Not the end of AI, but the end of poor adoption
In recent weeks, we shared a provocative message across METRICA’s channels: “ChatGPT is dead.”
This is not an announcement of the end of a technology, but a call to attention to a much deeper issue: how many companies are adopting Artificial Intelligence.
AI is already present in the corporate environment. However, in too many organizations its adoption has been fast, fragmented, and poorly governed. The result is not always greater efficiency or innovation, but rather higher risk, rising costs, and limited visibility.
Because the problem is not Artificial Intelligence.
The problem is how it is adopted.
The single‑model myth in corporate AI
One of the most common mistakes in enterprise AI adoption is the belief that one single model can serve every need.
While convenient, this idea is fundamentally flawed.
Today, it is increasingly common to see scenarios such as:
- Teams using different AI tools without coordination
- Strategic decisions based on a single, generic model
- Growing costs that are difficult to justify or measure
- Lack of control, traceability, and long‑term visibility
Every business task has different requirements. Cost, speed, accuracy, privacy, and level of control are not the same across all use cases. Forcing a single model to cover everything leads to rigid and unsustainable solutions.
A fragile decision with long‑term risks
Choosing a single AI tool may seem like a simple decision at first. In reality, it is a fragile one for several reasons:
- Technology evolves faster than any contract
- Rigidity limits innovation and adaptability
- Legal, security, and compliance risks often appear when it is already too late
In today’s environment, where regulation is advancing and AI usage is accelerating, these decisions can seriously compromise organizations.
Adopting AI is not about speed, it’s about doing it right
At METRICA, we believe in a simple principle: innovation is not about adopting faster, but adopting better.
Corporate AI should be designed as a system, not as an isolated tool. This requires:
- Governance and control from the outset
- Data security and privacy
- Traceability of usage and impact
- A long‑term strategic vision
Only then can Artificial Intelligence become a real and sustainable competitive advantage.
