How Artificial Intelligence AI and Digital Maturity Can Transform Corporate Financial Management

Finance is the area responsible for ensuring compliance while also supporting strategic business decisions, which increasingly requires efficiency and accuracy in delivering results. In this context, the sector has emerged as a leader in technological transformation in Brazil, with 59.4% of companies prioritizing investments in this area. According to the Brazil Digital Transformation Index (ITDBr), developed by PwC Brazil in partnership with Fundação Dom Cabral (FDC), finance is also the industry with the highest level of digital maturity.

This movement is driven by the growing adoption of technologies such as cloud-based ERP systems,Artificial Intelligence (AI), and automation, which provide direct solutions to the structural challenges faced by finance departments, where the impacts of inefficiency are often most evident. As a result, the profile of finance professionals is changing, becoming increasingly analytical as repetitive tasks are automated.

The financial close process, for example, is evolving into a continuous operation, with more reliable data available in real time. This significantly changes decision-making dynamics and increases the need for digital maturity to effectively understand and operate these tools on a daily basis.

ERP Systems and Financial Data

As part of the digital maturity journey, many companies are adopting ERP systems that connect core business processes, unify financial management with the supply chain, and provide integration, scalability, and real-time access to information. Unlike legacy systems, ERP solutions create a single source of data, share a common data set across business units, and use real time integration to give teams an accurate picture of operations while offering greater security and flexibility to support business growth.

In practice, these systems enable more agile and reliable financial management. They also add business intelligence and analytics tools for data analysis, help teams generate reports, and provide immediate visibility into financial data. When combined with AI, they create a dynamic data foundation on which predictive models can be applied. Machine learning and predictive analytics can use historical data alongside current inputs to support stronger financial planning and strategic decisions. This allows organizations to identify risks and take proactive action, ultimately transforming the quality of financial management. In 2025, 92% of CFOs plan to boost sustainability spending using ERP systems to strengthen regulatory compliance and broader planning.

The Combination of AI, Machine Learning, and Automation

Today, AI consistently addresses day-to-day operational challenges through anomaly detection, automated reconciliation of financial transactions, and fraud detection across large volumes of data. As a result, the quality of insights improves significantly, as decision-making can incorporate scenarios and patterns that would be virtually impossible to identify manually, with real time data processing and real time analytics generating real time insights for finance teams reviewing financial data.

AI also aids in fraud detection in finance by analyzing suspicious usage patterns as they emerge.

When artificial intelligence is combined with process automation in areas characterized by high volumes of work, repetitive accounting tasks, and susceptibility to human error, efficiency and productivity gains increase substantially, especially in accounts payable, where manual data entry and other time-consuming routines are handled by automated tools and automation tools. By 2030, the trend is for many of these activities to be largely automated, with human intervention focused primarily on exceptions and analysis.

Automated reconciliation can reduce errors by over 70%, improve accuracy, and complete tasks in minutes instead of days.

Many finance teams still take six or more business days to close, which is why automation matters. In addition, this combination enhances traceability and transparency through detailed audit trails and stronger regulatory compliance—two fundamental pillars of corporate governance—while also requiring responsible data usage, clear criteria, and bias mitigation. Within the ESG context, this translates into more auditable processes, more consistent decisions, and greater reliability of the information reported to the market. Bank feeds support faster matching, while data imports help keep records current. Sales data can also strengthen cash management, and payment reminders help maintain timely collections. For this reason, according to the ITDBr, the financial sector has become a benchmark for digital transformation.

Challenges to Achieving Digital Maturity

Despite significant progress, many companies still face challenges in achieving digital maturity. However, the main barriers are not technological but structural. Many organizations attempt to implement new solutions on top of disorganized data foundations, limiting the results they can achieve while also hindering real time integration and keeping teams reliant on batch processing instead of real-time data flows. Without governance, standardization, and a strategic vision, it is impossible to unlock the full potential of these technologies. Specialized tools and broader tech stack choices also shape how data sources connect, and platforms such as Apache Kafka and Amazon Kinesis support this work while real-time data helps speed fraud detection in financial transactions.

Therefore, the first step is to structure existing processes so that technology can be applied as a layer that enhances an already organized foundation, especially by reducing manual data entry and improving data entry across complex processes. Stronger process design also improves invoice and accounts payable automation; in 2024, 60% of invoices were manually entered into ERP systems. From there, companies can also adopt strategic outsourcing models, such as Business Transformation Outsourcing (BTO), which maximize the use of technological solutions, structure processes, and integrate tools. This combination helps reduce operational costs and achieve a level of digital maturity that many organizations would struggle to develop internally.

Furthermore, it is essential to adopt a gradual implementation approach, with clearly defined priorities and performance metrics across areas such as human resources and with adequate financial support for different business units, ensuring that governance and compliance are embedded from the very beginning. Better-integrated ERP environments also strengthen the supply chain, support AI optimization of inventory management in manufacturing, and improve customer satisfaction. After all, digital maturity in finance is directly linked to competitiveness, and companies with well-structured processes can respond more quickly to market changes and make decisions with greater confidence.

Renato Halt is Global President of Business Transformation Outsourcing at H&CO, a multinational company specializing in global consulting, technology, and professional outsourcing services.