AI-implementeringar inom organisationer har på den senare tiden växt explosionsartat där chatbotar baserade på generativ AI beräknas bli allt mer populärt. Tidigare forskning har framförallt bedrivit kvantitativa studier som dessutom fokuserat på externa chatbotar, där syftet varit att kommunicera med intressenter utanför organisationen. Den här studien fokuserar därför på interna chatbotar som ämnar stötta de anställda i deras vardagliga arbetsuppgifter, med fokus på hur samspelet ser ut mellan medarbetarna och AI på arbetsplatsen. Med hjälp av att använda en kvalitativ forskningsmetod, i form av tio semistrukturerade intervjuer på managementkonsultföretaget Centigo, var studiens syfte att bidra till en djupare förståelse för hur medarbetare på den utvalda organisationen upplever och skapar mening i förhållande till den AI-baserade chatboten på arbetsplatsen, samt vilken påverkan det har på deras självbild och den kommunikation som de för inom verksamheten. Genom att analysera empirin med en socialkonstruktivistisk utgångspunkt och meningsskapande som huvudsakligt analysverktyg, visade resultatet att chatboten både påverkar medarbetarnas självbild och de kommunikationsprocesser som medarbetarna för inom organisationen. Framförallt i form av att den digitala identiteten riskerar att bli alltmer generisk, vilket gör att de anställdas personlighet och karisma får en större betydelse i det fysiska rummet. Enklare problem tas dessutom numera med chatboten istället för med en kollega, vilket öppnar upp för ett nytt sätt att kommunicera på arbetsplatsen. Sammantaget var det tydligt att de mänskliga relationerna fyller en viktig funktion på arbetsplatsen, där det fanns skepticism mot att AI någonsin kommer kunna ersätta det. / AI implementations within organizations have recently grown exponentially, with chatbots based on generative AI expected to become increasingly popular. Previous research has primarily conducted quantitative studies, focusing on external chatbots aimed at communicating with external stakeholders. This study therefore focuses on internal chatbots intended to support employees in their daily work tasks, with a focus on the interaction between employees and AI in the workplace. By employing a qualitative research method through ten semi-structured interviews at the management consulting firm Centigo, the aim of the study was to contribute to a deeper understanding of how employees in the selected organization perceive and create meaning in relation to the AI-based chatbot in the workplace, and its impact on their self-image and the communication they engage in within the organization. By analyzing the empirical data from a social constructivist perspective and using sensemaking as the main analytical tool, the results showed that the chatbot affects both employees' self-image and the communication processes within the organization. Particularly, the digital identity risks becoming more generic, emphasizing the importance of employees' personality and charisma in physical interactions. Also, simpler issues are now handled with the chatbot rather than with a colleague, which introduces a new way of communicating in the workplace. Overall, it was evident that human relationships serve a vital function in the workplace, with skepticism towards AI ever being able to replace them.

Task-oriented dialogue systems have increasingly become integral to our daily lives. However, collecting dialogue data is notably expensive due to the necessity of human interaction. These systems are used in various applications, such as customer service chatbots, virtual assistants, and automated scheduling tools. Given the critical role of these systems, it is essential to develop methods that can leverage limited resources efficiently, especially in data-driven models like neural networks, which have demonstrated superior performance and widespread adoption. This dissertation proposes systematic approaches to address the limited-data problem in both modeling and data aspects, aiming to enhance the effectiveness and efficiency of task-oriented dialogue systems even when data is scarce. This dissertation is divided into three main parts. The first part introduces three modeling techniques to tackle limited-data challenges. As the base dialogue model evolves from traditional recurrent neural networks to advanced large language models, we explore meta-learning methods, meta-in-context learning, and pre-training sequentially. Besides modeling considerations, the second part of our discussion emphasizes evaluation benchmarks. We start by discussing our work on correcting MultiWOZ, one of the most popular task-oriented dialogue datasets, which enhances training and provides more accurate evaluations. We also investigate biases within this dataset and propose methods to mitigate them. Additionally, we aim to improve the dataset by extending it to a multilingual dataset, facilitating the development of task-oriented dialogue systems for a global audience. The last part examines how to adapt our methods to real-world applications. We address the issue of database-search-result ambiguity in Meta’s virtual assistants by constructing disambiguation dialogue turns in the training data. Furthermore, we aim to enhance Walmart’s shopping companion by synthesizing high-quality knowledge-based question-answer pairs and constructing dialogue data from the bottom up. Throughout this dissertation, the consistent focus is on developing effective approaches to building task-oriented dialogue systems with limited resources. Our strategies include leveraging limited data more efficiently, utilizing data from other domains, improving data quality, and distilling knowledge from pre-trained models. We hope our approach will contribute to the field of dialogue systems and natural language processing, particularly in building applications involving real-world limited data and minimizing the need for manual data construction efforts. By addressing these challenges, this dissertation aims to lay the groundwork for creating more robust, efficient, and scalable task-oriented dialogue systems that better serve diverse user needs across various industrial applications.

