  In this research paper, the authors aim to provide primary data through academic and qualitative research. 10 interviews were conducted primarily over Zoom with managers in the business that have experience working with AI either daily or occasionally. The interviews were semi-structured and synchronous. These can be found in Appendix A. The authors decided upon interviews via online platforms mainly due to convenience for managers, time zones, and physical distances. Conclusion  In conclusion, it is highlighted that the largest challenges that organizations face are related to AI implementation and the authors suggest strategies for navigating through the challenges to successfully implement AI by proposing partnerships or government-funded research institute projects. Furthermore, it is argued about the practical and the theoretical implications.

As technology continues to develop and form tools capable of helping people andorganisations become more efficient, there is a need for organisations to stay up-to-dateon the various tools and trends to stay relevant on the market and not fall behind. Artificialintelligence (AI) is one of those tools that is emerging on the market and challengingorganisations to implement the technology to stay relevant. However, not allorganisations know how to properly implement AI in their organisation, resulting in alarge array of barriers that hold organisations back from technological implementation.After studying the research papers out there, a research gap was discovered on how toovercome the barriers from an organisational perspective. Throughout the thesis, it wasdetermined that one common misconception is that AI is a solution that will solve anorganisation's many flaws, whereas in fact AI is a tool used to make work more efficientfor the organisation. As a result, it was determined that competence is the most importantaspect in overcoming the barriers to AI implementation.

Titel: Framgångsfaktorer vid implementering av artificiell intelligens Författare: Wilhelm Abele och Simon Starfelt Handledare: Jon Engström Nyckelord: Artificiell intelligens (AI), AI-implementering, framgångsfaktorer, hinder, organisation. Bakgrund: Artificiell intelligens (AI) har funnits sedan år 1956 men det är först det senaste årtiondet som AI blivit applicerbart inom organisationer. Forskning tyder på att AI har stor värdepotential och företagsledare menar att AI kommer ha stor påverkan på organisatoriska processer. Samtidigt som värdepotentialen ser lovande ut, visar undersökningar att majoriteten av de företag som investerar i AI upplever minimalt eller inget värde från investeringen. Tidigare forskning menar att svårigheterna ligger i implementeringsprocessen av AI och att organisationer bör ha ett affärsmässigt perspektiv för att uppleva värde. Därmed ska de faktorer som påverkar implementeringsprocessen undersökas. Syfte: Syftet med studien är att undersöka framgångsfaktorer vid implementering av AI i en organisation. Vidare är syftet att skapa ett ramverk organisationer kan förhålla sig till under implementeringsprocessen av AI. Genomförande: Studien är en tentativ flerfallstudie med kvalitativ karaktär. Empirin har samlats in genom intervjuer från sex organisationer av olika karaktär, där de antigen utvecklar AI-system själva eller köpt in externt. Detta för att skapa ett brett perspektiv för vilka framgångsfaktorerna är. Slutsats: Studien resulterar i ett ramverk som innefattar de faktorer som anses avgörande för en framgångsrik implementering av AI i en organisation. Ramverket är uppdelat i tre faser: (1) Förberedelsefas, (2) Implementeringsfas och (3) Utvärderingsfas. Framgångsfaktorerna kopplade till en lyckad implementering är: affärsdrivet syfte, involvering av intressenter, datakvalitet- och hantering, kunskapsdelning och organisationsstruktur, samt utvärdering och feedback. / Title: Success factors in the implementation of artificial intelligence Authors: Wilhelm Abele & Simon Starfelt Supervisor: Jon Engström Key Words: Artificial intelligence (AI), AI-implementation, Success factors, Obstacles, Organization. Background: Artificial intelligence is a concept that has existed for a while; however, it is only recently that the technology has caught up with the concept. Recent studies show that many organizations realize AI’s huge value potential, however, the majority of the organizations that have invested in AI generates minimal or no business value at all from the investment. Research shows that organizations face complications during the implementation process of AI and in order to generate value, the purpose of the AI solution should be business-driven, not IT-driven. Therefore, shall the factors impacting the implementation process of AI be studied further. Purpose: The purpose of this study is to examine what factors determine success when implementing AI solutions in organizations. In addition, an aim of this study is to suggest a framework for implementing AI solutions that organizations can use as a guide. Completion: The study is a tentative, multiple case study characterized by qualitative approach. The empirical data has been collected through interviews with six different companies that either produce and deliver AI solutions, or have bought AI solutions. These companies have been selected through a target-oriented selection process. Conclusion: The study results in the creation of a framework consisting of the factors deemed decisive for a successful implementation of AI in an organization. The framework is divided into three phases: (1) Preparation phase, (2) Implementation phase, and (3) Evaluation phase. The success factors associated with a fruitful AI implementation are: a business-driven purpose, stakeholder involvement, data- quality and handling, knowledge sharing and organizational structure, evaluation and feedback.

