I am very excited to write everything I learned. I am a person who normally finds the fun in telling my experience to people more than having the experience itself. First of all, let me introduce my profession. I am intending to minor in theatre because I like acting. I am currently the operations director in TFC (theatre and film club). I love music, and I play 2 instruments ( piano and electric guitar ). However, today I will be talking about my intended major, which is accounting. So, I haven’t interviewed any actual professionals, but I actually attended two data and AI analytics workshops by two managers at Bank Misr. They were so beneficial, and I would like to share the most important information I learned from them.

The first workshop, “AI and the Future of Finance,” led by Mr.Mohammed Saban, helped me to actually distinguish between analytical AI and generative AI. Analytical AI has only been used to total and analyze big collections of data to help in forecasting, classification, and risk analysis. Generative AI, on the other hand, can be used as a digital assistant, able to generate, synthesize, and act upon data. A good example cited was in credit analysis in banking. Generative AI can create the first draft of a credit memo for clients, speeding up a time-consuming process otherwise. Most critically, this doesn’t substitute credit analysts. Instead, it supports what they do by allowing them to focus on the expert judgment and client relationship aspects of their role, with the AI adding the upfront data-heavy foundation. The other key takeaway for me was the mindset. The workshop hammered home the point that AI has to be utilized and controlled, not feared. As technology continues to advance, the genuine value of human experts lies in their ability to gauge which activities and occupations will dominate, which will be displaced, and how to adapt as a result. This made me think again about the importance of continuously acquiring skills in critical thinking, ethical decision-making, and strategic direction skills that would never be replicable by machines. As the financial marketplace becomes ever more fluid, learning agility and flexibility are no less valuable than technical skill. The second speaker is Mr.Karim El-Farmawy. He gave us a very clear vision of how this technology is reshaping business today. To begin with, the presenters made AI tangible using examples. The idea of a “Chat with your Data” tool was especially convincing. It suggests that instead of needing to be a technical wizard, anybody can ask questions about their data in plain English or Arabic. This makes AI a helpful assistant. Seeing how this would be capable of working in an HR department answering employees’ questions or checking resumes, explained that AI’s only job is to make our work each day easier and faster.  Second, the workshop taught me that AI is no smarter than the information it’s given. The presenters spent a little time explaining the “Journey of Data.” First, before any of this high-tech AI will work, a business must gather its data from wherever it is, clean it up, and store it somewhere. The example of predicting machine downtime or population growth demonstrated that without this solid basis of data, AI cannot deliver useful results. That was the key lesson: the dull foundation is what makes it possible for the advanced AI tools. Finally, and most notably, the conversation of “Responsible AI” was essential. The speakers introduced that the use of AI safely and fairly is not an option. Things such as being unbiased, transparent, and accountable are essential. Especially when it comes to finance, where AI is being used to conduct credit scoring or fraud identification. Knowing that a company is taking these considerations into account instills trust. The fact that firms with high moral standards achieve a better return on their investment in AI shows that good business means good ethics. 

That was for the workshop part. For the second part, I interviewed my managerial accounting professor, Dr. Nariman Kandil. I asked her about AI at first, how resistant she was, and what the dangers and benefits of it are as a tool to help you teach students. I also asked her whether she lets her students use it or not. She said that at first they were resistant to AI, and there are still other professors in the university. But when Dr. Nariman used it, she found it helpful and thought why not students use it, but only as an assistant and guidance, because it hallucinates. And then when we debate, they tell us we’re right, and this is one of the drawbacks. Currently, Dr.Nariman is studying AI. To learn about language models and she uses many models. Dr.Nariman uses AI to help her develop the mini cases through the topic, and it was beneficial, but she also needs to make adjustments to the outputs according to her usage. Dr.Nariman does not believe that AI would replace their profession. However, it is an essential thing, and that is something that they can’t neglect. Whether they like it or not, it is something in our lives. She even thinks that not using AI nowadays is considered ignorance. But not to the extent of replacing human elements because we can’t fully depend on it, and it is also not ethical, as much as misleading. That’s why we need a human mind to make the decision. It also has risks that you can’t fully depend on them because they require the skills of human. For example, if you make surgeon robots, the next generation will not be able to do the skill at all, so that is the elimination of the human factor. How large language models work: they retrieve the data from webpages, and they forecast, but they need input first, so they lack accuracy, and if you don’t give it the right prompt, they produce irrelevant answers. So overall, you should be aware and understand how large language models work.