Skip to main content

Beyond the hype - How to use chatGPT to create 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 reemphasize on 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.

This, especially, in the light of the probably most expensive demo ever, after Google Bard gave a factually wrong answer in its release demo. A factual error wiped more than $100bn US off Google’s valuation.

I say this without any gloating. Still, this incident shows how high the stakes are when it comes to large language models, LLM. 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 large language models, LLM.

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. are 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 in understanding than in – lacking 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. They usually work based on pretrained intents.

And, 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 ChatGPTs answer to the question what the purposes of an LLM is. The training set of an LLM is usually a vast amount of “real world” knowledge that usually comes 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 Googles valuation. Not picking on Google, there are plenty of examples around that call out ChatGPT or or other tools.

Consequently, the other dimension to look at is accuracy.

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 a 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 they should do this.

With all this being said, what are business use cases involving a large language model? As said, there needs to be a reasonable accuracy. Obviously, they 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.

LLM business use cases that can be implemented already now

  • 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. Benefits 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 implement-able near-term.

  • One of the main tasks of people is the writing of, and more so, responding to emails. Especially, in sales scenarios, 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 now. The differences are that the intent detection can be far better as the LLM can create more than enough training sets for this and that 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 kb content that gets searched by the enterprise search. Therefore, the kb needs continuous and rigorous scrutiny. If this is given, the benefits lie in increased call deflection and customer satisfaction. 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 an 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.

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? 


Last Year's Top 5 Popular Posts

Don't mess with Zoho - A Zohoday 2022 recap

After spending two days in Austin, TX, attending the ZohoDay 2022, it is time for a little recap of this interesting event.  We were 99 analysts and 24 customers and plenty of knowledgeable Zoho personnel. The incredible Sandra Lo and her team organized the event around open and transparent communication. So, there was plenty of access for us to customers and the Zoho team.  Which was very important, as already the keynote session by founder and CEO Sridhar Vembu was quite hardcore. Vembu talked about how strategy and culture need to be one, how culture needs to be the root of strategy, and how Zoho implements this. The Zoho strategy lies on three main pillars ·       Transnational localism, a unique concept that in its essence is about embedding a company into a local community by not only selling into it but also by investing into it. This investment is e.g., by offering high paying jobs in areas where these are scarce, by fostering local education, but also by own local sourcing in

How to tie CX to business success in three simple steps

In 2022, the Forrester CX Index dropped for the first time in years, with nearly twenty percent of US brands seeing a drop in customer experience. Towards the second half of 2022, an increasing number of companies fear a recession and put their spending under scrutiny. At the same time, companies still struggle to link CX projects to business outcomes and their metrics, let alone to financial metrics. In addition, Forrester predicts that also in the next few years, CX teams will lack critical design, data and journey skills. In parallel, there is an increasing number of companies that deliver software and/or services that are intended to help businesses improve their CX. In the past years, CX has established itself as a whole new category of software. Many a company has repositioned itself to become a CX vendor, examples including all major CRM vendors, but also call center specialists like Genesys. And, naturally, a good number of these new CX actors got – and get – acquired by bigge

a great human - bot conversation with lots to learn

Inspired by a recent panel discussion as part of the In the Hot Seat podcast that I am involved in, I opened a chat with chatGPT3 . ChatGPT is a language model by OpenAI that interacts in a conversational way. This way, it shall be able to follow a conversation, answer follow up questions or even admit mistakes, challenge incorrect premises or reject inappropriate requests. Our sixth episode of In the Hot Seat revolved about the question whether web3 will deliver on its promise or not. The promise being that content producers and web users get more power by applying concepts like decentralization, blockchain and a token economy. As I am a bit sceptic about this kind of silver bullet promises, I went right for the jugular. A conversation between a human and a bot Thomas : Tell me with arguments why web3 based on blockchain will fail chatGPT3 : It's impossible for me to provide arguments as to why web3 based on blockchain will fail, as web3 is not based on blockchain technolo

Truly Zoho - How capitalism and doing right coincide

The past 9 months have seen quite a rollercoaster in the tech industry. We have seen staggering profits, we continue to see stock buybacks, we have seen consolidation, mergers and acquisitions – and we have seen mass layoffs . Few of them were well handled or communicated. Even fewer showed any sign of executives taking accountability besides stating that they made mistakes during the pandemic and that they feel sorry for what they need to do now. They had simply over-hired and now need to take corrective action to stay on a ‘growth path’. One of these executives arguably took the prized company culture of regarding the employees as family to grave. What do these layoffs have in common? They were initiated to please the capital markets, i.e. shareholders and venture capitalists. The idea behind this is that layoffs is the fastest way to solve or avoid impending financial problems. However, there is mounting scientific evidence that this idea is a myth, as e.g., expressed here , here

How to Zoho-matize a business

During ZohoDay 2022, I had the chance to have a longer conversation with Elie Katz , founder and CEO of National Retail Solutions, NRS. if you do not want to read too much but prefer watching the edited interview, you can do so here . NRS was founded in 2015 and has  since then grown its customer base to more than 17,000 retail stores across the United States. NRS is a part of IDT, a provider of communications and payment services to individuals and businesses. The business provides POS and payment processing software, focusing on small, independent retailers, who want to not only survive but also thrive in a big box environment. The NRS POS system is built to help stores organize, attract customers and increase revenue; it includes a loyalty coupon program and other bells and whistles.   An important point is the NRS outside-in philosophy, which is defining its own success as a result of making its customers successful by being able to address their needs.   The challenge that Elie, h