Artificial Intelligence is already among the major forces transforming business on a global scale. According to the World Economic Forum’s Future of Jobs Report 2025, 86% of employers expect advances in AI and information processing to transform their organizations before 2030. One area where impact is most significant is the financial sector.
Real impact on the business
For this reason, Excelia identifies in its Practical Guide for Implementing AI in the Financial Area the seven key steps that companies should follow before investing in AI implementations in the financial area in a safe, scalable way and with real business impact:
- Start with low-risk internal use cases: The first step should be to identify internal processes where AI can add value with controlled risk. Automating reporting, analyzing deviations, generating financial summaries, classifying documents, or preparing presentations for the committee can be good starting points.
- Define clear autonomy limits: Not all financial tasks should have the same level of automation. AI can generate a first draft of a financial report or detect a relevant deviation, but final validations and decisions with financial impact should remain in the hands of the responsible team.
- Use role-based access control and full traceability: Finance deals with highly sensitive information, so any AI solution must respect permissions, roles, and access levels. Additionally, every query, recommendation, modification, or automation should be logged to facilitate audits and prevent AI from becoming a black box.
- Maintain human oversight in sensitive decisions: AI can help analyze, prioritize, summarize, detect errors, or recommend actions, but relevant financial decisions should remain under human supervision. This applies to payment approvals, accounting adjustments, official forecasts, external reporting, financing decisions, credit risk, or regulatory compliance.
- Choose the most suitable technology for each use case: Before deploying a tool, it’s wise to assess whether the need requires automation, predictive analytics, generative AI, assistants, agents, or capabilities already integrated into existing platforms. The decision should be based on criteria such as integration, security, traceability, scalability, and real capability to solve a specific problem.
- Prepare data, APIs, architecture, and organizational culture before scaling: Financial AI directly depends on data quality. If information is incomplete, duplicated, poorly classified, or dispersed across different systems, results will not be reliable. Before scaling, it is necessary to review data sources, integrations, permissions, governance rules, and to prepare teams to interpret and correctly use the technology.
- Avoid letting informal automation grow 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 prevent duplicates, errors, unauthorized access, or lack of traceability, Finance must work with clear ownership, validation criteria, security policies, and ongoing impact monitoring.
“Applying AI in Finance is not about automating for automation’s sake, but about identifying where it can generate real impact: improving forecasting, speeding up close processes, detecting deviations, avoiding human errors, strengthening controls, and freeing the finance team to focus on higher-value tasks,” says Antonio Cerdán, Hyperautomation Managing Director at Excelia, who adds: “The financial area handles highly sensitive information, so any project must move forward with clear criteria for security, traceability, human oversight, and data governance. The key is to start with concrete use cases, measure results, and scale progressively.”