Despite being explored and constantly improved through the years, onboarding of new hires in corporate organizations has remained a challenge. Many of the issues can be linked to a lack of communication between the organization and the new employee, as well as the common nature of these environments where information is spread across job titles and sources. This thesis discusses the feasibility of implementing a basic chatbot that will allow new hires to ask questions and request varied information at all times, using an interface such as a messaging app. This research explores the way chatbots should be designed in order to be effective, reliable and enjoyable from a user experience perspective. The chatbot was developed using the Chatfuel platform and tested by new employees at a corporate environment. The users were requested to explore the chatbot freely and then asked to answer a survey. The interactions were also recorded and analyzed from in both qualitative and quantitative ways (chat logs and analytics). The study proves that an onboarding chatbot is a useful tool for new hires and can be used as a communication facilitator between the organization and the new hires during the first weeks of employment, and also after that, serving as an information source and a broadcasting method. The chatbot gives the new hires an accessible source of information that helps on the process of getting to speed, and enables a positive experience that increases familiarity in the new workplace.

The digital age has brought many new technologies that are disrupting the way that companies interact with customers. Automated technologies are one aspect of technological development in recent days, and it has changed how regular business operations are performed. Chatbots, for instance, has changed the way that customers interact with companies. Facilitating interaction and being available around the clock to serve customers who have questions. This study investigated how automated technologies are used in CRM-activities of companies and how the implementation of such technologies can lead to customer engagement. CRM, Customer Relationship Management, is a core business strategy that aims to strengthen customer relationships through co-creating value with the use of supporting technologies. Customer engagement is a measurement of a customer's involvement with a firm over time, with the end goal of customers becoming “fans” that advocate and generate word-of-mouth for the firm. This study investigates how companies utilize automated technologies in CRM-activities and if these technologies can help lead to customer engagement. The study aims to answer the following research question: How are automated technologies, more specifically chatbots, used in existing CRM strategies to further create customer engagement? The study conducted investigates these technologies within five companies that utilize chatbot-technologies from chatbot-providers. The chatbot-providers are also part of the study to get a more comprehensive view of the functionalities and purpose of these technologies. The contrasting view of companies and chatbot-providers is used to answer the sub-purposes of the thesis regarding how companies implement automated technologies. A qualitative study with interviews was conducted, along with secondary data from written documentary sources. The data were analyzed in a theoretical thematic analysis in which themes were developed from the theoretical framework. The findings of the study show that despite the potential of automated technologies, the most prominent use is for customer service operations. The potential for automated technologies in CRM is greater than what was found in the interviewed companies. The findings also demonstrate that automated technologies facilitate connection, interaction, and increases customer satisfaction, which can lead to customer engagement. The findings also show that there is a discrepancy in the views of companies who use chatbots in their business processes and the chatbot-providers that help implement them.

