Impact of Large Language Models on Enterprise: Benefits, Risks & Tools

Discover how LLMs can impact your organization. We discuss how to automate customer service, conduct AI competitor analysis, or deploy autonomous agents within your business.

Occasionally, the tech world presents us with a new and promising tool that has the potential to revolutionize everything. Some companies eagerly experiment with it, while others prefer to watch from a distance.

Today, from the corporate greats to nimble startups, the burning question is:

Should we start using LLM and foundation models now, or should we wait and continue observing from the sidelines?

And, this is just the beginning. A flurry of additional questions are set to follow:

Will ChatGPT and Large Language Models (LLMs) turn the business landscape upside down, or will they simply blend into the norm, becoming part and parcel of everyday operations? Is this just handy tech to automate some tasks or is this the prologue to an AI-dominant corporate ecosystem?

We are tempted to lean towards the "game-changer" scenario. There is a long answer as to why, but if you cannot wait, you can jump to specific sections of this primer right now:

• Changes brought by Large Language Models and generative AI in tech
• Practical applications of LLMs to help companies save money or improve their operations
• Overview of important LLM tools and foundation models set to impact on the market

Hopefully, this article will help you navigate the hurdles of adopting large language models in business.

Before we dive into the specifics and explore practical examples and use cases, let's take a step back and view the bigger picture.

How foundation models differ from other recent tech “hypes”

Let’s consider cryptocurrencies, virtual reality, and the so-called “metaverse” for a moment. These all seemed like amazing ideas at one time or another. They were predicted to change the world as we know it. However, no matter how exciting a new technology may be, there are always three significant hurdles it needs to clear:

1. Getting people to use it (the user experience hurdle)
2. Building the tech to support it (the infrastructure hurdle)
3. Keeping it secure and private (the legal hurdle)

If we carefully analyze how each of these “revolutionary” technologies checks against these points, it is not that surprising that they have struggled to truly take off.

For example, virtual reality may be great for gaming, but using it for a simple HR meeting instead of a Zoom call can be an impractical overkill. Online payments and digital banking are useful, but for cryptocurrencies, scaling regulatory challenges has limited adoption. 

We can use technology to create simulations of our surroundings, essentially replicating real-world experiences. However, it is important to note that merely making everyday practices virtual for the sole purpose of being innovative may not provide any concrete benefits.

However—

LLMs are a different kind of beast.

LLMs and foundation models are not simply digitalising everyday experiences or environments. Instead, they involve combining almost all knowledge available online into a model that can effectively solve real world problems. In essence, instead of creating a simulation of external reality, with LLMs, we have created a living, interactive simulation of human language itself—complete with some of the knowledge that is encoded in it.

Generative AI powered by large language models can write emails to your customers, create website assets, and extract information from your data. However, using this technology for such tasks can feel like using a nuclear reactor to boil a kettle for a cup of coffee. The real heavy-duty use cases for generative AI are still emerging.

In essence, LLMs can create structured outputs from unstructured inputs.

And that is something quite new.

This is a genuine advantage and benefit for countless applications. We don’t just use technology to make experiences futuristic, or for the sake of them looking “innovative”. LLMs get stuff done, rather than creating hype without substance.

LLMs like ChatGPT have now become so powerful, they can pass the Turing Test. However, this has hardly made the news, because everyone is too busy creating amazing things with it!

So, what is the actual impact of ChatGPT on the world of business?

The impact of LLMs and foundation models on the business landscape

LLMs have promising practical applications that elevate them beyond hype. They are more than just a set of tools: they require a change in the entire framework and methods of using AI.

In essence, foundation models, and particularly Large Language Models (LLMs), have shifted the paradigm of AI development and deployment.

Now, to make sure that we’re on the same page when it comes to terminology—

What exactly are foundation models?

Foundation models are versatile ML models that are trained on extremely large datasets. Think along the lines of billions of articles, books, or images. To handle this amount of data, newer versions of models like the GPT engine are trained using thousands of NVIDIA GPUs.

But—

Once the training is completed, these models can be used on standard consumer-grade equipment. It takes millions of dollars-worth of processing power to train a foundation model like LLaMa or Stable Diffusion, but it takes a Macbook Air to deploy it locally and use it for pretty much anything you want. It’s worth noting, some open source foundation models are restricted to research-oriented use only, so you should be careful about implementing them in your business operations.

The primary difference between the “old” and the “new” paradigm is that you can use extremely powerful AI models without having to train your own. Foundation models can write, code, and generate images, which covers a significant portion of business operations. These pre-trained models can be used and adapted for specific tasks or uses. Currently, there are many free solutions available, which makes this a popular and accessible approach.

Not only does the new paradigm simplify the integration of AI into a variety of business use cases, but it also delivers solutions that outperform custom-built alternatives. For example, a ChatGPT model can be deployed as a virtual assistant, providing personalized and contextually appropriate responses to customer queries without requiring a substantial dataset of customer service scripts and conversations.

This change in the usage of AI from task-specific custom models to broader and more versatile foundation models represents a significant paradigm shift for businesses. Foundation models offer improved capabilities within their respective domains and they democratize access to machine learning solutions.

It is worth noting that LLMs have emergent abilities, such as an understanding of causality. These models learn to identify cause-and-effect relationships within text data just by observing patterns during training. This allows them to engage in complex reasoning tasks. For example, ChatGPT is a language model, but it also learned to solve simple mathematical problems on its own (under specific conditions) without explicit mathematical training.

💡 While traditional AI models were quite capable of solving specific 
tasks, their flexibility was very limited. With foundation models and
 LLMs, you can create complex workflows and handle more unique 
scenarios with confidence.

Read the full guide here to discover how to How to implement LLMs and foundation models into your business operations.