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Artificial intelligence is already part of companies’ daily routines. However, in 2026, the discussion is beginning to change. The focus is shifting from what a model can answer to what AI systems can execute within real-world processes.
According to the AI Index Report 2026, from Stanford University, 88% of the organizations surveyed were already using artificial intelligence in some capacity in 2025. The use of agents, however, was still much lower across most business functions. This gap between adoption and scale helps explain one of the main artificial intelligence trends for the second half of 2026: moving AI agents from testing into operations.
AI is moving from answering to executing
The first wave of generative AI gained ground as a productivity tool. AI agents expand this role: instead of simply responding to a command, they can interpret an objective, retrieve information, activate tools, and execute different stages of a task.
This progress, however, does not mean unrestricted autonomy. The AI Index 2026 shows that agents reached a 66.3% success rate on OSWorld, a benchmark that evaluates tasks in computer environments, but they still fail in approximately one out of every three attempts. The technology has evolved, but supervision and process design remain essential.
Agent orchestration is gaining ground
The next step does not depend only on more capable agents, but on how they are coordinated. Agent orchestration makes it possible to distribute stages of a workflow across specialized systems. One agent can interpret a request, another can retrieve data, and a third can execute an action, all within defined rules.
Research published by McKinsey in August 2026 shows that 40% of respondents from large organizations with annual revenue above US$1 billion say they are scaling AI agents, compared with 27% in the previous survey.
This does not mean that every automation needs to become agentic. Predictable processes continue to be well served by traditional technologies. The value of agents tends to be greater when tasks require contextual interpretation, the combination of multiple sources, and decisions across several stages.
In the financial sector, the value will be in processes
For financial services and payments, this evolution has practical implications. These are environments with high volumes of data, real-time operations, and demanding security and control requirements.
AI agents can support activities such as document analysis, customer service, reconciliation, anomaly investigation, software development, and processes related to fraud prevention and compliance.
But the outcome does not depend on the model alone. To execute tasks reliably, AI needs access to accurate data, integration with existing systems, and clearly defined permissions. That is why data architecture, APIs and integration are becoming part of the artificial intelligence strategy.
More autonomy requires more governance
The more AI participates in execution, the greater the need to understand how decisions are made.
The AI Index 2026 recorded 362 incidents related to artificial intelligence in 2025, compared with 233 in 2024. At the same time, AI-specific governance roles grew by 17%.
For companies that operate critical processes, governance, security, and observability can no longer be treated as issues to address after implementation. It is necessary to define autonomy limits, control access, track actions, and establish when a decision requires human intervention.
The advantage will not come from simply adopting AI
The second half of 2026 should make the difference between using artificial intelligence and embedding it into operations increasingly clear.
Standalone tools can increase productivity. Applying agents to end-to-end processes, however, requires structured data, integration, security, governance, and business knowledge.
The trend, therefore, is not simply the arrival of new tools. It is the evolution of AI from an interface to a part of how companies execute their operations. The differentiator will be less about adopting every new development and more about identifying where this technology truly creates value.
How Evertec can support this evolution
AI adoption rarely runs into a lack of technology, but rather the difficulty of identifying where it truly creates value for the business. Evertec works at this point: helping financial institutions prioritize use cases, adapt data and infrastructure, and move pilot initiatives into operations with predictability.
Evertec is present in more than 26 countries, with a comprehensive portfolio for the financial sector. Learn more about our solutions and speak with one of our specialists!
