How to use AI in business? – Guide 2024

In this article, I present you the options, their benefits and drawbacks, for integrating AI into your business.

Using Artificial Intelligence in 2024 has become a major issue for companies aiming to remain competitive.

Today, there are 3 main options for taking advantage of this technology:

  • develop AI solutions in-house
  • use third-party AI services
  • exploit Transfer Learning

However, it can be difficult to determine which option is best suited to your company’s specific needs.

Here, I present the advantages and disadvantages of each solution ⬇️

In-house development of AI solutions


Developing AI solutions in-house requires developing resources within the company.

This approach demands significant and varied investments, particularly in terms of:

  • qualified personnel 👨‍🦰
  • tools and software 💻
  • time ⌛️

The process of creating customized AI solutions in-house gives companies complete control over the development and implementation process.

However, this approach requires an in-depth understanding of Artificial Intelligence. What’s more, it requires a willingness to invest in the long-term development of these resources.

Benefits and challenges

One of the main advantages of in-house AI development is its adaptability. This enables them to respond precisely to the company’s specific needs.

This customization can lead to the development of more efficient and effective tools, aligned with team objectives and processes.

Moreover, retaining control over the technology enables greater flexibility in terms of adaptation and scalability as business needs evolve.

However, this approach comes with significant challenges.

The first of which is the need for substantial investment. Qualified personnel with expertise in AI and the relevant sectors need to be recruited.

Recruiting and retaining this talent can be a major hurdle. Particularly for small companies or those in competitive markets.

In addition, the investment in the necessary technological infrastructure can be considerable.

The risks of project failure are also higher. Developing AI solutions in-house does not guarantee success. It can result in significant sunk costs if the project fails to achieve its objectives.

Case studies/Examples

Let’s consider the case of small companies in the financial sector looking to develop in-house AI solutions for data analysis.

These companies typically face a unique set of challenges and opportunities.

On the one hand, they have specific data analytics needs. Such needs may not be adequately addressed by off-the-shelf solutions.

Indeed, a small financial company may require customized algorithms. These algorithms are needed to assess risk or formulate investment strategies tailored to their niche market or specific investment philosophy.

On the other hand, these companies often lack the considerable resources available to large corporations. This limitation makes the development of in-house AI solutions a major endeavour.

They have to invest strategically in recruiting talent and developing the necessary technological infrastructure.

Despite these challenges, when successfully deployed, in-house AI solutions offer a competitive advantage. They provide insights and efficiencies not available with standard AI solutions.

Using third-party AI services


Third-party AI services, such as those offered by OpenAI with ChatGPT, are a parallel solution to the development of in-house AI solutions.

These platforms offer ready-to-use AI services that can be integrated into a company’s existing systems.

Using these services enables organizations to exploit advanced AI technologies without the need for extensive in-house development.

This approach is particularly attractive to companies that may not have the resources or expertise to build their own AI solutions from scratch.

Cost-efficiency and ease of integration

One of the main advantages of using third-party AI services is cost-effectiveness.

These services often have lower initial costs than developing in-house solutions.

Since the AI technology has already been developed and is maintained by the service provider, companies escape the significant costs associated with the initial design of the service and its ongoing maintenance.

This aspect makes third-party AI services particularly attractive to small and medium-sized businesses, as well as startups.

Another significant advantage of this option is ease of integration.

Third-party AI services are designed to be easily integrated into existing systems. This enables companies to expand their business without significant disruption to existing operations.

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Now we can get back to what I was talking about earlier.

This integration can often be achieved with minimal technical expertise, reducing barriers to entry for companies new to AI.

Limitations and points to consider

However, the use of third-party AI services comes with its own set of limitations and points to consider.

One major limitation is the potential lack of customization.

While these services are advanced and diverse, they may not offer the level of bespoke functionality that some businesses require.

This can be a major handicap for companies with particularly demanding requirements.

Note: This is not necessarily the case for ChatGPT, which can easily adapt to new data and contexts. Nevertheless, this may be the case for other AI services.

Another consideration is the dependence on external suppliers.

The use of third-party services means that companies rely on another entity for crucial aspects of their service.

This reliance can raise questions about the stability of the service, control of the technology and possible changes in service conditions.

In addition, data confidentiality and security are paramount considerations.

Companies need to assess how their data is handled by third-party suppliers, and ensure that they comply with current data protection regulations.

This is particularly important in sectors where sensitive or proprietary data is involved.

So, while third-party AI services are a practical and cost-effective solution for many companies, it’s important to weigh their benefits against their potential limitations, and to consider the organization’s specific needs and situation.

Exploiting Transfer Learning

Explanation of the concept

Transfer Learning is an artificial intelligence method which involves reusing a model developed for one task as the starting point for a model for a second task.

This method is particularly useful in situations characterized by a scarcity of data, or when the development of a complete model requires substantial resources.

Companies can take advantage of Transfer Learning by using pre-trained models, such as those available on platforms like Hugging Face, and adjusting them with their own data to meet specific needs.

This approach makes it possible to harness the advanced capabilities of AI without the need for intensive prior training and development.

A balance between personalization and efficiency

Transfer Learning offers a balance between personalization and efficiency.

It provides a middle ground between developing AI solutions entirely in-house and using standardized third-party services.

By starting with a pre-trained model, companies can save time and resources in the initial development phase.

They can then concentrate on fine-tuning the model with their own data sets, adapting it to their specific needs.

This customization can result in AI solutions that are more effective than those that could be achieved through standard third-party services, without the significant investment required for full in-house development.

Practical steps and examples

Implementing Transfer Learning involves a number of steps.

Let’s take the example of a company looking to develop a computer vision system to control the quality of its products.

It could start by selecting a pre-trained model that performs well in object recognition tasks.

The next step would be to collect and pre-process its own data – in this case, product images – in order to refine the model.

The company would then train the model on this specific dataset. It would optimize its performance for its particular use case by adjusting parameters and making modifications.

If the manufacturing company is specialized in electronic components, using Transfer Learning, it could quickly develop a system that uses computer vision to identify defects in its components.

The pre-trained model would provide the basic ability to analyze images. Then refinement with the company’s own data would ensure that the model performs well. It would then be able to recognize the particular types of defects that are relevant to their products.

Transfer Learning offers a practical and efficient way for companies to develop tailor-made AI solutions.

It reduces the need for in-depth AI expertise and resource investment. Furthermore, it enables a degree of customization that can bring significant benefits to the company.

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Tom Keldenich
Tom Keldenich

Artificial Intelligence engineer and data enthusiast!

Founder of the website Inside Machine Learning

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