GenAI guide: Steps to start implementing it in your company

Now that you've grasped the key concepts surrounding Generative Artificial Intelligence (GenAI), it's time to delve specifically into the concept of GenAI.

In this article, we explain how Generative Artificial Intelligence (GenAI) has become a crucial component for companies aiming to lead in their respective sectors.

In this step-by-step guide, we will cover:

• Economic Impact in Spain
• Available GenAI Models
• Advantages and Disadvantages of GenAI
• GenAI Application in Business
• GenAITools

GenAI is a branch of AI, a subfield of deep learning that employs neural networks to generate new data resembling existing data, making a significant impact across various industries. This subcategory focuses on creating new data and content, from text to images and sounds, through machine learning models. This data generation capability opens up possibilities for product design, content creation, and service personalization, among other applications.

There are two types of models:

• Discriminative Models: Used for classification or prediction, trained on labeled datasets, learning the relationship between data point features and labels.
• Generative Models: Generate new data similar to the data they were trained on, understanding the data distribution data and predicting the next element in a sequence.

The economic scope of Artificial Intelligence (AI) at the business level and in the overall economy is considerable. By automating routine tasks, AI can enhance a company's performance, provide personalized recommendations, or improve the customer experience, helping businesses increase their profits.

Regarding IAG, it holds the potential for significant economic impact, as suggested by a McKinsey report indicating that IAG could contribute up to $4.4 trillion to the global economy. Although the specific impact on Spain is not specified, it is reasonable to assume that it could substantially improve efficiency and productivity.

From its early stages, AI models have progressed rapidly, with technology giants such as IBM, Microsoft, Google, and AWS leading the revolution. They have developed AI solutions capable of understanding customer needs, recommending the most suitable products, or optimizing business processes.

Before delving into the topic, it's crucial to understand the difference between models, applications, and solutions in the context of GenAI:

• Model: Machine learning algorithms trained to perform specific tasks, forming the basis for building applications and solutions. They are rapidly evolving, offering a wide range of applications in the business sphere.

Different generative AI models include:

• Generative language models used for text generation, translation, and natural language understanding.
• Generative image models for creating new images with features similar to patterns learned from other images.
• Text-to-text generation models for tasks like answering questions, summarizing, or sentiment analysis.
• Text-to-image or video generation models capable of translating text descriptions into visual representations and vice versa.

Some well-known models are:

• DALL-E: Presented by OpenAI in 2021, this pixel-generating transformer-based generative model has shown its ability to create high-quality images from natural language descriptions, with significant potential in product design and content creation. The currently available version of this model is DALL-E3.
• Claude 2: Developed by Anthropic, an AI research company, this GenAI model combines supervised and unsupervised learning to generate synthetic data. Claude 2 has proven effective in various tasks, including text generation, automatic translation, and image generation.
• LLAMA 2: Another GenAI model, published by Meta (Facebook), uses a combination of supervised and unsupervised learning, particularly effective in text generation and applied in various applications, from content generation to user interaction enhancement.
• ChatGPT: Developed by OpenAI, this GenAI model can generate text responses to questions or prompts in natural language. ChatGPT is used in various applications, including chatbots, virtual assistants, and content generation tools. The recent introduction of GPT-4 Turbo models with 128K context and GPT-4 Turbo with Vision was presented at the OpenAI DevDay on November 6.
• Bing AI: Developed by Microsoft, this GenAI model is utilized in the Bing search engine. It can generate responses to user questions, enhancing the user experience and search engine efficiency.
• Google Bard: Developed by Google, this GenAI model can generate coherent and high-quality text. It is used in various applications, from content generation to user interaction enhancement.

The infographic below illustrates the evolution of language models:

• Application: Specific implementations of GenAI models solving particular problems. For example, a customer service chatbot could be powered by the GPT model.
• Solution: Sets of applications and/or models addressing a larger business problem or a series of problems. For example, a digital marketing platform could use the GPT application to generate content in text or image format.

