AI for Business Leaders: How to Incorporate AI into Your Business While Ensuring Company Needs

A tricky question: are autoencoders or transformers for implementing AI in business?

In the articles and white papers, business leaders come across a lot of terms related to AI and machine learning. Is it necessary for a business leader to differentiate technological approaches and understand, let's say, the difference between certain types of neural networks? Should a business leader know what Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), or Transformers are to integrate AI effectively?

This guide to implementing AI will help clarify what knowledge is essential and what can be delegated while offering insights on how to incorporate AI into your business effectively.

The challenge that a business leader will likely face is choosing the most performative and cost-efficient solution from among a number of good options. These challenges’ key points often come down to understanding the trade-offs between performance, cost, and implementation of AI in business processes.

These steps to implement AI often require collaboration with a CTO. A tech expert will tackle two main tasks:

• Taking into account the key technical characteristics of LLMs and the cost of incorporating a solution with a model through an API. Key attributes of LLMs include the number of model parameters and context window size, both of which generally follow the principle “the more, the better.” It is also critical to evaluate the expected number of tokens, as this helps assess the anticipated cost of using the model. The number of tokens characterizes the volume of information expected to be processed by the model.

• Consideration and comparison of key criteria such as latency, perplexity, cost, and others for evaluating LLMs. Such an assessment involves specialized methodologies, approaches, and tools. Hence, ML expertise is needed to evaluate a particular model’s capabilities. What is even more important is that an ML engineer can assess a particular model’s potential for the specific project, which is key to the project’s success.

Let’s sum up. While CTOs and ML engineers, as bearers of technical knowledge, are well-suited to choosing the solution with the desired technical characteristics, a business leader creates the “project ideology” frame and incorporates it into the broader company strategy, providing a clear vision for how to use AI in your business.

Questions for a business leader to answer before AI implementation

Also, one more key question arises, critical from both a strategic and practical point of view:

It may seem that LLMs rule the world of AI-driven solutions. All widespread tools like ChatGPT, Microsoft Copilot, or Google Gemini are based on LLMs. They are competent and perform well — otherwise, they wouldn't be so widespread.

On the other hand, some LLMs' advantages turn into weaknesses. Moreover, certain LLMs' drawbacks are imminent due to their complexity and large scale. Considering all the mentioned factors, it looks natural that the trend of small language models becoming more in demand has become a benchmark for 2024 in the implementation of AI in business strategies.

Let’s compare both options in more detail.

The key consideration: an LLM vs a small-to-medium language model for integrating AI into business

As AI becomes more integrated into business processes, the choice between a large language model (LLM) and a smaller, more specialized model is crucial.

Business leaders must weigh the capabilities and limitations of each option to find the most effective solution for their specific needs.

This guide explores the core differences between LLMs and small-to-medium models, helping to clarify which approach best aligns with the company's strategic goals.

LLMs: powerful yet not domain-specific for integrating AI into business

LLMs are large (huge) neural networks. Their “understanding” of human language is the result of complex statistical computations and the establishment of connections, more or less close, between tokens (words and parts of words). Neural networks have evolved as a product of machine learning, meaning their operation is fundamentally rooted in ML principles.

LLMs are the foundation for a wide range of features and tools across different applications, making them a prominent choice when integrating AI into business strategies.

Here are just a few examples of tasks that LLMs are created for (for simplicity, we’ll focus mostly on LLMs’ abilities to work with text, i.e., on the text-to-text modality):

• Automated content generation.

• Chatbots and AI assistants.

• Search engines.

• Personalized content recommendations.

• BI tools.

To summarize, tools based on LLMs intelligently automate routine tasks by processing information, making decisions, and generating new content.

To be able to handle their tasks, LLMs go through some steps:

• Constructing the foundational model by pre-training the neural network. A model is trained to process human language and generate text that reflects patterns in how the world is described. Training data is provided by the model's creator, and this data is general. This stage can be compared to a model receiving fundamental education.

• Fine-tuning the model. At this step, the model acquires specialized knowledge in a specific domain. Training is based on proprietary data. Continuing with our metaphor, the model completes college or university.

To grasp the capabilities of LLMs, you can experiment with any of the AI copilots. Whether you're interested in personal use or incorporating these tools into your corporate workflow, you'll discover an efficient solution.

However, there is another side to the coin: LLMs have drawbacks, and knowing them is beneficial for leveraging AI tools to your business's advantage in the implementation of AI in business strategies:

• Excessive versatility. For some tasks, LLMs turn out to be too versatile. It's natural since LLMs are created to meet the requirements of various industries. However, if you want to incorporate an LLM as the foundation for a specialized tool, its performance will decrease, as materials from TechTalks, in particular, show. What is more, for fine-tuning, i.e., calibrating a model for tasks in a certain domain, one needs proprietary data of high quality.

• Vulnerability to data leakage. The model is accessible to a vast number of users, yet the mechanisms for personal data protection are far from perfect. As a result, data loss can occur, even among tech giants like OpenAI. (Let’s recall the incident when Samsung reported sensitive data leakage through ChatGPT).

• Jailbreaking. Protection mechanisms for models against hacking are being improved, but thanks to “enthusiasts”, new ways are constantly found to force the model to disclose data it should not. One of the latest examples is the experiment where researchers used ASCII art prompts to bypass safety measures set for such stellar models as GPT-3.5, GPT-4, Gemini, Claude, and Llama2.

• Proneness to hallucination. Because LLMs have plenty of knowledge in their “brains,” they can fantasize, and you’ll never know for sure when the information needs checking. As a result, verification is always necessary when the outcome is critical — very often.

