AI and GPT for Management Scholars and Practitioners: Guidelines and Implications

The use of artificial intelligence (AI) is quite usual in our life by now and increasing day by day (Latinovic, & Chatterjee, 2022). No doubt it has offered a large number of benefits but has also made a significant progress on raising more questions to be solved (Gevaert et al., 2021). But, to answer the related questions and move further in the right direction, it is important to understand some basic aspects related to AI and GPT. I am pleased to simplify the inference for those who would like to understand this area for further usage. One of the abilities of AI is natural language processing (NLP) and it has many use cases in management. It is a subfield of AI that deals with the interaction between computers and human language. It involves using computational techniques to enable computers to understand, analyse and generate human language. NLP is used in a wide range of applications such as sentiment analysis in investment, chatbots in customer relationship management, decision support system in retail and operations, legal, architecture, transportation and many more (Chowdhary, 2020). It involves various techniques such as statistical modelling, machine learning, and deep learning to build algorithms that can process, analyse, and generate natural language. Large language models (LLMs) are a type of NLP technology that has been gaining a lot of attention in recent years. LLMs are AI models that have been trained on massive amounts of text data, enabling them to generate human-like language and perform a wide range of language processing tasks (Schwenk, 2007). LLMs are usually trained on massive datasets, and learn patterns and structures of language without being explicitly programmed. This training process allows LLMs to generate coherent and contextually appropriate text in response to prompts. Some examples of LLM are ChatGPT by Open AI and Bard by Google. After the hype cycle of metaverse, recently, ChatGPT has captured attention in multiple forums, townhalls and debate groups. I see this as a progression from non-fungible tokens (NFTs), which is an extension of blockchain. The reason of ChatGPT hype may be that it is a tool from Open AI which is being offered in freemium model. However, my finance colleagues may relate it to the stock market prices recently experienced by Goggle and Microsoft. From a user’s perspective, you can access it using web application. As the name suggest ChatGPT is a Generalized Pre-trained Transformer (GPT), and it is pre-trained on the existing data set. The tool is trained on a dataset which is at least two years old. Hence, latency is one of the shortcoming of the GPTs. Second, a GPT is as good as the data it is trained upon (Shen et al., 2023). We should be cautious at the time of using GPT and even ChatGPT also suggests doing so. There have been LLM tools in use since last fifteen years but the adoption of ChatGPT has been phenomenal. May be it can be interesting for my marketing area colleagues to think upon how users started comparing or combining cloud, mobile and internet systems. ChatGPT has the potential to disrupt many industries such as services industries, healthcare, finance, education and Information technology. Use of chatbot based automation is not a distant dream now. More over ChatGPT has waged a new war in the area of information search.