Problemformulering: På vilka sätt hanteras och påverkas en organisations intressenter av implementeringen av AI-system?  Syfte: Syftet med denna studie är att utifrån en organisations intressenters uppfattning kartlägga på vilka sätt intressenterna hanteras och påverkas av implementering av AI-system. Metod: Studien har utgångspunkt i kvalitativ forskningsstrategi med en deduktiv ansats. Den genomförda studien är en fallstudie, där Länsförsäkringar har studerats. Det empiriska materialet är insamlat genom tio semistrukturerade intervjuer.  Slutsats: Med vår studie kan vi se att implementeringen av Länsförsäkringars chatbot påverkar de anställda. Den potentiella utvecklingen av AI däremot tenderar att påverka flera intressentgrupper. Vidare kan vi se i studiens resultat svårigheter att identifiera organisationens intressenter samt svårigheter att prioritera och värdera intressenter, vilket överlag överensstämmer med den framtagna teorin gällande intressentmodellen. Vi kan därför dra slutsatsen att Länsförsäkringar bör identifiera intressenter och dess påverkan av utvecklingen av AI för veta hur intressenter ska hanteras. / Research question: In what ways is an organization's stakeholders handled and affected by the implementation of AI-systems? Purpose: The purpose of this study is to map, based on the perception of an organization's stakeholders, in what ways stakeholders are handled and affected by the implementation of AI-systems.  Method: The study is based on a qualitative research strategy with a deductive approach. The completed study is a case study, where Länsförsäkringar has been studied. The empirical material is collected through ten semi-structured interviews. Conclusion: With our study, can we see that the implementation of Länsförsäkringar's chatbot affects the employees. The potential development of AI, on the other hand, tends to affect several stakeholder groups. Furthermore, we can see in the results of the study difficulties in identifying the organization's stakeholders as well as difficulties in prioritizing and evaluating stakeholders, which is generally in line with the developed theory regarding the stakeholder model. We can therefore conclude that Länsförsäkringar should identify stakeholders and their impact on the development of AI in order to know how stakeholders should be handled.

