When it comes to AI, are you looking in the right place?
Companies across many industries are announcing artificial intelligence strategies with enthusiasm, urgency and, often, little reflection. The desire to appear modern, competitive and “up to date with the future” has led to rushed AI adoption, driven by market trends and pressure to innovate.
The rhetoric is strong. The presentations look beautiful. In practice, however, what we often see is a superficial movement that looks straight at the center of what shines, while ignoring its surroundings.
When you want to observe an exoplanet — or any structure around a very bright star — you do not point a telescope and “aim at the middle.” That does not work.
The star’s light is so intense that it obscures everything around it. The elegant solution is an instrument called a coronagraph. It blocks the strongest light at the center of the image so the telescope can observe what lies around it — the edges.
And it is precisely there, where the light is subtler, that the most important discoveries happen: entire planets, dust disks and complete systems that were invisible in the main glare.
This is exactly the kind of approach missing from many corporate AI initiatives. Here, the coronagraph is a metaphor for the ability to filter excessive hype, guard against superficial fascination and focus on what must actually be built for AI to work well: reliable data, skilled teams, adjusted processes, clear objectives and, above all, a real understanding of where this technology can add value.
Without this, AI quickly becomes an ornament. A demonstration project, a proof of concept disconnected from reality, an initiative that generates headlines but not change.
The greatest risk is not adopting AI too early or too late, but adopting it without a strategy — or worse, as an anxious response to what competitors are doing.
Artificial intelligence does not replace the need for clarity and preparation. It does not solve poorly defined problems, correct inconsistent data or replace human capabilities that do not yet exist within the organization. It enhances. It amplifies. Integrating it responsibly requires maturity.
What do we have today? Where are the bottlenecks? Which questions are we trying to answer with AI? Is our culture ready to handle automated decisions? Do we trust the data? Do teams understand what the models are saying? Is anyone taking responsibility for the results?
These questions do not shine as brightly as the promise of total automation. But they define the success — or failure — of any AI journey.
Ultimately, perhaps the most important question every organization needs to ask itself now is precisely this: are we looking in the right place?
📌 Originally published in Época Negócios