ABSTRACT Date: [2023-05-31] Level: Bachelor thesis in Business Administration, 15 cr  Institution: School of Business, Society and Engineering, Mälardalen University  Authors: Mohammed Alkhateeb (97/01/04) Erik Duné (96/01/20) Ead Akriem (99/07/14) Title: The Impact of AI On Internal Communication Within An Organization - A Critical Examination of AI Adoption Supervisor: Lampou Konstantin Keywords: AI, Communication, Employee Engagement, Collaboration, Information,     Technology, AI Adoption Research question What are the advantages and disadvantages of adopting AI in internal communication within a company? Purpose: This study seeks to provide insights to help organizations make informed decisions about incorporating AI into their communication processes. Method: An exploratory approach was applied within its qualitative method to align the study with its purpose. The data collection is based on semi-structured interviews. The authors adopted an interpretive research paradigm and analysis through a thematic analysis. Conclusion: The authors conclude that AI can enhance intra and interdepartmental communication, and improve team interactions, efficiency, and flexibility. It offers advantages such as shorter and clearer communications, translations, and increased productivity. However, drawbacks include the lack of personalization and the potential for communication gaps, disengagement, and loss of the human touch. A balance between the benefits of AI and human interaction is crucial for effective communication. Organizations should develop strategies for adopting AI to consider employee readiness and monitor data quality to ensure accuracy. This will allow organizations to leverage AI's advantages while mitigating potential disadvantages.

Det svenska sjukvårdssystemet är konstant under hög press och situationen benämns ofta i media som en sjukvårdskris. Radiologin är en av de medicinska discipliner som drabbats av en kontinuerligt ökande arbetsbelastning och personalbrist. Detta sätter sjukvården i en situation att konstant tvingas väga effektivitet mot kvalitet. Trots höga förväntningar på att innovationer som Artificiell Intelligens (AI) ska kunna bistå behoven, används AI idag i en mycket begränsad utsträckning. Denna studie syftar till att utreda påverkande faktorer för införandet av AI inom radiologin. För att besvara arbetets forskningsfråga har HA Adoption-Decision Model, en modifierad version av det väletablerade Technology-Organization-Environment Framework (TOE), tillämpats. Ramverket innefattar tre kontexter; teknologisk, organisatorisk och extern kontext. Varje kontexts delaspekter, så kallade faktorer, följer under respektive kontext. Dessa tio faktorer utvärderades för att besvara studiens forskningsfråga om vilka faktorer som har störst påverkan på införande av AI inom radiologi. Genom att förena tidigare forskning med resultatet från sex intervjuer visade sig affärsvärde , strategisk lämplighet , ledningsstöd och reglering av datahantering ha störst påverkan. Avslutningsvis presenteras ett förslag om att introducera en elfte faktor, IT-mognad, till ramverket.

Background: Artificial intelligence has risen to prominence as a subject of study. The pace of artificial intelligence evolution in Bangladesh's IT industry is increasing by the day. In Bangladesh's IT Industry, artificial intelligence (AI) offers enormous potential. Despite the huge potential and advantages of AI implementation or adoption, Bangladesh's IT industry is still failing to move forward with its AI implementation. Objectives: The objective of this study is to identify the main factors that disturb the implementation of AI in the IT Company of Bangladesh. Novelty: In Bangladesh, most of the AI-related research conducted by focusing existing Scope of AI in the Bangladesh, as Bangladesh is in the initial phase of AI Adoption, this paper, therefore, sought to find out the factors that inhibit IT Industry to implement AI. Method: A quantitative method is used to find the results of the study. This paper reports on the results of an online survey questionnaire involving 51 IT professionals from a large IT company of Bangladesh about their perception regarding AI to find out the challenges. Results: Result indicates certain major challenges in AI implementation in Bangladesh’s IT Industry like Lack of AI skills and Incomplete knowledge or understanding regarding AI's capabilities and limitations, Internal culture Lack of Financial investment, , Data management, Lack of technological Infrastructure, Lack of top managerial support, Lack of legal and ethical framework, Non AI Approaches are sufficient by encapsulating them into three challenges context-organizational, environmental and technical barriers using IS theory TOE framework. Contributions: The study offers Insights to policymakers, executives and top-level managers to pay attention of adopting AI in IT Industry of Bangladesh by overcoming the challenges, besides further research can be conducted on how Bangladesh IT industry can overcome the AI implementation challenges. Conclusion: As Bangladesh is progressing with Technology, hence this is a high time to identify the major challenges that inhibit the AI Implantation in Bangladesh’s IT Industry. Policymakers, executives and top-level managers should find a proper solution policy to mitigate the challenges and adapt AI to boost up IT Industry of Bangladesh.

