Can Commercially available AI services reduce costs within the media analysis industry? : A case study

Sammanfattning: This bachelor thesis examines the potential advantages of implementing artificial intelligence (AI) services in the context of customer support. With the rise of chatbots utilizing Natural Language Processing (NLP), AI has recently become a widely debated topic. The authors aim to investigate the comparative costs of using AI-powered chatbots versus human-powered alternatives for customer support, focusing on analyzing the financial impacts of chatbot incorporation and the quality of the responses. This research seeks to offer insights to businesses considering using chatbots as a tool for customer support. The objective is to assist these businesses in making informed decisions concerning AI adoption and associated costs. The bachelor’s thesis is a case study that employs a qualitative research method using an analytical- and abductive approach. The thesis addresses the question: ’Can commercially available AI services reduce costs within the media analysis industry?’ The results demonstrate that cost savings can be achieved by reducing time-consuming tasks from manual labour. However, using AI services is not a simple solution and expected positive results are not always guaranteed. Numerous issues need be highlighted if the intend is to have customers using and having direct access to the AI chatbot. Incorrect responses from a chatbot can create problems for customers and companies. An important question is how to handle the incorrect responses sent to the customer. Who bears the ultimate responsibility when wrong actions are recommended by automation and carried out by a customer? On the other hand, these issues become less relevant if the chatbot is used by the Customer Support team as a complement to reduce time spent per task. The findings of the thesis indicate that while the prototype can complement the Customer Support team, it is insufficient to handle all customer support responsibilities due to its 59.85% accuracy score and limited capability to effectively address follow-up inquiries. In conclusion, while the findings support the potential cost savings achievable through automation with AI, it is crucial to further refine and enhance the capabilities of the prototype to better meet the requirements of comprehensive customer support.