Drafting Reporting from Structured Data

Note: This document describes a Proof of Concept (PoC) for AI-enhanced solutions in various application domains. All specific implementation details, technical configurations, and organizational references have been generalized for public use. 📖 Technical terms are explained in our glossary.

1. Short Introduction & Background

Development organizations and humanitarian agencies require regular impact reports outlining the reach and effectiveness of their activities, specifically detailing the number of beneficiaries served and the types of assistance provided. Data is typically collected continuously within organizational systems and subsequently transformed into narrative reports based on quantitative data extracted from these systems.

2. Use Case Overview

Problem Statement

The current process for generating impact reports for donors, which relies on extensive quantitative data from organizational systems managed through Excel spreadsheets, is a highly time-consuming and labor-intensive endeavor for humanitarian staff.

Objective

This Proof of Concept (PoC) aims to develop and validate an AI-driven prototype capable of generating initial drafts of narrative impact reports. The primary goal is to significantly reduce the manual effort and time currently required for report creation.

3. Scope of the POC

In-Scope

The POC will focus on demonstrating the AI's capability to generate report drafts based on provided data and guidelines. Key functionalities include:

• Ingestion of indicator data from Excel/CSV spreadsheets.
• Adherence of AI-generated text to specified guidelines:
  • Prioritization of reporting higher numbers of key indicators over lower numbers.
  • Ensuring all key indicators are reported.
  • Breakdown of reporting by gender, location, and thematic categories.
• Generation of report drafts according to the structure of the sample narrative report, specifically covering the ICLA (Information, Counselling, and Legal Assistance) section.
• Prioritization of the standard donor report type, among the three report types generated by the NGO's system.
• Utilization of Excel spreadsheets and narrative context information as inputs.
• Implementation of a multi-step process:
  1. Pre-processing of spreadsheets (e.g., Excel-to-JSON conversion) to consolidate data and optimize for AI processing, thereby reducing input context length.
  2. Generation of report sections detailing:
     • Number of individuals with specific traits (age group, disability, displacement status, sex, nationality).
     • Counseling services received (e.g., Legal Identity, Employment Law and Procedures).
     • Association with field office and area office.
  3. Integration of provided narrative context information to enrich the quantitative data.

Out-of-Scope

• Full automation of the end-to-end report generation and submission process.
• Direct integration with the NGO's live system or other production environments.
• Generation of report types other than the prioritized standard donor report, unless explicitly agreed upon during development.

4. Approach & Methodology

Methodology

The PoC will be developed using a rapid prototyping approach, leveraging AI development platforms to iteratively build and refine the AI model. This allows for quick development cycles and incorporation of feedback from domain experts.

Metrics

The success of the POC will be evaluated based on:

• Qualitative Assessment: Evaluation of the coherence, accuracy, and relevance of AI-generated report drafts by domain experts.
• Guideline Adherence: Degree to which the AI-generated text conforms to the specified structural and content guidelines, including prioritization of indicators and categorical breakdowns.
• Completeness: Accurate reporting of all key indicators present in the input data.
• Efficiency Gains: Potential for time reduction in the report drafting process, assessed qualitatively.

Deliverables

• A functional prototype of the AI-supported report drafting system.
• A final POC report summarizing the development process, findings, and recommendations (this document).

5. Success Criteria & Expected Outcomes

Success Criteria

The PoC will be considered successful if:

• The prototype demonstrably generates coherent and contextually relevant narrative report drafts from organizational system data exports and supplementary narrative information.
• Domain experts provide a positive evaluation of the prototype concerning the quality, accuracy, and adherence of the generated drafts to established reporting guidelines.
• The prototype clearly indicates a potential for significant time savings and efficiency improvements in the overall report generation workflow.
• Key learnings regarding data pre-processing, prompt engineering, and model capabilities for this specific use case are identified.

Expected Outcomes

• A functional AI prototype that effectively assists in drafting narrative reports, thereby validating the feasibility of this approach for humanitarian organizations.
• Comprehensive feedback from domain experts on the prototype's utility, quality of output, and practical applicability.
• Actionable insights and recommendations for potential future development, scaling, and integration of the AI solution.

6. Requirements & Dependencies

Resources

The following resources are required for the successful execution of the PoC:

• Data:
  • Sample Excel spreadsheets exported from organizational systems, covering various time frames and scenarios.
  • Examples of final narrative reports (particularly the standard donor reports) corresponding to the sample data.
  • Narrative context information that typically accompanies the quantitative data for report generation.
• Domain Expertise: Consistent availability of domain experts from the organization:
  • Clarifying data nuances and reporting requirements.
  • Providing feedback during regular review meetings.
  • Testing and evaluating the prototype.

