The world is abuzz about ChatGPT. ChatGPT will most certainly contribute to lowering the barrier of entry for anyone who is not a professional in fields such as coding and content writing. At the same time, it will also raise the ceiling for output for those who are already skilled in those functions. ChatGPT, like other GPT models, uses a combination of machine learning techniques, including unsupervised learning, to generate coherent responses to prompts that are within the context of the conversation.
Since ChatGPT has the ability to remember the history of the conversations through contextual carryover, it offers users a fluid conversational experience, allowing for follow-ups to correct the responses where necessary. Furthermore, with ChatGPT, the language generation is controlled to a large extent.
Having said that, the key question is: Is ChatGPT mature enough for usage to solve real business problems? As it stands today, ChatGPT has further mainstreamed Generative AI by making it more accessible. However, for enterprises to connect with their end users and drive business impact, a more comprehensive end-to-end conversational AI solution is required. It is important to note that the success of conversational AI solutions depends on their ability to deliver a high-quality user experience. This includes being able to understand and respond accurately to the user’s input or perform a relevant action, and that too in a natural, human-like manner.
Businesses need to have control over the conversational flow. There must be control and understanding of the various conversational flows, intents, and utterances within each use case, which varies by business and industry. Conversational AI solutions also need to support integration with backend systems such as payment gateways, CRMs, and Contact Centre Platforms to pull and push relevant information in order to provide high levels of automation and subsequently greater ROI. On the other hand, ChatGPT can as of now only fetch information and respond to the user’s prompts based on the knowledge fed to it during its training, but it lacks the ability to perform a relevant action or integrate with backend systems.
For it to perform an action like fetching policy details or booking a flight, it needs access to third-party systems.Not only that, each business is highly distinct in nature; they have their own domain knowledge and sources that are very specific to their products and services and to the industry they operate in. In order for them to leverage ChatGPT, they would need to access the API to fine-tune ChatGPT with their own data and create their own variants of ChatGPT.
Essentially, there is still ground to cover in order for ChatGPT to be used effectively and accurately for enterprise use cases to solve real business problems at scale.