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.

The rapid development of artificial intelligence (AI) has led to significant changes in the business environment and academic discussions. AI boosts productivity and positively impacts the competitive advantage of organisations. However, it also has its dark sides, such as prejudice, non-transparent processes, and people's fears that AI will be able to take their jobs in the future. The successful implementation of AI in organisations depends on several factors, including geopolitical, cultural, ecosystem, organisational, and individual factors. Geopolitical context and cultural differences can play an important role in the adoption and implementation of AI in organisations. This study examines the influence of geopolitical and cultural contexts on the decision-making process for the adoption and implementation of AI by managers from the fashion retail industry in Sweden and India. Given the extensive scope of these contexts, the authors narrowed their focus on specific factors. In the cultural context, the authors consider selected dimensions of the GLOBE project that reflect national culture. Within the Geopolitical context, particular attention is given to aspects such as data access and control, as well as the regulatory framework. In the course of this study, semi-structured interviews were conducted, and additional secondary data was studied. The study showed that the specifics of data access and control, as well as governmental legislative regulation, directly affect the decision-making process regarding the adoption and implementation of AI. As for the cultural context, here the degree of influence is heterogeneous, and decision-making on the implementation of AI is not always subject to the direct influence of the national cultural factors.

Artificial Intelligence (AI) holds transformative potential for many fields including the finance sector. However, identifying suitable tasks for artificial intelligence implementation remains a challenge. This study proposes the artificial intelligence readiness task assessment tool, empowering finance professionals to assess task suitability for AI implementation from a bottom-up perspective. Artificial intelligence adoption often encounters barriers such as costs, compatibility, and skill gaps. The proposed tool addresses these challenges by allowing finance professionals to gauge artificial intelligence suitability for specific tasks without requiring extensive AI knowledge. The tool follows a design science research approach, ensuring it is user-friendly and effectively addresses real world challenges. The proposed tool is comprised of three sections: task framing, task assessment, and results interpretation. Unlike existing methodologies that focus on organization wide artificial intelligence readiness, the proposed tool centers on task specific readiness. This innovative approach provides practical guidance for finance professionals seeking to leverage artificial intelligence and helps organizations realize the potential of AI more effectively.