Artificial intelligence is already among the main forces of business transformation on a global scale. According to the World Economic Forum’s Future of Jobs Report 2025, 86% of employers anticipate that advances in AI and information processing will transform their organizations before 2030. One of the areas where it is having the most impact is the financial area. For this reason, Excelia, a Spanish consulting, technology and professional services firm, identifies in its Practical Guide to Implement AI in the Financial Area, which are the 7 key steps that companies must follow before investing in the implementation of AI elements in the financial area in a safe, scalable way and with real impact on the business:
- Start with low-risk internal cases: The first step should be to identify internal processes where AI can add value with controlled risk. Automating reporting, analyzing variances, generating financial summaries, classifying documents or preparing presentations for committees can be good starting points.
- Define clear limits of autonomy: Not all financial tasks need to have the same level of automation. AI can generate a first draft of the financial report or detect a relevant deviation, but final validations and decisions with economic impact must remain in the hands of the responsible team.
- Use role access control and full traceability: Finance works with especially sensitive information, so any AI solution must respect permissions, roles and access levels. In addition, each query, recommendation, modification or automation must be recorded to facilitate audits and prevent AI from becoming a black box.
- Maintain human supervision in sensitive decisions: AI can help analyze, prioritize, summarize, detect errors or recommend actions, but relevant financial decisions must maintain human supervision. This applies to payment approvals, accounting adjustments, official forecast, external reporting, financing decisions, credit risk or regulatory compliance.
- Select the most appropriate technology for each use case: Before implementing a tool, it is worth evaluating whether the need requires automation, predictive analytics, generative AI, assistants, agents or capabilities already integrated into existing platforms. The decision must be based on criteria such as integration, security, traceability, scalability and real capacity to solve a specific problem.
- Prepare data, APIs, organizational architecture and culture before scaling: Financial AI depends directly on the quality of the data. If the information is incomplete, duplicated, misclassified, or dispersed across different systems, the results will not be reliable. Before scaling, you need to review data sources, integrations, permissions, governance rules, and prepare teams to correctly interpret and use the technology.
- Prevent informal automation from growing unchecked: One of the most common risks is that different teams start creating automations, macros, assistants or AI models separately, without a common framework. To avoid duplication, errors, improper access or lack of traceability, Finance must work with clear responsible parties, validation criteria, security policies and impact monitoring.
The financial area works with sensitive information, which requires clear criteria for security, traceability, human supervision and data governance to be followed.
“Applying AI in Finance is not about automating for the sake of automating, but about identifying where it can generate a real impact: improving forecasting, accelerating closures, detecting deviations, avoiding human errors, reinforcing control and freeing up time of the financial team for higher value tasks,” says Antonio Cerdán, Hyperautomation Managing Director of Excelia, who adds: “The financial area works with especially sensitive information, so any project must advance with clear criteria of security, traceability, human supervision and data governance. The key is to start with specific cases, measure results and scale progressively.”
The company Excelia accompanies organizations in the implementation of artificial intelligence applied to the financial area through the identification of use cases, strategy and data governance, intelligent process automation, advanced analytics, predictive models, generative AI, intelligent assistants, AI security, training and development of customized solutions.
