In the age of artificial intelligence, many companies—especially in the financial sector—face a strategic dilemma: should they build their own AI solutions or integrate established external technologies? The rise of “fatta in casa” AI (homegrown AI) is driven by understandable concerns such as data security and dependency on tech giants. However, this sense of control often hides deeper risks: fragmented systems, lack of scalability, and missed opportunities for shared learning. In this article, we explore how the illusion of autonomy can lead to inefficient and chaotic systems, disconnected from the broader innovation ecosystem. The real challenge is to design AI from the ground up with a strategic, professional, and unified vision—not a fragmented or improvised one.
From the Illusion of Control… to the Awareness of Its Limits
During the Renaissance, the abandonment of alchemy in favor of the empirical method gave rise to a powerful illusion: that nearly everything could be controlled. Concepts like zero, algebra, the printing press, and the microscope symbolized an era of boundless faith in human knowledge. There was even talk of eradicating disease or conquering death.
Today, we are living through a similar turning point. Artificial intelligence offers tools with transformative potential, but it also brings an age-old temptation: to believe that everything is within the individual’s reach. Unfiltered access to information sells a false sense of autonomy.
Anyone can design their own diet, interpret their health, “cure” their ailments, or construct their personal belief system. Everyone feels like a creator. And while this trend empowers people, it also blurs the boundaries of responsible knowledge.
In-House AI in Companies
Much like in the Renaissance, when an unexpected plague dismantled the illusion of control, today we may be haunted by different specters.
One of them is a similar unconsciousness: blurring the lines between professional expertise and shallow, amateur knowledge.
Fatta in casa AI: A Solution or a New Threat?
In the corporate world—especially in the financial sector—many companies are choosing to develop their own artificial intelligence solutions.
Why? The initial motivation to adopt AI is the fear of being left behind in an unstoppable transformation. But the move toward “fatta in casa” (homemade) solutions stems from two deeper fears: the risk of losing control over sensitive data and the dependence on powerful external tech providers.
For these reasons, large enterprises—particularly in finance—are building their own AI systems out of fear of losing data, reputation, or independence.
While this reaction may seem reasonable in the short term, it harbors deeper and longer-lasting risks.
Silos and Systemic Chaos
Many companies create tools that work—relatively and individually—but in an entropic way. What does this mean? That they may solve a particular need, but fail to address the organization’s broader goals. Worse still, they often hinder the overall functionality of the system.
For example: a financial institution might develop an investment bot that contradicts another solution within the same organization, or a personal assistant that recommends the opposite.
This might seem like a minor inconvenience, but it is critical. To draw an analogy: it’s like a patient who sees a doctor for a cold and is prescribed a medication contraindicated for hypertension. The doctor simply didn’t know the patient had high blood pressure. The drug may relieve the cold but cause a more serious issue: hypertension and its consequences.
Compromised Scalability
What works on a small scale may collapse when scaled up, leading to higher costs or inefficient processes.
Furthermore, these implementations often generate functional problems. That is, something helpful in a limited scope can cause chaos—entropy—when applied at scale. This affects durability, cost, and efficiency.
In short, “fatta in casa” AI experiments are rarely a sound solution.
Lack of Ecosystem
Another key issue is that in-house AI misses out on external learning. These internal systems operate in isolated microclimates, while specialists benefit from shared, global ecosystems.
Each organization creates its own microclimate. Even if it achieves momentary, partial success, the homemade solution cannot evolve by learning from others. Since everything is either evolving or regressing—never static—any system must understand future models. This is an advantage that dedicated AI firms possess.
The challenge is not just hiring AI specialists for a bank or credit institution. It’s about designing a new system from the ground up—one that retains strategic focus while integrating AI tailored to the business, based on a unified, systematic, and coherent framework that avoids entropy, chaos, and fragmentation.
The Case of N5
One firm leading this shift is N5, which specializes in AI for the banking and financial industry. As its CEO explains, N5’s teams have focused on building and delivering an organized network of artificial intelligences that work in harmony within banking systems. This reflects a business-aligned, sector-specific strategy.
They’ve integrated real, useful AI into their platform to “transform banking into a fluid, predictive, and personalized experience”—far beyond vague futuristic promises.
From Handmade to Professional
In a few months, the same industries that created internal AI divisions will begin to feel the pain points described above. They’ll eventually move from the handmade to the professional approach.
Ultimately, building all AI from scratch leads to visible limitations. Those who take this path will soon realize that the safest, most efficient, and scalable solution lies in integrating systems developed by specialized talent—with a unified and long-term vision.