Background: Digitalized operations have become praxis for organizations of all shapes and sizes and while the digital tools keep developing, certain aspects make it difficult for certain organizations to keep up. One of the most modern, efficient, and sought-after digital tools is artificial intelligence (AI). With increased efficiency and decreased human errors, it has become the foundation for operations within organizations all over the world. One of these types of operations is the human resource management (HRM) process found in each organization. And while some industries have had a much easier task in adopting AI into their HRM function, others have had more difficulty. Purpose: While there are a few theories of what might affect the process of AI adoption, these are quite old and often known to managers. Yet, certain industries have a hard time adopting AI tools within the HRM function while other industries have fully implemented automated systems that have revolutionized the way they operate. The purpose of this study is to understand why and how AI adoption differs between these industries when it comes to similar operations such as the HRM function. Method:        The methods of this study were based on the grounded theory (GT) as a basis to analyze eight different organizations within the financial industry and telecom industry. Through semi-structured interviews, different aspects could be illustrated as crucial when it comes to the possibility to adopt AI within existing operations.  Conclusion: The results of this study show that the AI-maturity of the organization and industry alike play a crucial part in successfully adopting AI. But the institutional pressures and the available resources are equally important to understand to be able to successfully adopt AI. These two aspects form the outcome of AI adoption and the number of complex combinations that can be formed highlights why AI adoption differs between organizations and industries alike.

The hospitality industry confronts continuous challenges from external environments, such as the COVID pandemic, the proliferation of short-term rentals, and the disruptive innovations of Generative AI. For businesses, understanding these external conditions and adapting strategies accordingly is crucial yet challenging, especially considering environmental uncertainties. Therefore, this dissertation investigates the effectiveness of different strategies in navigating market, competitive, and technological uncertainties, through a big-data analytical approach. It incorporates three studies, each focusing on one specific strategy and its varying outcomes under environmental changes. These studies employ machine learning algorithms to quantify strategies and utilize econometric models to infer the causal relationships between strategies and their outcomes. The first study examines how standardization affects short-term rental unit survival across two market conditions: pre-COVID growth and during-COVID decline. The results indicate that the risks arising from standardization are heightened under market decline. In addition, the effectiveness of standardization varies with design attributes to which the strategy is applied. Standardizing functional design boosts unit survival in the growing market but leads to a higher failure rate during the decline. Aesthetic standardization, on the other hand, negatively impacts survival in both conditions, with a stronger effect in the declining market. The second study identifies the impacts of differentiation on unit performance in the short-term rental context in two competitive environments: local versus city-level. The findings suggest that the effectiveness of differentiation increases with competitive pressure. At the local level where firms face localized competition, differentiation enhances unit performance. Conversely, in city-level environments where direct competition diminishes, it yields negative outcomes. Moreover, competition intensity, as reflected by the number of competitors and the degree of market concentration, is found to amplify the benefits of and mitigate the drawbacks of differentiation. The third study explores if adopting Generative AI to hotel online review response can improve customer feedback, under varying technological settings. It finds that simulated AI adoption improves customer perceptions when Generative AI models operate at high temperatures, while models with low temperatures lead to negative outcomes. The findings further underscore the importance of task-technology fit, revealing that Generative AI's effectiveness varies with review valence. Specifically, high-temperature settings for positive reviews generate significant benefits, whereas low-temperature settings lead to adverse effects. Conversely, for negative reviews, AI adoption demonstrates more stable outcomes across temperature settings, indicating balanced benefits of both low and high temperatures. In short, this dissertation identifies that the effectiveness of standardization, differentiation, and AI adoption strategies is contingent on environmental conditions. It underscores the importance of strategic adaptation in navigating contemporary challenges. / Doctor of Philosophy / It is difficult to operate hospitality businesses because this industry faces constant challenges from ever-changing external conditions, including the COVID pandemic, the rise of short-term rental platforms, and the breakthroughs in technology like Generative AI. It is important but challenging for hotels and short-term rentals to understand these conditions and plan their operations accordingly. Thus, this dissertation aims to help business operators to understand how to deal with different external changes. It carries on a series of studies based on big data, using various analytical tools. This dissertation is composed of three studies. The first one finds that, generally, it is risker for short-term rental hosts to make one property similar to his/her other properties when the whole market declines. There are differences identified between functionality and aesthetics. Keeping the functionalities, such as WIFI and coffeemaker, consistent among multiple properties will make the property more likely to survive when the market grows but it increases the likelihood of failure when the market demand decreases. When deciding property aesthetics, like color or layout, it is risky to have properties similar to each other, no matter if the market demand grows or drops. The second study concludes that short-term rental hosts should decide the product design relative to their competitors from different scopes of areas. They are suggested to make their properties' interior design style different from their nearby competitors to gain high revenues, especially when there are more neighboring supplies managed by a large number of hosts. On the contrary, it is more beneficial to follow the general trend of properties located in the same city when deciding one property's aesthetic style. The third study guides hotels to apply Generative AI like ChatGPT to generate response to customer online reviews. It found that, to reply to online reviews with four- or five-star ratings, hotels should not use the default GPT model to increase the quality of customer communication. Instead, they need to use the professional OpenAI API and set the parameter called temperature to 2. However, when hotels reply to online reviews with lower star ratings, like one or two, there is no big difference between low and high temperatures (0 to 2). They can simply use the default model. In general, there are no one-size-for-all solutions to deal with external challenges. Hospitality operators are highly recommended to adjust their operations to fit different conditions.

