With ChatGPT already becoming one of the most widely-popular technology innovations in history, many business leaders want to leverage its promise to optimize their operations. Its advanced language processing and generative AI capabilities provide hope to reengineer a wide array of business processes, especially in the areas of marketing and customer service. However, ChatGPT’s simplified interface belies the real work necessary to successfully integrate it as part of any company’s business application assets.
In fact, the embarrassing and inaccurate responses ChatGPT occasionally provides illustrates that this technology – while potentially transformational – very much remains a work in progress. Significant training and filtering need to happen to prevent ChatGPT from recommending a competitor’s products or generating offensive output. Simply stated, it’s not a “plug and play” application.
So let’s take a closer look at four critical business concerns you need to take into account when thinking about adopting ChatGPT or any other generative AI tool at your organization. After all, while the benefits provided by advanced AI remain promising, they also come with a significant risk. As such, addressing these critical concerns needs to be part of any analysis before beginning your first pilot project.
As hinted at above, what if an AI-powered chatbot serving as a customer service representative for a business shares proprietary information and company secrets with the public. Someone (or even a virtual agent) from a competitor with skills as an AI prompt engineer simply queries the bot to get access to this information. Ultimately, this scenario serves as a new example of the data privacy risk facing many modern businesses.
In fact, in addition to protecting corporate data, there needs to be sufficient data siloing in place to prevent private customer data from being used in an AI bot’s language model. At NineTwoThree, we continue to hone our best practices which combine siloing with strong prompt engineering to ensure any ChatGPT integration stays on messaging, preventing these types of newfound data privacy risks. This protection of intellectual property and customer data remains a critical piece of any project involving generative AI and large language models.
Another worry for businesses involves a chatbot recommending the product line of the competition as opposed to championing your own business’s products and services. It offers the potential to embarrass your own company while providing competitors with a chance to have some fun on social media at your expense. A simple example involves a Coca-Cola chatbot recommending the latest diet version of Pepsi as opposed to Coke Zero Sugar. No business wants to deal with the issues surrounding that kind of misstep.
Once again, it illustrates the importance of properly training any machine learning model used for a chatbot as opposed to implementing it without this extra level of training. As highlighted above, using data silos in combination with experienced prompt engineers remains the best way to ensure an advanced chatbot behaves properly. As mentioned earlier, plug and play does not apply in this scenario.
Companies with significant exposure to regulatory issues – especially those in the financial and insurance sectors – need to tread lightly before considering integrating any generative AI tool with their technical assets. Any organization in the healthcare sector also remains at risk in this area, especially when HIPAA is taken into account. The legal ramifications are numerous and require a strong level of moderation when training models used in a chatbot.
Notably, the European Union continues to debate whether ChatGPT itself violates the EU’s AI Act, which identifies risk levels for AI-based tools depending on their individual use-case. In fact, it appears each EU country has their own take on the subject, with Italy currently banning ChatGPT.
As opposed to security and privacy issues, some business might simply struggle with implementing ChatGPT or a similar tool within their existing application suite. In some cases, it might involve the inherent complexity of those older applications. At the same time, some businesses lack the technical talent with the necessary machine learning model training experience. This issue becomes exacerbated when considering the rigorous model training necessary to protect against the first two business concerns noted in this article.
In this scenario, organizations need to consider partnering with digital agencies with the requisite skills in this emerging technology. They need to find talent that understands modern enterprise app development – everything from client-server to service-oriented architecture – as well as deep experience training ML models. Finding a team well-versed in iterative software development methodologies, as opposed to individual contractors, also helps.
If your business wants to explore integrating ChatGPT, connect with the experts at NineTwoThree. We combine state of the art technical chops with keen business acumen, making us the right partner for your team. Schedule some time with us to discuss your generative AI integration ideas.