For decades, technology has been managed around standardization: one ERP, one CRM, one major vendor, one closed ecosystem. This model worked because traditional software was predictable and changed very little.
Agentic AI Is Changing the Balance
Agentic AI, autonomous systems that act to achieve objectives by making decisions and executing tasks, is changing that balance because models evolve at great speed, differentiating themselves by function, cost, performance, and regulatory framework.
When a company locks itself into a single solution, it loses the flexibility to negotiate, correct course, or adapt, becoming dependent on external decisions it cannot control. Creating value now means preparing the organization to change when necessary while keeping the business running without turning every adjustment into disruption.
When AI Agents Become Part of Business Operations
This reality becomes even more evident when we recognize that AI agents are no longer simple support tools. They are systems that plan, execute, and make decisions within real business processes, from customer service to operations, from financial analysis to compliance.
When multiple agents from different vendors coexist without coordination, the challenge is no longer technological but organizational. Inconsistent decisions, misaligned information, and cascading errors multiply, often being detected only after they have already impacted the business.
The Critical Role of Orchestration
This is where a layer that is often underestimated comes into play: orchestration.
Orchestration is not simply about connecting APIs or automating workflows. It is about clearly defining who makes decisions, based on which information, within what limits, and under whose responsibility.
Without this design, Artificial Intelligence does not improve the way organizations operate. Instead, it creates confusion, generates hard-to-predict costs, and leaves accountability unclear.
AI Governance Has Become a Leadership Issue
This is also why AI governance is no longer an abstract debate about ethical principles. It has become an executive management issue.
When autonomous systems take actions on behalf of a company, responsibility does not disappear. It shifts to those in leadership positions. The impact is not theoretical; it is reflected in financial performance, legal risk, and corporate reputation.
Automating without human validation mechanisms means accepting decisions without fully taking ownership of their consequences.
Trust Requires Explainable Decisions
This is precisely where many organizations misunderstand what accountability really means. Exposing the internal reasoning of models adds little value to those responsible for validating decisions.
What truly matters is understanding why a decision was made, which data was used, which rules were applied, and who is accountable for its impact. Trust is built through traceable and explainable decisions, not through promises of access to the inner workings of systems whose complexity few people fully understand.
The Real Challenge Is Organizational, Not Technological
Ultimately, the greatest challenge is not technological. It lies in how companies organize themselves and make decisions. Efficiency is not about using the same tool everywhere. It is about ensuring that different tools work toward the same objectives under clear and consistent rules.
As long as the debate remains focused on finding the “best model,” the most important issue will continue to be overlooked. The decisive question is whether companies can maintain control over the decisions they automate or whether they are simply deploying technology without accepting responsibility for the outcomes.
This opinion article was written by Manuel Costa, Commercial Director at Glintt Next.
source: Diário de Notícias
