Recent public artificial intelligence (AI) advancements, particularly ChatGPT, are predicted to transform whole industries, work roles and organisational structures, leading to some jobs becoming obsolete while also creating new opportunities. This qualitative research explores the effects of ChatGPT on work roles and organisations in the information technology (IT) industry, more specifically, the effects on skills, competence, and organisational processes such as the automation of routine and non-routine tasks. The aim is to fill the gap in how ChatGPT affects the IT industry and to provide recommendations for policy makers, companies, and workers to address these challenges. Two research questions were formulated: “How does the increasing adoption of ChatGPT in internal work processes of businesses in the IT industry change work roles” and “impact the organisation and what are the potential implications for changes in work roles due to ChatGPT?”. To explore and answer these questions two data collection methods were used such as semi-structured interviews and qualitative questionnaires, with a combined sample size of 14 participants. The data was analysed using thematic as well as content analysis and the theoretical framework. The findings suggest that adopting ChatGPT is indeed transforming work roles and organisations by automating routine and non-routine tasks, leading to efficiency and cost savings. While some roles and skills change, others become entirely obsolete. The impact varies based on organisational factors, the nature of work and adaptability to new technologies, leading to the emergence of new opportunities in AI management and big data. Smaller companies in particular benefit from implementing ChatGPT, allowing focus on other tasks such as for example strategic development. Organisational challenges include training employees and adapting to new technology as well as concerns for job loss.

yes / This paper aims to demystify the hype and attention on chatbots and its association with conversational artificial intelligence. Both are slowly emerging as a real presence in our lives from the impressive technological developments in machine learning, deep learning and natural language understanding solutions. However, what is under the hood, and how far and to what extent can chatbots/conversational artificial intelligence solutions work – is our question. Natural language is the most easily understood knowledge representation for people, but certainly not the best for computers because of its inherent ambiguous, complex and dynamic nature. We will critique the knowledge representation of heavy statistical chatbot solutions against linguistics alternatives. In order to react intelligently to the user, natural language solutions must critically consider other factors such as context, memory, intelligent understanding, previous experience, and personalized knowledge of the user. We will delve into the spectrum of conversational interfaces and focus on a strong artificial intelligence concept. This is explored via a text based conversational software agents with a deep strategic role to hold a conversation and enable the mechanisms need to plan, and to decide what to do next, and manage the dialogue to achieve a goal. To demonstrate this, a deep linguistically aware and knowledge aware text based conversational agent (LING-CSA) presents a proof-of-concept of a non-statistical conversational AI solution.

This paper evaluates and analyzes three conversational AI-platforms; Dialogflow (Google), Watson Assistant (IBM) and Teneo (Artificial Solutions) on how they perform based on a set of criteria; pricing model, ease-of-use, efficiency, experience working in the software and what results to expect from each platform. The main focus was to investigate the platforms in order to acquire an understanding of which platform would best be suited for enterprises. The platforms were compared by performing a variety of tasks aiming to answer these questions. The technical research was combined with an analysis of each company’s pricing model and strategy to get an understanding of how they target their products on the market. This study concludes that different softwares may be suitable for different settings depending on the size of an enterprise and the demand for complex solutions. Overall, Teneo outperformed its competitors in these tests and seems to be the most scalable solution with the ability to create both simple and complicated solutions. It was more demanding to get started in comparison with the other platforms, but became more efficient as time progressed. Some findings include that Dialogflow and Watson Assistant lacked capabilities when faced with  complex and complicated tasks. From a pricing strategy point of view, the companies are similar in their approach but Artificial Solutions and IBM has more flexible methods while Google has a fixed pricing strategy. Combining the pricing strategy and technical analysis this implicates that Teneo would be a better choice for larger enterprises while Watson Assistant and Dialogflow may be more suitable for smaller ones. / Det här arbetet evaluerar och analyserar tre konversationella AI-plattformar; Dialogflow (Google), Watson Assistant (IBM) och Teneo (Artificial Solutions) utifrån hur de presterar baserat på ett antal  kriterier; prismodell, enkel användning, effektivitet, upplevelse att arbeta i programvaran och vilka resultat man förväntar sig från varje plattform. Huvudsakligt fokus var att undersöka plattformarna för att få en uppfattning om vilken plattform som skulle passa bäst för företag. Plattformarna jämfördes genom att utföra en mängd olika uppgifter som syftade till att besvara dessa frågor. Den tekniska forskningen kombinerades med en analys av varje företags prismodell och prisstrategi för att få en uppfattning av hur de riktar sina produkter på marknaden. Denna studie drar slutsatsen att olika programvaror kan vara lämpliga för olika sammanhang beroende på ett företags storlek och dess efterfrågan på komplexa lösningar. Sammantaget överträffade Teneo sina konkurrenter i dessa tester och verkar vara den mest skalbara lösningen med förmågan att skapa både enkla och komplicerade lösningar. Det var mer krävande att komma igång i jämförelse med de andra plattformarna, men det blev mer effektivt med tiden. Vissa fynd inkluderar att Dialogflow och Watson Assistant saknade kapacitet när de mötte komplexa och komplicerade uppgifter. Från en prissättningsstrategisk synvinkel är företagen liknande i sin metod men Artificial Solutions och IBM har mer flexibla metoder medan Google har en fast prissättningstrategi. Genom att kombinera prisstrategi och teknisk analys innebär detta att Teneo skulle vara ett bättre val för större företag medan Watson Assistant och Dialogflow kan vara mer lämpade för mindre.

No description available.