Three Cautionary Tales of ChatGPT Misapplication in Business You Should Know
Introduction
Natural language processing tools like ChatGPT, are rapidly becoming a vital part of the modern business landscape. Personally, I can attest to their value in distilling information, generating ideas and summarising complex concepts. However, like any technology, AI isn't a silver bullet that will solve all business problems. In fact, the misuse or misunderstanding of AI can lead to serious consequences. The effectiveness of AI is largely contingent on
how it's applied
the data it's trained on
the understanding of its limitations by its users
While it's tempting to get carried away with AI's potential and automate 90% of your job, let's step back for a moment to consider some cautionary tales of its misapplication in business. These stories serve to underline the necessity for businesses to approach AI implementation with care and a comprehensive understanding of both its strengths and weaknesses.
Case 1: Misuse of AI in the Legal Field
In one well-known instance, two lawyers used artificially generated cases from ChatGPT as real examples in court. This resulted in sanctions for the lawyers because they consciously ignored signs that the cases were not genuine. This episode highlights the ethical implications of AI use and the importance of verifying AI-generated information, especially in environments such as a court of law.
Case 2: AI in Program Development
At the other end of the spectrum, an AI tool was used to assist in the development of computer games. While the AI was successful in generating the code for simple games, it struggled with the creation of a complex 3D game. The developers had to spend a significant amount of time tweaking the AI-generated code, indicating that while AI can be an excellent starting point, it still requires a considerable degree of human supervision, especially in complex projects. Now if we apply this to in a business context, using AI to generate important code can have repercussions across whole data platforms if not QA'd appropriately.
Case 3: Data Privacy Concerns at Samsung
Finally, a more subtle issue with AI is that of data privacy. Samsung's engineers fed sensitive database source code and recorded meetings into ChatGPT. Because ChatGPT learns from user prompts, there was a significant risk that confidential information might leak. The case underscores the need for stringent data privacy measures and careful usage policies when integrating AI tools into a business.
Conclusion
While these stories highlight potential pitfalls, it's essential to remember that they are not indicative of AI being an inherently risky or unproductive tool. Rather, they emphasise the need for careful consideration, appropriate use, and ongoing scrutiny in the application of AI. As with any tool, its effectiveness is reliant on the hands using it. Therefore, as we continue to integrate AI more into our business processes, we must strive to educate ourselves about both its potential and its limitations.
In modern business, I suggest there be an internal discussion within teams about the use and misapplication of ChatGPT. Some companies have approached the problem with a widespread ban which is not the way to solve a solution! I don't doubt that most data professionals have found a few answers to problems with the tool and that's great however how does that then interact with the business environment is the question.
Thank you for reading. My name is Douglas, I'm a former data analyst and Founder of DR Analytics Recruitment, specialist data & analytics recruitment agency. We help businesses identify, screen and hire top data talent. Get in touch to learn more about what we do.
