ChatGPT: Five business use cases with genuine customer experience value

Now that we are in the middle of – or hopefully closer to the end of – a general hype that was caused by Open AI’s ChatGPT, it is time to re-examine what is possible and what is not; what should be done and what not. It is time to look at business use cases that are beyond the hype and that can be tied to actual business outcomes and business value.

One only needs to cast their minds back as far as Google Bard to highlight the importance of knowing the strengths and limitations of your platform. In its release demo, Google’s AI chatbot gave a factually wrong answer, which wiped more than $100bn off of Google’s valuation – in what must surely go down as the most expensive demo of all time.

I say this without any gloating. Still, this incident shows how high the stakes are when it comes to large language models (LLMs). It also shows that businesses need to have a good and hard look at what problems they can meaningfully solve with their help. This includes quick wins as well as strategic solutions.

From a business perspective, there are at least two dimensions to look at when assessing the usefulness of solutions that involve LLMs.

One dimension, of course, is the degree of language fluency the system is capable of. Conversational user interfaces, exposed by chatbots or voice bots and digital assistants, smart speakers, etc. have been around for a while now. These systems are able to interpret the written or spoken word, and to respond accordingly. This response is either written/spoken or by initiating the action that was asked for.

One of the main limitations of these more traditional conversational AI systems is that they are better at understanding than in – for lack of a better word – expressing themselves. Relying on well-trained machine learning models, they are also quite regularly able to surface a correct solution for problems in the problem domain that they are trained for, due to the fact that they usually work based on pretrained intentions.

Based on the training data, they usually give quite accurate responses to questions in their domain. The problem: they are usually limited to a fairly small number of domains.

LLMs, on the other hand, are generally trained to “understand the relationships between words, phrases and sentences in a language. The goal is to have the LLM generate outputs that are semantically meaningful and reflect the context of the input.”

This is part of ChatGPT's answer to the question: what is the purpose of an LLM?. The training set of an LLM is usually a vast amount of “real world” knowledge that tends to come from publicly available sources – aka the internet. The output itself can be in written, graphical or other formats.

What LLMs excel in is generating responses to questions in a human way. And they can respond to a wide variety of topics. When focusing on text, they are built to generate coherent and meaningful responses.

The problem: they sometimes lack accuracy and give wrong output with full confidence. Even worse, wrong or inaccurate output is not easily identifiable by a user without the requisite knowledge.

Again, refer to the Google Bard example that (temporarily, at least) wiped off $100 billion US from Google's valuation. And it isn’t just Google; there are plenty of examples around that call out ChatGPT or You.com, amongst other tools.

How accurate are LLMs?

The question is whether both dimensions always matter equally or not. In a business sense, one can argue that accuracy matters always. Receiving factual errors in a business conversation is not only an example of poor customer experience but may in extreme cases even lead to legal issues.

What is also important to understand is that the more accuracy is required the more the necessity of integrating additional systems to augment the LLM increases.

An LLM on its own is not much more than some form of entertainment. Even in search engines, LLMs only augment the search by enabling natural language queries and the delivery of results in human language instead of a mere link list.

At least, this is what they should do.

With all this being said, what are business use cases involving an LLM? As said, there needs to be a reasonable accuracy. Obviously, they also require fluency as a precondition, as fluency is the core differentiator of an LLM.

Let’s look at some use cases in no particular order of priority.

aheadCRM

Examples of LLM business use cases

I’d start with something that I’d call “storytelling”. This is basically the creation of market-relevant documents that describe the capabilities and differentiating factors of a product, solution or service.

Being somewhat marketing related (no offence intended) and a first point of contact for customers, it needs to be easy to understand without requiring a great deal of technical accuracy. At the same time, it must not be wrong. A stripped-down version of this could be the (improved) generation of social media content, e.g. tweets.

The benefits of this are faster creation of high-level content for general websites but also, more specifically, for ABM scenarios and landing pages. To be able to create this text, an LLM needs to be connected to internal systems holding requirements, specifications, as well as communications between the involved persons. This is also a use case that should be implementable in the near-term.

One of the main tasks of people is the writing of, and more so, responding to emails. Especially in sales scenarios, where customer inquiries can get formulated and suggested based upon previous emails and the context given by the CRM system, e.g. about proposals made.

This scenario would already require quite a high accuracy to avoid sending out faulty information that might be legally binding. The benefit of this scenario is a significant reduction time needed to send emails, resulting in increased productivity. It is a scenario that Microsoft has already implemented in its Viva Sales solution.

Generation of documentation is a scenario that somewhat varies in the requirement for fluency. It can be mainly divided into technical and user documentation. While user documentation needs to be extremely readable, the writing style is somewhat less important for technical documentation.

Conversely, technical documentation likely needs to have a high degree of technical accuracy that is not needed in user documentation, which means that either different repositories or different parts of source documents need to be used to create the texts and potentially diagrams and images.

One of the most promising use cases in the short-term is customer service, including enterprise search.

Here, users want answers to their questions, not just links or something actioned. To achieve this, it is necessary to connect to a conversational AI, business systems and a well-functioning knowledge base that helps in generating accurate answers when searching for something.

The actioning of issues is very similar to what conversational AIs do already. The differences are that the intent detection can be far better, as the LLM can create more than enough training sets for this and the answers given by the system are far more fluent. The same holds true for an inquiry scenario.

However, as a word of caution, the accuracy of responses to inquiries depends heavily on the knowledge base (KB) content that gets searched by the enterprise search.

Therefore, the KB needs continuous and rigorous scrutiny. Properly implemented, benefits include an improved call deflection as more cases can get handled by the system, combined with an increased customer satisfaction as issue handling can become quite easy and efficient for the customer. Cognigy has recently presented some very good examples (here and here) that also include voice in- and output.

Agent assistance is somewhat easier to implement, as it mostly needs to connect to the customer service application, including the chat history. Having complete access to sales and marketing data, of course, is helpful too.

Combined with a sentiment analysis, the LLM can suggest text blocks for the agent to use. The benefits of this are increased agent efficiency and quite possibly also higher customer satisfaction as the text blocks do exhibit more empathy with the customer’s situation than texts generated without an LLM.

Conclusion

In summary, these five scenarios show use cases involving an LLM that are beyond the hype. They can get implemented in a short time and they can also be easily tied to business outcomes. That way, their benefits can get measured.

Which other use cases do you see? And how would you tie them to business value?