AI in the back office for cost allocation and data processing
Executive Summary
The Squadra administrative area is responsible for allocating expenses among projects, cost centers, and company areas, an activity that involves reading documents, cross-referencing information between different databases, and applying specific business rules for each allocation model.
With the increase in document volume and the complexity of these models, the cost and expense allocation process began to demand more and more time from the team, making the operation intensive and dependent on constant manual checks.
Faced with this scenario, using Google AI Studio and Prompt Engineering techniques, Squadra developed a Generative Artificial Intelligence agent to support the back office. It is capable of interpreting documents, cross-referencing information between different bases, and structuring the results for analysts' review, with the objective of automating repetitive activities, standardizing the application of business rules, and increasing team productivity.
The AI agent was initially developed to work on a specific allocation model and resulted in an approximate 70% reduction in operational time, creating a reusable technological base for the evolution of automation in other administrative processes.
The Challenge
The administrative area performed expense allocation through a process with strong dependence on manual intervention: reading documents, cross-referencing information between different spreadsheets and databases, and applying allocation rules with specific criteria for each model. The increase in document volume and the complexity of the different allocation models limited the scalability of the process and intensified the demand for manual checks to avoid inconsistencies, highlighting the need for a solution capable of increasing productivity and standardizing the application of business rules.
The Solution
To address this challenge, Squadra developed an Artificial Intelligence agent to support the administrative expense allocation process. Using Google AI Studio and Prompt Engineering techniques, the solution was built to interpret documents, cross-reference information between different databases, and automatically apply the business rules defined by the team.
The development started with mapping the existing process: the steps of each allocation model, the information sources used, the validation criteria applied by the administrative team, and the points of greatest operational effort, such as document analysis, spreadsheet cross-referencing, and manual checks, were identified. With the process mapped, the agent began to act as an operational accelerator: it reads and interprets PDF documents, identifies relevant information, compares the extracted data with employee lists and other databases used by the administrative area, and automatically applies the business rules defined for each process.
In the end, the AI delivers structured results for analysts' review, reducing the need for manual data entry, calculations, and validations.
Key deliverables
Mapping of business rules
Identification of the steps, necessary information, and criteria used by the administrative team to validate each allocation model.
AI agent for document reading and interpretation
Automated reading of PDF documents and identification of relevant information for allocation.
Automatic data cross-referencing
Comparison between information extracted from documents and the databases used by the administrative area, with automatic application of business rules.
Structuring of results for review
Organization of processed information in a format ready for validation by the team, reducing the need for manual data entry and calculations.
Results
The experimentation demonstrated the potential of Artificial Intelligence to support administrative processes traditionally performed manually. Acting as an operational accelerator, the agent reduced the time dedicated to administrative processes traditionally performed manually, bringing more agility, standardization, and reliability to the allocation process.
Operational efficiency
The time to execute allocations was reduced by approximately 70%, from about one day to a few hours.
Process standardization and reliability
The automatic application of business rules reduced the dependence on manual checks and variations in execution between different allocations.
Foundation for automation expansion
The developed agent can be used to evolve the automation of other administrative processes.
Metric | Result |
Operational time reduction | 70% |
Allocation execution time | From approximately 1 day to a few hours |
Manual effort reduction | Repetitive activities automated |
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