Generative AI at the service of your brand's progress

A few months ago, I had the chance to attend a conference led by a lawyer specialized in artificial intelligence. He spoke about the legal gaps surrounding the use of this technology, which has increasingly become part of our everyday lives.

During the talk, he addressed some major challenges: controlling the authenticity of content generated by generative AI, the possibility of confidential business information leaking into AI learning algorithms, such as ChatGPT’s, and the ethical boundaries we must consider when extensively using this new technology.

As the conference went on, I remember thinking that these existing legal gaps might lead some companies to fear using artificial intelligence due to the risks involved. As a result, they could end up restricting its use within their teams, which could eventually affect their competitiveness and slow down their business progress.

Before continuing, I should clarify that I’m an avid advocate for artificial intelligence. I genuinely believe it's vital for humanity’s future that we develop general artificial intelligence (I’ll explain this term later), which can help us break knowledge barriers and propel our civilization beyond our planet.

However, I acknowledge that the challenges we face with this new technological revolution are broad, complex, and might even be discouraging for some. This is why I felt motivated to write this article—not only to share my beliefs about this fascinating topic but also to explain why I think it's crucial for your company to start using generative AI, or if you're already using it, to keep effectively scaling its adoption.

What is artificial intelligence?

Before diving into this topic, let's make sure we’re all on the same page. You’ve probably heard about ChatGPT, Gemini, Claude, Llama (Meta’s AI, which you might have seen on your WhatsApp or Instagram), and several other AI tools that suddenly appeared in our lives.

But do you really know how to tell what artificial intelligence is—and what it's not? Artificial intelligence is an area of computer science and engineering that aims to create systems capable of performing tasks that normally require human intelligence.

Some examples of these tasks include language processing, speech recognition, vision and interpretation of the world, and, above all, continuous learning through interaction with the environment.

Types of Artificial Intelligence

The new AI tools we've seen become available for everyday users in the last two years are the result of decades of research, algorithm development, and applied science. Even though recent progress seems incredible, we're still on the first page of an exciting story where humans are on the path toward generating or simulating human consciousness.

To help you visualize this better, it's important to highlight two main types of artificial intelligence, on which there's general agreement:

• Weak or Narrow AI: These AI models are designed to perform specific tasks. This type of AI is programmed and trained using datasets, allowing it to recognize clear patterns that determine its inputs and outputs. Some examples include ChatGPT (and similar tools), Siri, Alexa, Google Assistant, and the algorithms used by platforms like Meta, YouTube, and TikTok for recommending content.
• Strong or General AI: For now, this is only a theoretical concept, though many of us hope to see it in our lifetimes. The idea is an AI capable of understanding, learning, and applying knowledge in a general way—much like humans do. To make this concept a reality, AI would need to achieve some level of consciousness, reasoning, planning, communication, and the ability to adapt to a changing world. Currently, there's no existing general AI developed by humanity, but this goal is being pursued by companies like OpenAI, Anthropic, and Google DeepMind, among others.

Uses of Artificial Intelligence: Generative and Prescriptive AI

Although it might seem that AI suddenly appeared in our lives around 2022, the truth is we've been living alongside it for a long time. Social media algorithms, smartphone assistants, translation tools, and many other examples demonstrate this. However, the major breakthrough that changed everything was the invention of the Transformer, giving rise to what's now called generative artificial intelligence.

This concept was introduced for the first time in 2017 by researchers at Google in a paper titled “Attention is All You Need.” In this scientific article, they proposed that through attention mechanisms and parallel processing, AI models could better understand the context of the information they're analyzing.

If you'd like to learn more about transformers and how they work within neural networks, I recommend watching this video:

Context is the key element that ensures both the input and output processed by an AI model make sense, allowing the model to generate meaningful information from given data. This concept has led to one of the most recent breakthroughs that's revolutionized our lives: generative artificial intelligence. Generative AI, such as ChatGPT (by the way, GPT stands for Generative Pre-trained Transformer), can understand an input (prompt) and generate output in the form of text, audio, or images with such quality that it has started to challenge human creativity itself.

Similarly, prescriptive AI—which not only predicts future outcomes but also provides recommendations to reach specific goals—has also grown rapidly thanks to the rise of transformers. Its popularity has increased significantly due to the growing need of many businesses to use data-driven insights for structured decision-making about the future.

However, we shouldn't confuse these two types of AI. Generative AI doesn't have the same capabilities as prescriptive AI. Their goals are different, and they serve distinct purposes. Some people mistakenly believe technologies like ChatGPT can generate predictive models. While these tools can certainly help analyze data and provide deeper insights based on the information users supply, their primary function isn't predictive or general-purpose forecasting.

The importance of scaling productivity with generative AI

There's a common challenge faced by all businesses looking to strengthen their digital presence: the huge volume of content now needed across various channels and platforms.

Brands have multiple touchpoints requiring content creation or curation. On social media, companies must create highly interactive, viral content tailored to each platform’s unique tone. Video is king, and companies must produce videos for different channels and purposes. Working with influencers and content creators is essential to capture increasingly scattered audience attention and earn their consideration. For e-commerce, accurate product descriptions, images, and general product information must be consistently delivered to every retailer where products are sold. Additionally, all web content needs to be curated and optimized for Google SEO—and these are just some examples of what’s necessary for a robust digital strategy that drives business results.

In this scenario, where content truly is king, companies and creative/media agencies need more specialized teams with solid judgment, significantly increasing operational costs. This makes it harder for small and medium-sized companies to compete fairly.

The digital revolution has increased the complexity of content, and that's exactly where generative AI comes into play, leveling the playing field for companies, startups, and entrepreneurs alike. Small teams can now double their productivity by having an AI help with content structure, curation, and auditing.

However, not everything that glitters is gold. Generative AI should be approached carefully, as it's prone to "hallucinations" or inaccurate outputs when asked about something it doesn't have sufficient information for. Additionally, content created solely by these tools can often be easily recognized by Google and other platforms, potentially leading to penalties or lower rankings if there’s no human creativity involved.

With this in mind, generative AI should be wisely used by teams to prevent misuse and encourage adoption in areas such as:

• Generating drafts, storyboards, or general prototypes of ideas, speeding up the initial ideation, presentation, and approval processes.
• Brainstorming to figure out how to approach a topic—for example, gaining insights for brand campaigns or creating general structures for presentations.
• Structuring SEO-focused texts, later fully developed by human teams.
• Quickly and safely developing code for initial digital structures and solutions.
• Creating initial graphic assets (images) that can be refined later to achieve key visuals or brand elements.

These are healthy examples of how generative AI can boost tasks that ultimately require human refinement. To illustrate this point, this website, and even this article, benefited from AI by helping generate ideas, code, sources, and more.

Conclusión

I firmly believe it's possible for companies and their teams to responsibly use generative AI. However, clear governance and effective policies must be established to minimize risks and maximize results. Not using AI models now, both in your company and professional life, is a big mistake that can undermine your competitiveness and your ability to scale your efforts.

I recommend reading this article where I discuss an option allowing anyone to run generative AI models locally on their own computers, eliminating the privacy concerns that worry many businesses. Additionally, it’s worth mentioning that OpenAI (the company behind ChatGPT), as well as other providers, offers an API that anyone can safely and privately use, ensuring your data won’t be reused for further training of their models.

If you've made it this far and took the time to read this whole article, I congratulate and thank you. I encourage you to experiment with available artificial intelligence options and become an early adopter of these technologies—your life and career will surely thank you for the invested time.