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.

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.

The emergence of Generative AI (GenAI) foundation models presents transformative potential across industries, promising not only to increase productivity but also to pioneer new ways of working and introduce novel business models. Despite this, GenAI adoption levels have lagged behind early projections, and many firms report difficulties in finding appropriate applications. One such firm is Scandic Hotels, a Swedish hospitality company seeking to identify use cases for GenAI within the Scandic Data Platform (SDP), the firm’s analytics unit. The goals of this study were twofold: firstly, to identify GenAI use cases for the SDP based on their organizational needs, and secondly, to create a framework to guide organizations in harnessing the technology’s potential purposefully based on their specific organizational contexts. A conceptual framework was developed based on a synthesis of existing AI use case frameworks and the incorporation of GenAI characteristics to guide the investigation of the SDP. A qualitative case study approach was employed, achieving the first research goal through two primary activities: first, by assessing the organizational context through interviews and a questionnaire, and subsequently, by identifying concrete use cases designed to address organizational challenges based on the domain mapping through collaborative workshops. The investigation into the organizational context culminated in the formulation of a complex problem space with eleven logically interconnected domain problems stemming from two root causes: a high technological complexity of the data platform and a lack of organizational ownership concerning data. These problems lead the SDP to be occasionally overwhelmed with support requests, resulting in a range of time-consuming downstream issues that lock the team in reactive rather than proactive work. The use case identification process yielded eleven concrete use cases leveraging a range of GenAI technologies, including retrieval-augmented generation, fine-tuning, and prompt chaining. An evaluation based on the perceived business value of these use cases found that those directly addressing root problems or contributing to strategic imperatives received the highest value scores by members of the SDP. Our findings reinforce the problem-driven use case identification approach suggested by previous AI use case literature and offer nuances in the importance of basing use cases on a structured hierarchical problem space, allowing use cases to be designed to address root problems and break negative feedback loops for maximal business value. By iterating the literature-informed conceptual framework with these practical insights, a novel framework for GenAI use case formulation was developed, centered around matching root domain problems with GenAI-specific capabilities. This framework provides an overview of key components for the identification of use cases based on the organization’s unique context, contributing important starting points for managers wishing to engage in GenAI adoption and addressing the literature gap in GenAI-specific use case exploration frameworks. / Utvecklingen av grundmodeller inom generativ AI (GenAI) har demonstrerat potential att öka produktivitet, omdefiniera befintliga arbetsflöden och införa nyskapande affärsmodeller. Trots detta har införandegraden i näringslivet legat under tidigare prognosticerade nivåer, och många företag rapporterar svårigheter med att identifiera lämpliga tillämpningar. Ett exempel på ett sådant företag är den svenska hotellkedjan Scandic, som önskar identifiera interna användningsområden för GenAI inom analysenheten i företagets centrala organisation, Scandic Data Platform (SDP). Denna studie ämnade att först identifiera användningsfall för GenAI inom SDP baserat på enhetens specifika behov, och sedan utveckla ett ramverk för att vägleda organisationer i identifieringen av GenAI-användningsfall baserat på deras specifika organisatoriska kontext. Baserat på en syntes av befintlig litteratur inom AI-användningsfall och integreringen av karaktäristiska egenskaper för GenAI konstruerades ett konceptuellt ramverk för att orientera utredningen inom SDP. En kvalitativ fallstudieansats uppdelad i två huvudaktiviteter tillämpades för att uppnå det första forskningsmålet: först undersöktes den organisatoriska kontexten genom nio intervjuer samt en enkät, sedan identifierades konkreta användningsfall utformade för att behandla organisatoriska behov förankrade i kartläggningen av domänen genom kollaborativa workshoppar. Undersökningen av den organisatoriska kontexten kulminerade i formuleringen av en komplext problemrymd med elva logiskt sammanlänkade domänproblem härrörande från två grundorsaker: en hög teknologisk komplexitet hos dataplattformen och en brist på organisatoriskt ägarskap gällande data. Dessa problem leder till att SDP ibland överväldigas av supportförfrågningar, vilket resulterar i en rad tidskrävande efterföljande problem som låser in teamet i reaktivt snarare än proaktivt arbete. Identifiering av användningsfall resulterade i formuleringen av elva konkreta användningsfall som utnyttjar en rad GenAI-teknologier såsom retrieval-augmented generation, finjustering och promptkedjning. En utvärdering baserad på det uppskattade affärsvärdet av dessa visade att de användningsfall som direkt bemötte de två rotproblemen eller bidrog uppfyllandet av strategiska imperativ fick de högsta värdebetygen av SDP:s medlemmar. Våra resultat validerar framgången i det problemstyrda tillvägagångssättet för identifiering av användningsfall som föreslagits av tidigare litteratur, men nyanserar förfarandet genom att understryka vikten av att förankra användningsfall i en hierarkiskt strukturerad problemrymd—vilket gör att användningsfall kan utformas för att direkt bemöta rotproblem och bryta negativa återkopplingsslingor för att uppnå maximalt organisatoriskt värde. Genom att iterera det litteraturinformerade konceptuella ramverket med dessa praktiska insikter utvecklades vi ett nytt ramverk för identifieringen av GenAI-användningsfall, baserat på matchningen av rotproblemen inom domänen med GenAI-specifika kapaciteter. Detta ramverk ger en översikt över nyckelkomponenter för identifiering av användningsfall baserade på den organisatoriska kontexten. På så sätt bidrar studien med en utgångspunkt för företag som önskar engagera sig i införandet av GenAI och bemöter bristen på litteratur innehållandes GenAI-specifika ramverk för utforskning av användningsfall.