Dependencies

The successful completion and outcomes of this PoC are contingent upon:

• Data Quality and Availability: Timely provision of comprehensive and representative sample data by the domain experts. The quality of input data will directly impact the AI's output.
• Domain Expert Engagement: Active participation and timely feedback from domain experts throughout the development lifecycle.

7. Implementation Approach

The PoC implementation follows a structured approach utilizing AI-powered processes to address the automated report generation challenge. The implementation consists of two main components that work together to transform structured data into narrative reports.

7.1 Report Outline Generation

7.1.1 Overview

This functionality provides automated generation of report outlines based on established donor reporting structures. The system creates initial frameworks that align with humanitarian reporting requirements and organizational conventions.

7.1.2 Process Description

The outline generation process operates autonomously to create structured report templates. It establishes the foundational structure including area offices, field offices, and service categories based on standard humanitarian reporting formats.

Key Features:

• Automated generation of hierarchical report structures
• Compliance with donor reporting conventions
• Customizable templates for different organizational contexts
• Integration with existing reporting workflows

Benefits:

• Consistent report structure across all generated documents
• Reduced time for initial setup and planning
• Standardized approach to donor reporting
• Foundation for subsequent content generation

7.2 Narrative Report Generation

7.2.1 Overview

This functionality transforms structured activity data into comprehensive narrative reports suitable for donor submissions. The process combines quantitative data analysis with contextual information to create coherent, professional reports that meet humanitarian reporting standards.

7.2.2 Process Description

The report generation process operates through a two-stage approach:

Stage 1: Data Analysis and Narrative Creation

• Ingestion of structured activity data containing organizational hierarchy, service types, and beneficiary demographics
• Analysis of service delivery patterns and beneficiary reach
• Generation of natural language descriptions prioritizing high-impact activities
• Structured presentation of data with appropriate demographic breakdowns

Stage 2: Contextual Enhancement

• Integration of background information and contextual details
• Augmentation of quantitative data with qualitative insights
• Preservation of factual accuracy while improving narrative flow
• Preparation of final report suitable for stakeholder review

Key Features:

• Automated transformation of spreadsheet data into narrative format
• Intelligent prioritization of content based on impact metrics
• Demographic analysis with gender-disaggregated reporting
• Integration of contextual information for comprehensive reporting
• Maintenance of source references for verification and compliance

Benefits:

• Significant reduction in manual report drafting time
• Consistent application of reporting guidelines and standards
• Comprehensive coverage of all key indicators and metrics
• Professional narrative suitable for direct stakeholder communication
• Scalable approach adaptable to different organizational contexts

8. Evaluation and Lessons Learned

Key Performance Outcomes

Efficiency Gains: The PoC demonstrated significant potential for time savings in report generation processes. Users reported substantial reductions in effort required for initial drafting phases, particularly when dealing with complex multi-source data requiring synthesis and analysis.

Quality Assessment: AI-generated content consistently met baseline quality expectations for donor reporting. The structured approach to content generation resulted in improved consistency and comprehensive coverage compared to traditional manual methods.

User Acceptance: Positive feedback regarding practical applicability within operational workflows. Users expressed willingness to integrate AI-assisted approaches into regular reporting processes, highlighting potential for widespread adoption in humanitarian contexts.

Core Lessons Learned

Data Quality is Fundamental: The importance of high-quality, well-structured input data became evident throughout the evaluation. Effective pre-processing of spreadsheet data proved essential for accurate reporting and reducing inconsistencies in generated outputs.

Context Integration Enhances Value: The combination of quantitative data with relevant narrative context information significantly improved the comprehensiveness and usefulness of generated reports. AI performed optimally when provided with both structured data and background information.

Comprehensive Over Concise: Users consistently preferred longer, more detailed AI-generated outputs rather than abbreviated versions. The ability to edit and reduce content proved more valuable than having to expand insufficient material.

Iterative Development Approach: Continuous feedback loops and iterative refinement emerged as critical success factors for achieving desired levels of accuracy and nuance in report generation.

Best Practices for AI-Enhanced Reporting

Structured Data Preparation: Maintaining consistent data formats and schemas significantly improves AI processing accuracy and reliability. Investment in data preparation yields substantial benefits in output quality.

Multi-source Integration: The most valuable applications involve combining information from multiple sources rather than simple content extraction or reproduction from single datasets.

Quality Assurance Processes: Implementing systematic review and verification processes ensures accuracy and relevance of AI-generated content while maintaining organizational reporting standards.

User Training and Support: Comprehensive understanding of AI capabilities and limitations is essential for effective utilization and appropriate expectations management among staff.

Future Development Considerations

The evaluation highlighted several areas for continued development, including enhanced multi-language support, improved data integration capabilities, and expanded customization options for different organizational contexts. The foundation established by this PoC provides a solid basis for scaling AI-enhanced reporting processes across various humanitarian and development cooperation contexts.