Manual processes continue to hinder control over business travel expenses

59% of organizations acknowledge that they have to correct manual errors before making financial decisions, while more than half still rely on manual tasks to prepare forecasts. A study by SAP Concur warns that poor data quality and a lack of integration between systems continue to compromise expense control, especially when it comes to business travel.

Companies continue to grapple with significant challenges stemming from the manual management of financial data. This is reflected in the latest global CFO Insights report by SAP Concur, according to which 59% of CEOs and CFOs acknowledge that they have to correct manual errors before using the data for analysis or decision-making.

The study highlights a reality that continues to hinder the financial efficiency of organizations: excessive reliance on manual tasks and a lack of integration between systems. In fact, 51% of financial leaders admit that they still combine manual processes with technological tools to make financial forecasts, while only 4% claim to have completely eliminated this type of task.

This situation has a direct impact on particularly sensitive areas such as corporate travel management, which involves multiple platforms, suppliers, and information sources. This fragmentation requires constant data review to avoid errors, budget deviations, and internal control issues.

Among the main causes of this reliance on manual processes are difficulties integrating data across systems (55%), the need to use custom models (49%), the limitations of current financial forecasting tools (45%), and the persistence of legacy processes within companies (41%).

João Carvalho, director of SAP Finance & Spend Management for Southern Europe, asserts that ?the real challenge is no longer accessing the technology, but having reliable data on which to build.? As he explains, organizations that manage to consolidate a solid data foundation will be better prepared to optimize investments and improve spend control.

The report also highlights a lack of confidence in the quality of data. 100% of the CEOs and financial executives surveyed state that they need to review or correct information before using it in decision-making. Among the main problems identified are the lack of consistent criteria across the organization (62%), a weak data culture within teams (49%), and the absence of clear accountability for data quality (40%).

The consequences are also reflected in financial forecasts. Four out of ten financial managers consider inaccurate forecasts to be one of their organizations? main internal challenges. Furthermore, only 31% of CEOs and general managers fully trust the reliability of their company?s cost forecasts.

At the same time, artificial intelligence continues to gain ground in finance departments, although its development is limited precisely by the poor quality of data. Fifty-three percent of respondents believe that robust databases increase the return on investment in AI, but that same percentage identifies data quality and integration issues as the main obstacle to achieving tangible results.

Currently, AI is used primarily for revenue forecasting (51%), risk analysis (45%), demand forecasting (43%), and scenario planning (41%). However, technology leaders warn that the lack of expertise in data analysis, governance, and artificial intelligence within finance teams continues to hinder its effective implementation.