Businesses nowadays are increasingly adopting new technologies to obtain competitive advantages. Artificial Intelligence (AI) stands out as an advanced, novel technology that has potential benefits across industries. The fast-food industry is one such industry that is highly competitive, evolving, and requires advanced technologies to cater to modern customers who increasingly demand fast, digitized services. Increased customer engagement has also become a main driving force to adopt technologies since these consumers demand quick, personalized, digitized services. The fast-food industry, compared to other industries, produces food that is perishable, and quick, which demands proper handling before, during, and after preparation, for instant consumption. Services should be quick, fast, and accessible, where adopting advanced technologies has become a necessity for the industry players' survival. Larger organizations have successfully adopted AI and have harnessed a competitive advantage. Conversely, Smaller and Medium Enterprises (SMEs) have successfully adopted digital technologies, assuming it as AI. They have not yet translated to adopt AI, which could threaten their survival and competitiveness in a highly evolving, dynamic industry. On the other hand, AI is a novel technology that has much potential, yet many are unaware of where the technology is heading, specifically, SMEs have a limited understanding and exposure to this technology, demanding more research.  The main purpose of this study is to gain a comprehensive understanding of how fast-food SMEs in Sweden perceive AI, the reasons for non-adoption, and the reasons influencing the behavioral intention to utilize AI for customer engagement within the organization. The study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to analyze how performance expectancy, effort expectancy, social influence, and facilitating conditions influence individual SMEs' behavioral intentions towards AI adoption on customer engagement by studying it from an individual, organizational context. Through qualitative interviews with fast-food SME owners, IT managers, and marketing managers, the research explored a nuanced understanding of how AI is being perceived by SMEs, challenges, barriers, and factors influencing their adoption behavior.  The research findings indicated that AI technology itself is immature and the immediate business use case is not apparent for SMEs. It was also revealed that SMEs have a misconception between AI and digital technologies. Though there is enthusiasm and willingness to adopt AI within SMEs, significant challenges remain, such as a lack of understanding about AI, resource constraints, complexity, skills, and influences from competitors and stakeholders. The research identified factors specific to SMEs that contribute to extending the UTAUT framework, such as customized payment plans, establishing technology associations, and new business models suiting SMEs. It was further evidenced that customer engagement is not an impactful mediator that influences AI adoption within SMEs. It was concluded that though SMEs have the potential to improve performance, their adoption is limited due to the immaturity of AI and due to identified challenges.