It’s time to reevaluate what is possible and what is not; what should be done and what should not be done now that we are in the middle of — or ideally closer to the conclusion of — a general hype that was brought on by Open AI’s ChatGPT.
It’s time to consider commercial use cases with real economic value and outcomes that go beyond marketing hype. We thought of putting together a few ChatGPT use cases for businesses and also look at how LLMs are taking over the world.
One only needs to think back as far as Google Bard to see how important it is to be aware of the advantages and disadvantages of your platform.
In its release demo, Google’s AI chatbot provided a factually incorrect response that caused more than $100 billion to be subtracted off the company’s worth. This must rank as the most expensive demo in history.
LLMs Are Finally Here
ChatGPT is a LLM. I don’t mean this as bragging. However, the above example demonstrates how risky Large Language Models (LLMs) can be.
It also demonstrates the necessity for businesses to carefully consider what issues they can actually contribute to solving. This covers both tactical and strategic gains.
When evaluating the efficacy of solutions including LLMs from a business standpoint, there are at least two factors to consider.
Of course, the level of linguistic fluency the system is capable of is one factor. Conversational user interfaces have been present for a while, revealed by chatbots, speech bots, digital assistants, smart speakers, etc.
These systems can comprehend spoken or written language and react appropriately. This response can be given verbally, in writing, or by starting the requested activity.
The fact that these more conventional conversational AI systems are better at understanding than, for lack of a better term, expressing themselves, is one of their fundamental drawbacks.
Due to the fact that well-trained machine learning models typically operate based on pretrained intentions, they are also quite frequently able to surface a right solution for issues in the problem domain that they are learned for.
They frequently provide surprisingly accurate answers to issues pertaining to their field based on the training data. The issue is that they are frequently constrained to a relatively narrow set of domains.
The training of LLMs, on the other hand, typically focuses on “understanding the links between words, phrases, and sentences in a language. The objective is to have the LLM provide semantically significant outputs that accurately reflect the input’s context.
What is the aim of an LLM? is one of the questions that ChatGPT addresses.
A large portion of the “real world” knowledge required for an LLM training program typically comes from publicly accessible sources, such as the internet. The actual result itself can be presented in written, graphical, or another manner.
LLMs are exceptional at coming up with humane answers to inquiries. Additionally, they have a broad range of responses to offer.
They are designed to produce thoughtful and logical responses when concentrating on text.
The issue is that they occasionally perform inaccurately and confidently produce incorrect results.
Even worse, incorrect or inaccurate output is difficult for a user without the necessary understanding to recognize.
Refer once more to the Google Bard example, which (at least momentarily) reduced Google’s valuation by $100 billion US. And it’s not just Google; there are numerous instances that mention tools like ChatGPT or You.com, among others.
Related Reading: Can CX Drive The Switch From Product To Services-based Offering
How accurate are LLMs and ChatGPT?
The issue is whether or not both dimensions are always equally important. One can argue that precision is always important in business.
Receiving factual mistakes in a business conversation is not only a terrible customer experience example, but it could also, in extreme circumstances, result in legal problems.
It’s also crucial to realize that, the more precision is needed, the more extra systems must be integrated to support the LLM.
An LLM is really just a type of entertainment on its own. Even in search engines, LLMs only improve the search by permitting natural language questions and delivering results in human language rather than just a collection of links.
This is at least what they ought to do.
After all of this, what are some business use cases for an LLM? As previously stated, there must be some degree of precision. Naturally, since fluency is what distinguishes an LLM, they also need it as a prerequisite.
ChatGPT Use Cases in Business
1. Storytelling
I’d start with what I’d classify as “storytelling.” In essence, this is the process of producing market-relevant documents that outline the features and unique selling points of a certain good, service, or solution.
Since it is a first point of contact for clients and is largely marketing-related (no offense meant), it must be simple to understand without demanding a high degree of technical correctness.
Additionally, it must not be incorrect. The (better) generation of social media content, such as tweets, may be considered a simplified form of this.
Faster production of high-quality content for general websites, as well as more especially for ABM scenarios and landing pages, are the advantages of this.
An LLM must be connected to internal systems that hold requirements, specifications, and communications between the interested parties in order to be able to construct this language. In the near future, it should be possible to implement this use case as well.
2. Emailing
Writing and, more specifically, responding to emails is one of the primary tasks of individuals.
Particularly in sales circumstances, when customer enquiries can be recommended and formed based on prior emails and the context provided by the CRM system, such as regarding made proposals.
To prevent delivering inaccurate information that would be legally binding, this scenario would already call for quite a high level of accuracy.
The advantage of this scenario is that sending emails will take much less time, which will enhance productivity. Microsoft has already included this scenario in its Viva Sales solution.
3. Generating Documents
The scenario that requires fluency the most is the generation of documents. Technical documentation and user documentation are the two main categories.
Technical documentation’s writing style is somewhat less crucial than user documentation, which must be exceedingly readable.
As a result, alternative repositories or distinct portions of source documents must be used to construct the texts, diagrams, and possibly even images for technical documentation because it is likely that technical documentation requires a high level of accuracy that is not required for user documentation.
4. Customer Service
In the immediate term, customer service, including enterprise search, is one of the most promising use cases.
Users seek answers to their inquiries in this instance, not just links or an action. Connecting to conversational AI, business systems, and a functional knowledge base that aids in producing accurate results when looking for anything is required to do this.
A lot of what conversational AIs already do is comparable to how issues are handled. The LLM can produce more than enough training sets for intent detection, therefore it can be much better.
Additionally, the system’s responses are much more fluid. For a scenario including an enquiry, the same is true.
However, a word of caution: the enterprise search’s knowledge base (KB) content searches have a significant impact on how accurately queries are answered.
The KB must therefore be rigorously examined on a regular basis. When properly implemented, benefits include improved call deflection since more cases can be handled by the system and higher customer satisfaction because handling issues may be done by the customer in a way that is quick and easy.
5. Agent Assistance
Implementing agent assistance is a little bit simpler because it mainly requires a connection to the customer service application, which includes the chat history. Of course, having full access to sales and marketing data is also beneficial.
The LLM can recommend text blocks for the agent to employ when used in conjunction with a sentiment analysis.
Since the text blocks do demonstr
ate more empathy for the customer’s position than texts created without an LLM, this has the potential to result in higher customer satisfaction as well as increased agent productivity.
In closing
In conclusion, these five examples demonstrate real-world applications of an LLM. They may be quickly adopted, and it is also simple to link them to business results. Their advantages can therefore be quantified in this way.
What other ChatGPT use cases can you think of? And how would you connect them to commercial value? We at Kilowott, are here to help you with this transition thanks to customer experience transformation. Let’s talk!!!