Purpose – the purpose of this study is to investigate managerial and organizational barriers that are associated with artificial intelligence (AI) and develop a structured process to overcome the organizational barriers throughout different phases of the implementation process. Method – The study applied a qualitative research approach that consisted of multiple case studies from various organizations in traditional industries. Each organization worked with an AI-projects that were based on application of machine learning (ML).  The respondents came from various positions from the AI-project and were interviewed. The collected data was analyzed by using a thematic analysis with 17 interviews in total.  Findings – The study found four barriers in total from pre-implementation, implementation, and post-implementation phases of AI. These were: lack of use-case definition, low ai-knowledge, missing appropriate data, and end-user misalignment. The study would present key-activities to overcome the AI-barriers categorized that are presented in three phases: defining AI-transformation, anchoring AI-implementation and optimizing AI-usage. Theoretical contribution - Firstly, the study highlighted underlying implementation barriers in traditional industries that were business and managerial related. Secondly, the study contributed with an empirically rooted structured AI-implementation process framework. These findings extend current dialogues in the literature on challenges related to AI and connect them to specific phases in the AI-implementation process. These findings also extend current dialogues in the literature on challenges related to AI and connect them to specific phases in the AI-implementation process. Practical Implications - The practical implication of this study highlighted that there existed a lack of clearly defined strategies for implementing AI-solutions in traditional industries which this study covers by developing a basis to build on. Limitations and Future research - The study investigated a handful of organizations in different industries. Because of time- and resource constraints, increased research scope could provide more insightful perspectives which could be beneficial. In addition, because the study itself was based as a qualitative study, the methodology of the project could be prone to inconsistencies or a lack of coherence. As this approach was based on phrasing, some phrases may not be able to capture the full meaning of what was articulated. For future research proposals, a quantitative research method of this subject could give further breadth to the literature by investigating likely correlations.

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.

This thesis projct examines the implementation of Artificial Intelligence (AI) in Project Management, focusing on its impact on the working personnel. AI has the potential to improve management processes, increase efficiency, and enhance decision-making. This thesis aims to explore the implementation of AI in project management with a focus on the impact on working personnel. The study analyzes the benefits and challanges of AI implementation, the impact on the role of project managers and team members, and the ethical considerations of using AI in project management. The research incorporates a literature review and semi-structured interviews conducted with project managers from Greece and Sweden to gather comprehensive insights. The findings suggest that the integration of AI in project management can significantly benefit working personnel by reducing workload, increasing accuracy, and providing better insights. However, the implementation of AI requires careful consideration of ethical issues and proper training of personnel. / Detta examensarbete undersöker implementeringen av artificiell intelligens (AI) i projektledning, med fokus på dess inverkan på arbetsgivare. Studien innehåller en literaturgenomgång och semistrukturerade intervjuer med projektledare från Grekland och Sverige för att smala in omfattande insikter. AI har potential att förbättra projektledningsprocesser, öka effektiviteten och förbättra beslutsfattandet. Studien analyserar fördelarna och utmaningarna med AI-implementering, inverkan på rollen för projektledare och teammedlemmar, och de etiska övervägandena av att använda AI i projektledning. Resultatet tyder på att integrationen av AI i projektledning avsevärt kan gynna arbetande personal genom att minska arbetsbelastningen, öka noggrannheten och ge bättre insikter. Men implementeringen av AI kräver noggrant övervägande av etiska frågor och korrekt utbildning av personal.

