Agustín Arias

Artificial intelligence, in its purest form, refers to a machine's ability to perform tasks that typically require human intelligence. These tasks include learning from data, making decisions, solving complex problems, and, at times, interacting with humans in a way that feels natural. However, the general public's perception of AI doesn't always align with its technical definition, creating a growing gap between understanding and reality.

To clarify what AI truly entails, it's essential to distinguish between two critical aspects:

• AI Development: This is where the foundational work takes place. Data scientists, researchers, and specialized teams focus on building the core models that drive AI. Their efforts involve publishing research papers, designing innovative architectures, and advancing theoretical frameworks that make machine intelligence possible. This process often demands years of study, experimentation, and iteration to push the boundaries of what machines can achieve.
• AI Integration: Once the foundational models are established, another set of players comes into the picture. Developers, product teams, and businesses work to integrate these AI capabilities into real-world applications. This may involve using APIs from pre-existing models, embedding AI into workflows, or creating user-friendly interfaces that make advanced technologies accessible to a broader audience. While this role may seem less "scientific," it is vital for translating cutting-edge advancements into practical tools that improve people's lives.

Understanding the distinction between these two areas highlights the gap between the research driving AI and the tools most users interact with daily. This gap is often where misunderstandings about what constitutes AI arise.

User perception

For the average user, AI is any technology that simplifies their life and appears "intelligent" in some way. It doesn't matter whether the underlying system is a sophisticated neural network or a simple program with pre-programmed responses. What matters is how the technology feels and performs from their perspective. If it seems capable of doing something smart, users are quick to label it as AI.

This perception, while not always accurate, significantly impacts how technology is marketed and understood. Many users don't differentiate between technologies built from scratch and those leveraging pre-existing tools. For them, the result is what matters most—does it work effectively, and does it seem innovative?

Companies perception

From a business perspective, user perception is paramount. If users believe a product or service involves AI, it becomes the company's responsibility to align with that expectation. This doesn't mean every product must feature groundbreaking machine learning capabilities. Instead, companies should prioritize delivering results that align with user expectations, even if the technology behind the scenes isn't "pure AI."

This involves more than just technological development; it requires effective communication and a thorough understanding of user behavior. If users perceive a product as "intelligent" or "smart," it fosters trust and strengthens their connection to the brand. Companies can meet these perceptions by adopting existing AI models or integrating tools that simulate intelligence in ways that feel intuitive and seamless.

At the same time, businesses must balance pragmatism with innovation. Using technologies that are "a generation behind" isn't inherently negative if they fulfill user needs and create positive experiences. The true value of AI often lies not in its technical sophistication but in its ability to solve real-world problems and deliver convenience.

Ultimately, bridging the gap between technological progress and user expectations is essential. By meeting users where they are—even if their understanding of AI is imperfect—companies can drive adoption, enhance satisfaction, and solidify their competitive position. Pragmatism, adaptability, and a user-first approach are the keys to thriving in a world where "AI" is as much about perception as it is about capability.