With the advent of agentic AI, this premise goes from being a prudent recommendation to a survival imperative. The risk is no longer limited to models that generate text, but to agents that execute actions on systems, customer databases, and supply chains. Herein lies a dangerous disconnect: according to the same study, only 13% of professionals consider their organization to be “very prepared” to manage these risks. This is an alarming statistic that reveals that the vast majority of companies are rushing into the AI race while operating in an unacceptable zone of vulnerability.
That is why I will never tire of repeating that disruptive advances, such as agentic AI, require that all evolution be grounded in governance. Governance is not understood as bureaucracy that slows down agility, but as the set of rules that define the limits, responsibilities, and necessary evidence: which use cases are approved, what data agents can work with, what the mandatory controls are, how automated decisions are supervised, and who is responsible when something goes wrong.
Within this complex landscape, the good news is that the market is beginning to mature in its reading of the situation. It is true that the use of AI in areas such as cybersecurity can alleviate operational burdens, but it also generates an inevitable implementation toll. IT teams must lead the deployment of AI solutions and the development of policies governing their use, with the goal of safe and responsible adoption, which requires time, resources, and vision.