• Unpredictable costs. Companies that provide access to large language models (LLMs) through APIs for building AI-powered solutions base their pricing policies on the number of tokens. For example, the usage of GPT-3.5-turbo ranges from $10 to $30 per million tokens, depending on input and output usage. Is the price low, medium, or high? It’s hard to say, especially when considering that the budget must also cover the salaries of an ML engineer and a backend developer to incorporate and maintain the API pipeline. Therefore, the final figure can be quite vague when managing the implementation of AI in business solutions.

LLM's advantages and vulnerabilities

Let’s sum up. LLMs are versatile and powerful tools that extend human capabilities in handling information, creating content, and analyzing data, making them highly valuable for integrating AI into business processes. However, they also have vulnerabilities.

Large models require significant resources, including data for fine-tuning and computational power, as well as ML engineers and backend developers to establish processes and maintain AI-powered tools.

Small-to-medium models: custom and domain-focused

We’ll consider the specificity of smaller models using one of the CoSupport AI’s solutions, CoSupport Agent, as an example. This way, we can back up these considerations with the experience of our team of engineers who have been developing AI-powered assistants for customer support since 2020.

In addition, by using patented AI architecture as an instance, we can make more informative comparisons on how to incorporate AI into your business effectively and optimize the implementation of AI in business.

Before diving into how small models are different from LLMs, it’s worth mentioning that both types have the same background for skills, as they are neural networks by nature.

The key peculiarity of the CoSupport AI constellation of small-to-medium models lies in two dimensions:

• Focus on the tasks of a specific domain (customer support, in particular). Returning to our metaphor, a small model is like a student at a specialized university with a high level of knowledge in their field.

• Potential for customization. While building an AI solution on an LLM, one doesn’t have precise knowledge of the data on which the model was trained. In contrast, a custom model is trained explicitly on data provided by the client, which focuses on the company’s products and services. The quality of this data is assured, provided the client makes an effort to supply high-quality information. Hence, the inclusion of spare or unknown information in models’ “brains” is minimized.

As with LLMs, let's examine the stages of training and adjustment to a specific business field that a small-to-medium custom model undergoes to power a solution like an AI assistant — the most in-demand tool for enhancing customer support:

• Pre-training. This stage can be compared to elementary school, where AI learns language and acquires communication skills.

• Fine-tuning. At this stage, the straight-A student immediately becomes a university student to acquire specialized knowledge, in our case, in customer support.

The fact that the CoSupport AI copilot skips the stage of “basic university education” might seem like a disadvantage, but it is not. The AI assistant is not burdened with "extra" knowledge, and it allows the model to respond more accurately and specifically.

There are some other custom models’ advantages:

• Faster response. One of CoSupport AI’s products, Cosupport Agent, demonstrates a faster response speed than ChatGPT. While ChatGPT requires up to 3 seconds to respond, the CoSupport Agent provides an answer within milliseconds. It’s possible partially because of the unique neural network’s architecture and partially because of CoSupport AI’s patented technology of multimodel message processing. Both justifications for the speed of responses speak in favor of the custom model, its flexibility, and the ability to adapt the solutions to the needs of the particular domain.

• High accuracy. Custom models operate with knowledge closely related to the business domain. A smaller volume of more focused data reduces the models' speculation about what the correct answer could be, thereby lowering the risk of hallucinations. Moreover, the custom models’ training is based on proprietary data of verified quality; it increases response precision.

• Data security. A smaller model can be fine-tuned with high-quality data and then isolated from databases, significantly reducing the risk of data leakage. Furthermore, personal data can be anonymized to ensure the highest level of protection.

• Predictable prices. While small-to-medium model providers’ pricing schemes vary significantly, their pricing policies are generally more flexible compared to those offered for using large language models (LLMs). In particular, CoSupport AI offers comprehensive pricing policies that cover definite outputs — such as model fine-tuning, building an API pipeline, model testing, reinforcement learning, hosting, and maintenance — for a fixed price,vall of which contribute to the efficient implementation of AI in business operations.

Small-to-medium language models' advantages and vulnerabilities

For companies in search of an AI solution for a particular domain, small-to-medium custom models can be a worthy alternative to large language models (LLMs).

Custom models offer more opportunities for tailoring AI solutions to specific types of tasks and often outperform large models in certain metrics. Additionally, vendors of small-to-medium models usually offer more flexible pricing policies that allow for easier budget planning.

LLMs and small-to-medium models comparison: an overview

When it comes to developing an AI-powered solution, one of the most crucial decisions a business leader faces is selecting between a large language model (LLM) and a small-to-medium model. This decision can significantly impact the performance, cost, and security of the AI system.

While LLMs are known for their versatility and broad knowledge base, small-to-medium models offer more precision and focus, particularly in specialized domains.

Each option brings its own set of advantages and limitations, which need to be carefully weighed based on the specific needs of the business.

To provide clarity, let's break down the key differences between these models and explore how they align with various business objectives. This comparison will help you make a more informed and strategic choice as you move forward with how to use AI in your business and AI implementation.

LLMs and small-to-medium language models comparison

Conclusion: how to use AI in your business

How much technical knowledge did you need to grasp the essence of this article? You need a knowledge of basic terms — LLM, neural network, ML, model training, and fine-tuning — nothing more.

These two groups of solutions differ in their ability to provide quick and accurate responses and ensure data security when working with a specific subject area. What is more, small-to-medium model vendors usually offer more flexible pricing schemes that make budgeting more predictable.

Although comparative studies on this topic are still in their infancy, business leaders can make well-informed decisions by engaging with AI solution providers to discuss model capabilities, potential vulnerabilities, ways to address them, and customizable pricing policies.