5/5 (1)

During my two-month long internship at a financial advisory and consulting firm, generative AI tools were gaining a lot of traction. Hence the firm decided to buy a premium account for the same and include it in the library of resources of the firm based on recommendations of junior analysts. My coverage was in the industries of digital tech and financial services in the firm and one of our client’s needed assistances with a go to market strategy of the new product in their platform. The tool was used extensively by some analysts and interns while some resisted the tool due to its accuracy and inability to provided complex solutions.

It was efficient in providing summary of industry reports, image solutions as well. For instance, if certain information was needed while creating slides for a client for instance what is the “total market size of cloud-based computing services in America”, prompts were directly fed to get an idea however it could be used only as an aid and not as a source in these slides. It provided us with a good starting point and source of relevant information. Some analysts even used it to create a rough template slide to work and ideate upon. My discussions with seniors at the firm made me ask my seniors that the tasks which are trivial in nature and demand a lot of effort in terms of time and research, can it not be automated. The senior director of my team answered and provided a new perspective to the same age old question. He said that the value we create for our client is not the solution, but our ability and competence which we provide, AI can read up a ton of documents and provide summaries and relevant data from it, but an analyst only can select and decipher what is relevant to the client and the problem. The learnings from the strategy will be different for each analyst in the deal which is the value added to client and not the strategy itself. With improvements in its deep learning, image processing models, it can certainly become accurate and efficient.
Surprisingly weeks after my internship was over, the final solution was submitted to the client and chatGPT recommended the same go-to market strategy for the client when I prompted it with the similar problem statement. So, the question arises, can it replace or outdate consulting and similar jobs. The answer is complex, but the nuances of a human interaction, expertise and experience can’t be always substituted with AI. This example can be a case where the solutions match but companies certainly benefit from such tools in their arsenal as an aid and rather as the complete solution provider.

The name of the client has not been shared due to NDA.

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Generative AI tools have disrupted multiple industries and sports is no different. Use of such tools to generate real time commentary, analyze data. Sports journalism was one of the first movers in the industry to experiment with such tools to provided summaries of matches and create prompt stories. Interview responses are being processed with the help of natural language processing for editors to provide concise feedback for the same.

These tools help in even creating comprehensive published article using the stats and highlights of the game. From my personal experience I have used such tools in creating fantasy teams for competition and sports betting as well where you provide the prompt and let the generative tool predict the outcome of a game, create the best formation of a football team, or provide patterns and trends using the data. Sports betting websites and sprots streamers have also started using such these tools with advanced models and compared the outcome of their decision making with the team selected by AI. What surprised me was the accuracy with which it was able to make the best team based on the stats. For fantasy players in English Premier League, xG is one of the most important stats about any player and the tool consistently created the team using this as one of its inputs. However in the long run the team could not beast the points scored by my team as there were some of the human biases and punts involved in selecting few players which was based out of the knowledge of following the sport for a while and not just data and statistics.

While it continues to disrupt sports journalism, commentary, analytics, and data processing it certainly needs improvements to expand the scope and impact the industry further.

Standardization and lack of personal human touch inhibit the possibilities of sports commentary and journalism using gen AI. Peter Drury commenting on the comeback of Ronaldo in Old Trafford, or the miracle by AS Roma against FC Barcelona hit the right chords with sports fans across the world due to the use of language and knowledge of the game which came from decades of watching it. Such tools lack such tone which can be developed using advance NLP models, video to text processing using deep learning. Another spectrum where it can be used to impact this industry is fan engagement. Stadium guided tours, sports trivia quizzes, games and analysis can be efficiently done and provided to fans around the world. Teams can create Chatbots to give walkthroughs and tours to fans and engage them. Multi-lingual capabilities and imitating famous commentators and editors like Martin Tyler, Alan Smith, Peter Drury, Harsha Bhogle can be another experience for fans while it helps them with its data analysis and processing capabilities.