AI in medical imaging is promising. Breast cancer screening has particularly seen advancements as researchers have demonstrated how commercially available AI algorithms could detect breast cancer at the same level as the best radiologists. The clinical uptake of AI implementations has however been slow and research studies on the real-life effects AI would have when it is implemented in healthcare settings, are lacking. As AI is integrated into the workflows of hospitals, new risks, are likely to be introduced. The breast radiology department at the hospital of Capio S:t Göran is among the first in the world to clinically let AI act as an independent reader, replacing one of the two radiologists reading the mammograms. This study thus aimed to investigate how a hospital like Capio S:t Göran may prepare for the clinical uptake of AI by exploring risks from an enterprise risk management perspective, i.e., looking beyond risks associated with patient safety, and proposing risk mitigation strategies. Data was qualitatively collected through different means. Brainstorming sessions were conducted with personnel at the hospital, either directly or indirectly involved with AI, with the purpose of identifying risks. Two external experts with competencies in cybersecurity, machine learning, and the ethical aspects of AI, were interviewed as a complement. Insights were also gained via observations at the hospital and internal documents/information. The risks identified were analyzed according to an enterprise risk management framework adopted for healthcare, that assumes risks to be emerging from eight different domains. Additionally, appropriate risk mitigation strategies were identified and discussed. The findings from the study demonstrates 23 risks associated with the clinical AI implementation in medical imaging and proposes risk mitigation strategies to each identifiedrisk. Not only does the study indicate the emergence of clinical/patient safety risks, but it also shows that there are operational, strategic, financial, human capital, legal, and technological risks. In addition, the study emphasizes the existence of possible synergies between the risks. The study concludes on the significance for hospitals to view risks holistically and to manage them proactively. / Användandet av AI inom bild- och funktionsmedicin är lovande. Det har framför allt skett framsteg inom bröstcancerscreening i takt med att forskare lyckats demonstrera hur kommersiellt tillgängliga AI algoritmer kan detektera bröstcancer på samma nivå som de bästa bröstradiologerna. AI införandet inom klinisk praxis har däremot varit långsam och det finns en avsaknad på forskningsstudier som studerat effekterna av ett AI-införande när det implementeras i den verkliga sjukvårdsmiljön. När ett AI system ska integreras i ett sjukhusarbetsflöde är det sannolikt att nya risker introduceras. Mammografiavdelningen på Capio S:t Görans sjukhus är det första sjukhuset i världen som ska börja använda AI kliniskt i syfte att ersätta en av två radiologer. Planen är att låta ett AI-system agera som en oberoende granskare och därmed ersätta en av de två radiologer som normalt sett granskar mammografibilderna. Syftet med denna studie har därav varit att undersöka hur sjukhus, såsom Capio S:t Göran bör förbereda sig för ett kliniskt införande av AI. Detta har gjorts genom att både identifiera risker från ett Enterprise Risk Managementperspektiv, vilket ur en sjukvårdskontext bland annat innebär att titta bortom patientsäkerhetsrisker, samt identifiera och föreslå riskhanteringsstrategier. För att identifiera risker hölls brainstorming sessioner med personal på Capio S:t Görans sjukhus med antingen direkta eller indirekta kopplingar till AI implementeringen. Detta kompletterades med två expertintervjuer där den ena hade kompetens inom cybersäkerhet och maskininlärning och den andra inom de etiska aspekterna av AI. Dessutom erhölls insikter via observationer gjorda på sjukhuset samt genom tillgång till intern information. Riskerna som identifierades analyserades därefter enligt ett Enterprise Risk Management ramverk som anpassats till sjukvården och som utgår från åtta olika risk domäner. Till sist diskuterades och identifierades lämpliga riskhanteringsstrategier. Resultatet från studien kunde indikera 23 risker relaterade till ett kliniskt användande av AI inom bild- och funktionsmedicin samt föreslå riskhanteringsstrategier till respektive risk som identifierades. Studien kunde identifiera operativa risker, patientsäkerhetsrisker, strategiska risker, finansiella risker, humankapitalrisker, juridiska risker och tekniska risker samt synliggöra eventuella synergier som existerar mellan riskerna. Slutsatsen av studien är att en holistisk syn på riskhantering och att en proaktiv hantering av risker är av avgörande betydelse för sjukhus som ska genomgå en implementering av AI.

While the proliferation of AI technologies offers opportunities for the workplace and its processes, their implementation in business effectively is still a challenge. Today, companies require strategic guidance in their AI management. Accordingly, there is a need for more research on the topic with a holistic approach including governance of data. Considering the challenges of the private sector and the gap in the IS research, this thesis focuses on the barriers to implementing AI in the private sector. It specifically assesses the sociotechnical mechanisms for AI evolution in the case of the agricultural machinery industry. The conducted case study suggests an overall approach including data governance for AI implementation and an alignment between the digital, it, and business strategies. Based on the research findings, this study suggests a model for AI management with three parts: The opportunities and the new data generation to realize these opportunities lie on the benefit side of the digital transformation while the sociotechnical mechanisms to tackle the barriers stand at the core. By introducing a model for AI management, the thesis offers a roadmap for the case company while bringing a new perspective to the literature and further research.