GenAI offers numerous advantages, with some highlights:

• Synthetic Data Generation: GenAI can create data imitating real data, allowing companies to test and optimize without compromising customer privacy.
• Fostering Innovation: GenAI can foster creativity in various areas, from product design to content creation and service personalization.
• Operational Efficiency: GenAI can automate repetitive tasks and business processes, resulting in increased efficiency and cost reduction, enhancing productivity.
• Advanced Personalization: GenAI enables deeper and more accurate customization of products and services, improving the user and customer experience.
• Scalability: GenAI models can easily adapt to different sizes and types of data, making them highly scalable.
• Flexibility and Adaptability: Unlike specific AI, GenAI can adapt to various tasks and applications.
• Improved Return on Investment (ROI): Implementing GenAI can lead to a significant return on investment by optimizing various business areas.
• Rapid Prototyping: GenAI can accelerate the development process of new products or services by generating rapid prototypes for testing.
• Competitiveness: Companies adopting GenAI gain a competitive edge in the market by staying at the forefront of technology.

Regarding disadvantages, they include:

• Cost: Implementing and maintaining GenAI solutions can incur high costs, especially for small and medium-sized enterprises. However, this is changing as cloud technology eliminates the need for data specialists, making such projects feasible for engineering teams.
• Technical Complexity: GenAI requires a high level of data science and machine learning expertise, which can be a barrier for some organizations. Therefore, having an expert or a good partner in this field to facilitate the task is recommended.
• Data Dependency: GenAI's effectiveness is strongly linked to the quantity and quality of available data. Hence, having a proper data architecture that enables its utility is crucial.
• Ethical Risks: Generating synthetic data and AI-generated content can pose ethical issues, such as creating false, misleading, or biased information.
• Privacy Concerns: Although GenAI can generate synthetic data, concerns persist about how data, i.e., the original source, is collected and used. To address this, it is crucial to respect the GDPR, verify the data's origin, ensure that its treatment complies with principles of transparency, legality, and minimization, and conduct impact assessments on their protection.
• Resource Consumption: GenAI models often require a large amount of computing resources, which can also be a disadvantage regarding energy efficiency.
• Technological Immaturity: GenAI is still a recent technology with certain limitations and implications still being discovered. Nevertheless, its evolution is advancing rapidly.

Introducing GenAI into your company is a significant strategic opportunity. To address its challenge, here is a brief guide with the following steps:

1. Identify Your Objectives: The first step is to define your company's goals and objectives and understand how AI can assist your business in achieving them. This way, you can choose the AI solution that best fits your business needs.
2. Analyze Your Data: The effectiveness of AI depends on the quality and quantity of the data you have. Therefore, it is important to have a good understanding of the data at your disposal and consider what additional data you may need.
3. Data Architecture: It should be designed with scalability in mind, supporting both current and future needs of your company in terms of data storage, processing, and analysis. Refer to the article on artificial intelligence models as a service for more information.
4. Implement AI Solutions: Once you have a clear understanding of the previous steps, it's time to take action. You can choose to develop your own AI solution with custom development or opt for pre-developed Cloud AI solutions that require less customization. We discuss the latter in more detail in the article.

The selection of tools is fundamental for the success of GenAI projects. Some essential tool categories and specific examples are:

• Development Platforms and Frameworks: Necessary for developing, training, and deploying GenAI models, such as TensorFlow or PyTorch frameworks.
• Data Processing, Analysis, and Visualization Tools: Used to train GenAII models. Python libraries such as Pandas, NumPy, or Seaborn can be used for data manipulation and analysis.
• Integrated Development Environments (IDEs): Such as Visual Studio Code or Jupyter Notebooks, facilitating code writing, testing, and debugging, and including collaboration and versioning tools like GitHub.
• Specific Libraries and APIs: Such as the generative language model development libraries offered by OpenAI.
• Cloud Platforms and Services: As discussed in this article.

You can use these tools to innovate your products and services, thereby improving your competitiveness and efficiency in the market. For advice from our team of experts, contact us. We will help you explore the possibilities that GenAI can offer and how it can transform your business for the future of AI.