En el presente trabajo de investigación se pudo conocer acerca de dos acciones de omnicanalidad brindadas por las tiendas minoristas de ropa; tales como, retargeting y chatbots, los cuales son utilizados durante la etapa de búsqueda de los clientes. Asimismo, se logró recopilar información sobre la estrategia omnicanal, los comportamientos web y la intención de compra. Para ello, se plantea ejecutar una encuesta a clientes de tiendas retailers de ropa que hayan realizado compras de manera virtual o física y que residan en Lima Metropolitana. / In this research, you will be able to know about two different actions of omnichannel strategy provided by fashion retail stores such as chatbots and retargeting used during the customer search stage. Likewise, it was possible to collect information on the omnichannel strategy, web behaviors and purchase intention. For this, it is proposed to carry out a survey of customers of clothing retail stores who have made purchases virtually or physically and who reside in Metropolitan Lima. / Trabajo de investigación

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ABSTRACT  Title: Customers perception of Artificial Intelligence as Chatbots in CRM  Subject: Bachelor thesis in business administration, 15 hp  Authors: Hanna Trägårdh & Elsa Kuylenstierna  Supervisor: Klaus Solberg Söilen  Examiner: Ulf Aagerup  Keywords: Artificial intelligence, Chatbots, Customer Relationship Management, Generation Z, Generation Y  Purpose: The purpose of this study is to determine the perception Swedish customers from Gen Z and Gen Y have of companies using chatbots in customer relationship management. Furthermore the study will analyze if the different generational groups perceive chatbots in different ways. The study also focuses on finding out what role chatbots play in terms of CRM.  Methodology: In this study a deductive research approach was used where the empirical study was collected through a survey. The survey had 212 respondents, 100 of them were between 15 and 25 years old and 112 were between 26 and 44 years old.  Conclusion: Gen Z and Gen Y have similar opinions about how they perceive chatbots. Both Gen Z and Gen Y prefer communicating with a human since chatbots are not living up to their expectations. However, they are optimistic that chatbots have the potential to improve customer services to the better. Companies need to make sure that chatbots create value for customers and not only for companies themselves. Chatbots need to keep developing and make progress to be beneficial for customers to use.  Implications: This study can contribute with new perspectives about chatbots and customer relationships that can be a big advantage for companies when implementing chatbots for their business. The study brings attention to the unexplored issue of Gen Z and Gen Y’s perception in this matter. This study can also be helpful for further research within the subject area.

Actualmente vivimos en un mundo lleno de cambios tecnológicos y muchos sectores del mercado se han vuelto más digitales. De esta manera, los chatbots, a través de la inteligencia artificial, se encuentran posicionándose en todos los sectores del mercado para diversos usos. Es por ello que en esta investigación se analiza las acciones del uso del chatbot en el sector bancario de Lima Metropolitana con respecto a la satisfacción del cliente final. / Nowadays we live in a world plenty of technological changes and many market sectors have become more digital. In this context, chatbots, by the use of artificial intelligence, are positioning themselves in all sectors of the market for various uses. For this reason, this research analyzes the actions of chatbot use in the banking sector of Metropolitan Lima with the aim of satisfying the final client. / Trabajo de investigación

Användning av AI har ökat i skolans värld, och med ökningen av användandet följer viss osäkerhet och kritik kring ämnet. En del av problematiken ligger i korrektheten och pålitligheten hos AI:n, medan annan problematik ligger i hur tillgången till AI ser ut mellan olika skolor, då detta kan skapa orättvisor. Ytterligare en aspekt där problematik kan uppstå är kring elevers integritet och säkerhet.  För att belysa ämnet AI i skolor har denna studie genomförts i syfte att identifiera och beskriva de för- och nackdelar som AI användningen kan ha på skolväsendet utifrån ett gymnasienivå perspektiv. Samt besvara frågan om användningen av ChatGPT kan vara ett stöd eller ett hinder för undervisningen, utifrån gymnasielärares perspektiv. Datainsamling har skett genom teoretisk litteraturstudie samt semistrukturerade intervjuer av fem gymnasielärare. Analysmetoden som har använts är en tematisk innehållsanalys.    Resultatet av insamlat material klargör för- och nackdelar gällande användandet av AI på gymnasiet. Några av fördelarna som synliggjordes var att AI skapar möjligheten till individanpassad utbildning, kan komplettera befintligt studiematerial och ge en sammanfattande beskrivning av utvalt material. Några av nackdelarna som uppmärksammades var att materialet som genereras av ChatGPT eller andra AI-verktyg saknar ansvarig utgivare och kan skapa möjligheten för eleverna att fuska.   Studiens slutsatser är att ChatGPT är ett bra utbildningsstöd om verktyget används på ett etiskt sätt. Skolverket med andra akademiska intuitioner måste ta fram riktlinjer och policyer som både elever och lärare kan ta del av och följa. Slutligen bör utbildning erbjudas till såväl lärare som elever på hur ChatGPT kan användas som ett studiestöd i stället för ett hinder.

