Treasury is the nervous system of companies, where liquidity, payments, banking relationships, risk management and financial forecasting converge. The article warns that the debate on Artificial Intelligence in this area should not be limited to automation, as AI tends to expose problems that already existed in organizations, revealing the maturity of processes, the quality of data and the capacity of teams.
Technology does not fix poorly designed processes, does not compensate for incomplete data, nor does it replace business knowledge. When an organization works with scattered information and overly manual tasks, AI will merely accelerate those weaknesses. In more prepared companies, however, AI can reduce operational friction, structure information and accelerate tasks such as bank statement analysis and Know Your Customer processes.
Treasury forecasting is pointed out as the most sensitive example, as an incorrect estimate can lead to incorrect financial decisions. The article argues that AI in this area cannot just be intelligent, but also explainable, indicating which data was considered and what information was left out.
The article also identifies a paradox: the operational tasks that AI tends to take over have been for years the school for junior profiles. Without these learning paths, organizations may create a future talent fragility. The conclusion is that the advantage will not be in organizations with more AI, but in those that first did the harder work of structuring processes, improving data and developing people.
