Appendix C: Case Study Example – GenAI for Wildland Fire Management

Introduction

The principles and phases outlined in this Best Practice document are modeled on successful, business-driven transformation methods. The OGC Engineering Reports D-030: Generative AI for Wildfire State-of-the-Art Report and D-123: Generative AI in Wildland Fire Management serve as a practical case study of these principles in action.

While these reports were not created by explicitly following this document, their structure, content, and recommendations retroactively demonstrate the core activities of each phase of the methodology. They provide a robust example of how to conduct a thorough analysis that results in a credible and actionable plan for a complex GenAI initiative.

Application of the 5 Phases in the Case Study

Phase 1: Initiation and Scoping (“Foundation Building”) The reports clearly establish a foundation by defining the problem space and scope.

Business Need: The reports identify the need to enhance planning and operational decision-making in the Wildland Fire community, which currently relies heavily on experiential knowledge, by leveraging GenAI’s ability to scale data processing.
Scope and Objectives: The scope is clearly defined as assessing the “domain adaptation needed for Generative AI to support advancements in the wildland fire community”. The objectives include exploring the necessary inputs, tools, challenges, and recommendations for implementation.
Stakeholder Identification: The reports identify and incorporate input from a wide range of stakeholders, including the US Forest Service , Bureau of Land Management practitioners , and Natural Resources Canada (NRCan) , as well as key roles like National Planners and Incident Command.

Phase 2: Business Analysis (“Use Case Development”) This phase is extensively covered in both reports, demonstrating a deep analysis of stakeholder needs and potential GenAI applications.

“As-Is” Analysis: The reports implicitly analyze the current state by discussing the challenges of situational awareness and the limitations of existing tools and workflows.
Use Case Identification and Prioritization: A core activity in the reports is the detailed identification and prioritization of use cases. The analysis for the Canadian insurance sector, for example, explicitly rates use cases for “Helping People” and “Business Management” on their “GenAI Value” and stakeholder “Need”.

Phase 3: Technical and Data Analysis The reports contain a rigorous assessment of the technical and data feasibility required for the identified use cases.

Data Readiness Assessment: The reports perform extensive “Data Mapping” , inventorying over 200 Canadian data sources and providing a conceptual Data Reference Model (DRM) to categorize them. They also identify critical data gaps, such as the need for national structures data.
Technology Assessment: A dedicated section evaluates the “Technology Components Need to Support Gen AI,” detailing the roles of LLMs, RAG, GANs, and AI Agents. This assessment concludes that augmenting foundational LLMs is necessary to handle the specific, real-time demands of wildland fire management.
Risk Analysis: The reports explicitly detail risks in sections on “Challenges in Leveraging Gen AI” and “Key Considerations for Gen AI Model Development”. These sections discuss risks such as model “hallucinations” , poor data traceability , and the challenges of user adoption.

Phase 4: Modernization Blueprint and Business Case The reports themselves serve as the Modernization Blueprint, synthesizing the analysis into a set of clear, actionable recommendations and plans.

Solution Recommendation: The reports recommend specific “Potential Prototypes,” such as a “Predictive Risk Dashboard” and a “Claims Automation System,” which function as concrete solution proposals.
Implementation Roadmap: A detailed, four-phase “GenAl Roadmap” is provided, outlining key activities and deliverables for Foundation Building, Use Case Development, Pilot Implementation, and Scaling & Optimization.

Phase 5: Governance and Continuous Improvement A strong emphasis on governance and long-term evolution is woven throughout the reports’ recommendations.

Governance Model: The reports recommend establishing a “Governance Framework” and adopting standards like the OGC TrainingDML-AI to ensure data provenance and ethical AI deployment.
Continuous Improvement: The analysis highlights that LLMs can be “closed loop” systems and therefore recommends establishing a “continuous training and labeled data Improvement lifecycle” to ensure models remain accurate and reliable over time.

Conclusion

The GenAI for Wildland Fire reports provide a compelling, real-world example of how this best practice methodology can be applied to produce a high-quality analysis. They demonstrate a logical progression from understanding the business problem to defining a feasible, well-justified, and actionable path forward for a complex and mission-critical GenAI initiative.