En civilingenjör som utbildas idag förväntas ofta att lära sig programmering och för att klara en civilingenjörsutbildning krävs att man ska kunna programmera på många olika nivåer. I samband med den växande populariteten av chatbotar inom utbildning och bland företag så finns det skäl att undersöka hur en chatbot hade kunnat assistera studenter som läser programmeringskurser. Det stora antalet studenter och den begränsade tillgängligheten av lärarassistenter bidrar till lägre nivåer av personlig uppmärksamhet och support inom programmeringskurser. Studien syftar därför till att förstå om en meny-baserad chatbot kan vara ett effektivt komplement till laborationstillfällen. Först utvecklades en meny-baserad chatbot som webbapplikation med hjälp av React, Javascript, HTML5 och CSS3. Den meny-baserade chatbotens prestanda utvärderades sedan under ett simulerat laborationstillfälle där även deltagarnas (N = 6) upplevelser med chatboten samlades in. Resultatet visade att den meny-baserade chatboten användes totalt fyra gånger per student i snitt och kunde hjälpa studenterna att lösa deras problem i 62.5% av fallen, samt att den var generellt uppskattad av studenterna. Genom att möjliggöra hjälp för flera studenter samtidigt och att bättre förbereda studenterna inför deras möten med lärarassistenter uppvisade chatboten potential för att öka laborationstillfällets effektivitet, men att ytterligare undersökningar krävs för att säkerställa detta påstående. Vidare upptäcktes flera potentiella implementationsmöjligheter av meny-baserade chatbotar i denna miljö. Exempelvis kan olika visualiseringar av användardata som chatboten samlar in sammanställas för att få en inblick i studenternas lärande och vilka aspekter av undervisningen som är mer resurskrävande. Potentialen för användandet av meny-baserade chatbotar för programmering inom högre utbildning är därför tydlig. / An engineer educated today is often expected to learn programming, and to successfully complete an engineering education, one needs to be able to code at various levels. Given the growing popularity of chatbots within education and business contexts, it is worth investigating how a chatbot could assist students taking programming courses. Furthermore, the imbalance between the large number of students and the limited availability of teacher assistants leads to a low level of personalized attention and support for students in programming courses. The study therefore aims to understand if a menu-based chatbot can be an effective complement to lab sessions. Firstly, a menu-based chatbot was developed as a web application using React, Javascript, HTML5, and CSS3. The performance of the menu-based chatbot was then evaluated during a simulated lab session where data was collected as well as participants' (N = 6) experiences with the chatbot. The results showed that, on average, the menu-based chatbot was used four times per student and successfully helped students solve their problems in 62.5% of cases. The chatbot was also generally appreciated by the students. By enabling assistance for multiple students simultaneously and better preparing them for interactions with teaching assistants, the chatbot demonstrated potential for increasing the effectiveness of lab sessions. However, further investigations are required to substantiate this claim. Additionally, several potential implementation possibilities for menu-based chatbots in this environment were discovered. For example, different ways of visualizing user data collected by the chatbot could be compiled to gain insights into students' learning and identify more resource-intensive aspects of teaching. The potential for using menu-based chatbots in programming within higher education is therefore evident.