Intelligent Ombudsman Management Platform: transforming operational and historical data into actionable insights

The Problem

The Ombudsperson's Office receives and processes thousands of citizen submissions each year, including complaints, requests, suggestions, compliments, and reports of irregularities. Managing this volume of information requires continuous monitoring of workflows, response deadlines, case assignments, and recurring citizen-raised issues.

Much of this work has traditionally relied on manual analysis, fragmented information sources, and reactive monitoring processes. Managers and analysts often need to spend significant time consolidating data from different systems, identifying trends, monitoring deadlines, and determining the most appropriate unit to handle each case.

As the volume and complexity of citizen submissions increased, it became more difficult to identify emerging issues, anticipate operational bottlenecks, prioritise cases effectively, and allocate resources based on evidence. Valuable management insights often remained embedded within large volumes of operational data, limiting their use in supporting timely decision-making and service improvements.

The challenge we sought to address was how to transform large volumes of historical and operational data into actionable intelligence to support more efficient case management, strengthen compliance with response deadlines, improve resource allocation, and enable a more proactive, data-driven approach to public service delivery.

The Solution

To address these challenges, the Ministry is developing an AI-enabled Ombudsman Management Platform that transforms operational and historical data into actionable insights for both analysts and managers.

Once fully implemented, the platform will use Artificial Intelligence and data analytics techniques to support key stages of the ombudsman workflow. Planned functionalities include automated case classification and routing, identification of response deadline risks, detection of emerging themes and recurring issues, monitoring of operational performance, and the generation of recommendations and alerts to support managerial decision-making.

We chose this approach because the Ombudsperson's Office generates a large volume of structured and unstructured data containing valuable information about citizen needs, service performance, and operational risks. However, extracting meaningful insights from this information through manual processes alone has become increasingly difficult and resource-intensive.

By combining Artificial Intelligence, data integration, and performance monitoring capabilities into a single platform, we aim to shift from a predominantly reactive management model to a more proactive, evidence-based approach. The objective is not only to improve operational efficiency, but also to strengthen the organisation's ability to anticipate challenges, prioritise actions, allocate resources more effectively, and continuously improve public service delivery.

The project is currently under development and costs approximately $1,500/month. Its implementation is also an opportunity to explore the practical application of AI-driven management tools in the public sector and to identify lessons for future initiatives.

The Impact

1. Consolidated previously dispersed data into an integrated evidence base

As the platform is still under development, its full impact has not yet been measured. However, the project has already led to significant changes in the Ombudsperson's Office's approach to data management and operational decision-making. The development process has consolidated historical and operational data previously dispersed across multiple sources, laying the foundation for a more integrated and evidence-based management model. It has also helped identify opportunities to automate routine monitoring, improve visibility into operational risks, and enhance the ability to detect recurring issues and emerging trends in citizen submissions.

2. Encouraging a shift from reactive to proactive management

The project has encouraged a shift from reactive to proactive management practices. Rather than relying primarily on retrospective reports and manual analysis, the platform is designed to provide managers with timely insights, alerts, and recommendations to support faster, more informed decision-making.

3. Generated valuable organisational learning on AI-enabled management tools

Although the platform has not yet been fully implemented, the initiative has already generated valuable organisational learning regarding data governance, AI-enabled management tools, and the practical challenges of integrating analytics and Artificial Intelligence into public-sector workflows.

Lessons Learned

• Building around real operational needs improved relevance. One aspect that worked particularly well was the decision to build the platform around real operational needs identified by analysts and managers. The project benefited from the availability of historical data and from the close involvement of end users throughout the design process, helping ensure that the proposed functionalities address practical challenges faced in the daily management of citizen submissions.
• Integrating dispersed data revealed significant untapped value. Another positive outcome was the realisation that significant value can be generated by integrating data that was previously dispersed across multiple sources. The development process highlighted opportunities to improve visibility into workflows, operational risks, response deadlines, and emerging citizen concerns.
• Translating institutional knowledge into automated processes proved complex. One of the main challenges has been the complexity of translating institutional knowledge and business rules into automated processes and AI-supported functionalities. Data quality, standardisation, and system integration requirements also proved more demanding than initially anticipated. In addition, defining the appropriate balance between automation and human oversight required careful consideration throughout the project.
• Earlier investment in data governance and stakeholder involvement would accelerate development. If starting the project today, the team would invest earlier in data governance activities, process mapping, and user testing. A broader range of stakeholders would also be involved from the outset to help validate requirements and prioritise functionalities. These measures would likely accelerate development and facilitate the platform's future adoption across the organisation.

The project was developed within the regulatory framework of Brazil's Federal Executive Branch Public Ombudsman System, which is coordinated by the Office of the Federal Ombudsman-General, part of the Office of the Comptroller General of the Union (CGU). The Federal Ombudsman-General is responsible for establishing the guidelines, standards, and procedures that govern public ombudsman activities across the federal government. The initiative operates within the legal framework established by Brazil's Access to Information Law (Law No. 12,527/2011), the Code for the Protection and Defence of Public Service Users (Law No. 13,460/2017), and other regulations governing transparency, citizen participation, public accountability, and the handling of citizen feedback. The use of Artificial Intelligence was designed to support, rather than replace, human decision-making, ensuring compliance with principles of accountability, transparency, and responsible public-sector AI adoption.