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, in the most recent case, naturally conversing with users through a conversational interface. However, public perception of AI does not always align with its technical definition, creating a growing gap between understanding and reality.

To clarify what AI truly entails, it is essential to distinguish between two key aspects:

• AI Development/Research: This is where fundamental work happens. Data scientists, researchers, and specialized teams focus on building the core models that drive AI. Their efforts include publishing research papers, designing architectures, and advancing theoretical frameworks that enable machine intelligence. This process often requires years of study, experimentation, and iteration to push the boundaries of what machines can achieve.
• AI Integration: Once the foundational models are established, another group comes into play. Developers, product teams, and businesses work to integrate these AI models into applications or products via an API. Although this role may seem less scientific, it is crucial for translating cutting-edge advancements into practical tools that improve people's lives.

Understanding the distinction between these two areas highlights the gap between AI research and the tools users interact with daily. This gap is often the source of misunderstandings about what truly constitutes AI.

User perception

For the average user, AI is any technology that simplifies their life and seems intelligent in some way. It does not 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 functions from their perspective. If it appears capable of doing something smart, users quickly label it as AI.

This perception, although not always accurate, significantly impacts how technology is marketed and understood. Many users do not differentiate between technologies built from scratch and those leveraging pre-existing tools. For them, the most important factor is the experience of using something that appears intelligent.

For years, machine learning and AI have powered features that may not seem intelligent but are fundamental to AI-driven software. One example is the iOS Photos app, which, even before ChatGPT, allowed users to search for photos using descriptions. Another example is recommendation algorithms like those of YouTube or TikTok, which, while not directly interacting with users, are deeply integrated and enhance user experience.

So, when does a technology feel intelligent enough for users to call it AI? My assumption is that technology is perceived as intelligent when it enables constant interaction. The defining example was the conversational interface, where, through input and output messages, users felt like they were naturally conversing with an intelligent—yet artificial—system.

Business perception

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

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

At the same time, companies must balance pragmatism with innovation. Using technologies that are a generation behind is not inherently negative if they meet 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 provide convenience.

Ultimately, bridging the gap between technological progress and user expectations is essential. By meeting users at their level—even if their understanding of AI is imperfect—companies can drive adoption, enhance satisfaction, and strengthen 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.