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One of the most eager industries to explore generative artificial intelligence in their operations is telecommunications (KPMG, 2023). The telecom sector in the Nordics experiences high rivalry among the big and established companies that all strive for the leading position. Customers have low switching costs and strong bargaining power, and therefore creating a competitive advantage and standing out to customers is crucial for telecom companies.

In my previous post, I talked about how in my traineeship in the telecommunications industry, ChatGPT was my savior. Apart from just creating solutions facilitated by artificial intelligence, our unit was also responsible for teaching other units about generative artificial intelligence and future business cases it could be used for.

Using generative AI in marketing was one of the main paths we were exploring in the company. Using OpenAI (ChatGPT and Dall-E) we investigated possible business cases with marketing executives and specialists. As we aimed for the “low-hanging fruits” and easy and efficient applications to use, we focused on using generative AI in marketing campaigns (text and visuals). The main reasons for using a generative AI solution were creating a better customer experience and standing out to the target customer groups more efficiently as well as saving costs as innovation and content creation processes within the company were ineffective and resource intensive. We had taught our brand image to Dall-E, and it succeeded in creating campaign visuals that fit the company vision perfectly. Accurate text generation was a harder task, the text generated was too formal, inconsiderate, or stammering. However, our team felt like teaching ChatGPT about previous successful marketing campaigns would have solved that problem.

One big challenge with using OpenAI we were facing was exposing our future campaigns and leveraging customer data, as those posed major data privacy risks as well as a possibility of revealing company secrets. Therefore, creating our own company GPT platform was one of the most important tasks for our intelligent automation team. Today, that solution is ready and waiting to be published to be used for marketing campaigns.

Has your workplace created its own generative AI-based tool or leveraged existing ones? Share your own experiences!

Reference list:

KPMG. (2023). Technology, media, and telecom lead the charge on generative AI. Available at: https://kpmg.com/kpmg-us/content/dam/kpmg/pdf/2023/technology-media-telecom-gen-ai-survey.pdf

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During the past summer, I worked at one of the leading telecommunications companies in the Nordics in a development unit focusing on robotic process automation (RPA) and intelligent automation (IA). One of my team’s main focuses was using generative artificial intelligence in our business solutions. Creating more accurate chatbots based on company and customer data, implementing speech-to-text (S2T) solutions, and leveraging large language models (LLM) in order to enhance customer experience while simultaneously decreasing manual customer contacts were at the heart of our work.

In this post, however, I will not talk about our solutions, but highlight how, with the aid of ChatGPT, I trained myself for my position. My unit was tech-centered, in fact, I was the only person with a business background which at times proved challenging. My main responsibilities included developing our S2T model (NLP task) and creating data visualizations and analyses for different business units as well as the executive management. One main obstacle in the way of succeeding in my role was having to learn a new software, Qlik, without having appropriate training. A crucial part of further developing existing and creating new meaningful analyses was knowing how to write QlikScript which is a similar language to SQL. However, I was only familiar with SQL basics.

I can confidently say that I would not have survived the summer without OpenAI, particularly ChatGPT Enterprise which turned out to become my savior. “Not perfect but overwhelmingly impressive” (McKinsey, 2022) is how I would describe my experience with ChatGPT. While it had trouble understanding the intricacies of our ETL-based data warehousing, it was particularly helpful in theorizing where my codes went wrong and helped me think of new ways of implementing them. In the end, ChatGPT provided better training for me than our company’s Qlik trainer from whom I just got “I do not know” replies. While a worrying remark from the company end, I am truly thankful to ChatGPT for giving me the tools to succeed in my work.

Have you ever felt like ChatGPT saved your life like it did mine this summer?

Reference list:

Chui, M., Roberts, R., & Yee, L. (2022). Generative AI is here: How tools like ChatGPT could change your business. McKinsey. Available at: https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business