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Large language models change the work processes in companies – in sometimes surprising ways.

Before LLMs, companies generally had little choice but to manually manipulate unstructured data – or set it aside for lack of resources.

Take customer requests managed by employees, for example. There is likely to be enormous statistical insight in this amount of data.

However, manually processing calls or emails involves so much data that converting it into some form of structured data would be a herculean task , not to mention the effort involved.

Added to this is manual work when a customer needs a contact person to answer their call.

But LLMs are currently breaking new ground. Artificial intelligence has spawned a generation of chatbots that serve as the first point of contact for customers to provide fast, knowledgeable, and comprehensive answers.

The result is lower costs, greater responsiveness and greater customer satisfaction.

Chatbots are nothing new. However, five years ago they were generally not particularly helpful, often resembling two people talking to each other through translation software.

This system still exists and will continue to exist. It’s just that software programs can now quickly learn how people communicate through language.

They recognize patterns, nuances, tones, structures and more and interact with you in a language you understand.

Before large language models

Before LLMs existed, companies had to manually process structured data and create analyzes or reports from it. This was a time-consuming, laborious and error-prone process.
There were few or no ways to analyze unstructured data, e.g. B. in emails, social media and videos.
Customer requests were not processed as efficiently as it was a manual process. Understanding and responding to a query took time.

According to large language models

LLMs enable companies to quickly analyze large amounts of data and provide insights and analysis.
Because LLMs can mimic natural language interactions , chatbots are used to interact with customers as the first line of support. Chatbots interact in a natural language that customers can relate to; they are available 24/7 and can – in most cases – provide the majority of the information a customer needs.
One can make natural language queries to the databases, which will return the answers. This is a boon for executives who don’t want to communicate via SQL.
Companies can now make better use of both structured and unstructured data.
Large language models can analyze the tone of a user’s speech and react accordingly. This allows companies to understand a customer’s behavior and emotions.

Case study: Netflix’s recommendation system

With more than 17,000 titles, Netflix offers something for every preference.

However, if you put yourself in the shoes of a subscriber, finding what you like from this gigantic library of content can be an overwhelming experience.

Netflix is trying to solve this problem using machine learning in its recommendation system.

For example, you prefer to watch television series and films in the horror genre, often in Spanish.

The recommendation system remembers everything related to your behavior – patterns, types, viewing time, likes and dislikes – and compiles and suggests a special list of content to you.

How does the system work? Machine learning is about constantly collecting subscriber data on behavior, likes, likes and dislikes, genres, and more.

It continuously leverages data and gains insights into individual subscribers’ behavior and decisions, as well as the characteristics of viewers in a similar demographic.

It is an ongoing and evolving method that continues to work even if the subscriber changes their preferences.

What’s crucial in this context is how the large language models have helped Netflix ‘s business intelligence tools .

Limitations of LLMs

The limitations of large language models must be considered in terms of improving business intelligence tools.

LLMs may not understand the context of the data. The English word “bark” can be used in various contexts, from the bark of a tree to the dog that barked at the man. This is quite risky as it can lead to false information and reports.

There are also concerns about ethics and potential bias in responses from LLMs.

Bottom line

LLMs have many advantages; depending on how they are handled, they can completely redefine business intelligence.

Manual work can be moved to a server, allowing unstructured data to be analyzed comprehensively without the need for thousands of employees.

Specifics, characteristics and insights about your customers and business practices can be conveyed to you in natural language and with the weight of deep (artificial) thinking.

Although there are obstacles or limitations to consider, this is a new tool for businesses.