Phase 2: Business Analysis ("Use Case Development")

Key Activities

1 Analyze the "As-Is" State:

• Document the current business processes, workflows, and performance metrics associated with the problem area. This includes mapping the sequence of activities, the systems used, and the roles involved. This includes mapping the step-by-step flow of activities — showing how tasks progress from one person, system, or decision point to the next — and identifying the key systems and roles involved at each stage. The goal is to capture how work currently moves through the process, not to build a detailed technical system diagram.
• Conduct structured interviews and workshops with stakeholders to understand their day-to-day operations, challenges, and unmet needs. The goal is to identify specific pain points and inefficiencies that GenAI could potentially alleviate.
• Analyze existing data and reports to establish a baseline for current performance. Document how historical fire reports, incident logs, and weather forecasts inform decisions on crew and equipment deployment. Identify trends in these reports, such as recurring high-risk areas, resource bottlenecks, or patterns in fire spread. For example, in the wildland fire context, this could include how situational reports are created and how allocation decisions are made. Highlight how GenAI could assist by analyzing large volumes of geospatial and sensor data to detect emerging trends, generate predictive insights, optimize deployment strategies and crew assignments, and provide recommended actions in near real-time.

2 Identify and Prioritize GenAI, AI, Analytics and Data Workflow Use Cases:

To ensure a comprehensive approach, this step will examine use cases across a spectrum of interrelated capabilities, from foundational to advanced:

• Data Workflows: The end-to-end pipelines that ingest, move, and transform data (e.g., ETL processes, streaming data ingestion from IoT sensors).
• Analytics: The methods used to derive insights from existing data, typically by examining "what happened" (e.g., BI dashboards, statistical reports, geospatial analysis of fire perimeters).
• AI (Artificial Intelligence): The broader set of systems that learn from data to perform intelligent tasks, such as making predictions or classifying information (e.g., a machine learning model that forecasts wildfire spread based on weather and fuel data).
• GenAI (Generative AI): A specific category of AI that creates new, novel content (text, code, imagery, data) rather than just analyzing or predicting (e.g., an LLM summarizing complex environmental reports for a policymaker, a model generating synthetic data for a simulation).

This process will look for opportunities across all four areas.

• Brainstorm a list of potential GenAI, AI, Analytics and Data Workflow Use Cases based on the "as-is" analysis. These use cases should be directly linked to the identified pain points and desired business outcomes.
• Categorize the use cases into logical MISSION groups. Group use cases by logical mission phases (e.g., if in Wildland Fire: Mitigation, Preparedness, Response, Recovery) or by stakeholder group (e.g., if in Insurance: Helping People, Insurance Business Management). Consider using a Business Reference Model Taxonomy concept to assist in this step.
• Categorize the use cases of the capabilities and consider using a framework to guide stakeholders of what areas to consider the use cases capabilities touch. The below framework is designed as a guide to help - not direct - teams categorize potential GenAI, AI, Analytics and Data Workflow use cases according to the technical and functional capabilities they involve. Each main category represents a different capability area (e.g., how data is managed, how reasoning is performed, or how users interact with the system). The sub-items illustrate examples of activities, models, or services that fall under that category. Stakeholders can use this framework as part of this categorization step to assess which capabilities each use case touches and to identify gaps or patterns.

• Evaluate and prioritize these use cases using a formal matrix. This matrix should score each use case against a set of defined criteria. Valuable criteria can include:
  • Stakeholder Need / Mission Impact: How critical is this for the end-user and the organization's mission? 
  • Capability (GenAI, AI, Analytics and Data Workflow) Value: How significant is the potential improvement that GenAI can provide compared to existing solutions? Measures the significance of the improvement GenAI/AI provides over existing solutions. This value can be assessed qualitatively (e.g., Low, Moderate, Transformative) or by estimating quantitative impact (e.g., "potential 50% reduction in manual reporting time," "improved forecast accuracy," "time savings," or "higher quality of decision support").
  •  Categorization: Have capability to breakdown by the mission or capability category (GenAI, AI, Analytics and Data Workflow) tagged to assist in trends or to uncover findings. This helps identify gaps, overlaps, or emerging opportunities across prioritized use cases. This also helps tie the table to this as well as clarify understanding of the table that is the GenAI capability framework.
  • Technical Feasibility: How practical is it to develop and implement this use case with current technology and data?
  • Data Readiness: Is the necessary data available and of sufficient quality?

The output should be a ranked list of use cases, allowing the team to focus on the one(s) with the highest potential for success and impact and weighted by factors in Stakeholder Need, Mission Impact, Capability Portfolio, Categorization, Technical Feasibility, and Data Readiness. This will be critical to providing guidance with a line of sight set of potential balanced improvement opportunity recommendations.

3 Develop the "To-Be" Vision:

• For the highest-priority use case(s), develop a clear and detailed "to-be" vision. This is a narrative and visual description of the desired future state with the GenAI solution implemented.
• Create a conceptual workflow diagram that illustrates how users will interact with the new GenAI, AI, Analytics, and workflow capability(ies) powered process.  Dare to cite and group by key components without citation of IT or technical aspects. ICOM (Inputs, Controls, Outputs, and Mechanisms) boxes of IDEF0 models tend to help in noting the mechanism as an interface, without mention of technical or product specifications. Example Mechanisms may be: 

  Data Aggregation	Information Platforms	Data Platforms	Knowledge Platforms	Wisdom Capabilities
  Serverless Data Pipelines Traditional ETL Batch Data Data Quality RPA Services Metadata Harvesters Web Service Processing Big Data ELT Pipelines Real-Time Data Hubs Large Feed & Raster Processing	Web, Voice, & Mobile App Dev MIS Stack Integration (e.g. MIS, ERP, CRM, etc.)	Interfaces / APIs Geospatial Platforms Business Intelligence Rules-Based & Gamified Search Signals Metadata Catalogs Enterprise Datasets	Data Science Cloud Workbenches Advanced Data Analytics Platforms Semantic Platforms Mission Data Lakes Orchestration Data Warehouse	AI NLP Gap, Confidence Highlight, and Geospatial Extraction ML classification, clustering, and predictive analytics. Massive streaming aggregations Data Science AI Workbench platforms A/B Signal testing & acceptance/rejection lifecycle Multiple-ontological and linked data solutions Sentiment analysis Low-level atomic big data analytics.

• Develop a high-level conceptual architecture diagram. This diagram should show the key components of the solution and how they connect with data sources and end-users. It should also illustrate the flow of data from input to a potential GenAI-generated solution. Use the reference above to help in laying out and organizing the groups of components. While this would be flushed out in the next Technical Step, initial components needed. Consider the full data, information, knowledge integration with AI Wisdom and informed by the Data Aggregation, Information Platforms, Data Platforms, and Knowledge Platforms capabilities. While technologies could be noted, they should be illustrative purposes (e.g. to consider use of potential existing enterprise investments).

Inputs for this Phase

• Project Charter and Stakeholder Map (from Phase 1).
• Access to subject matter experts and end-users for interviews and workshops.
• Documentation of existing processes and systems.

Outputs of this Phase

• "As-Is" Process Analysis Report: A document detailing the current workflows, pain points, and performance baselines.
• Prioritized Use Case Matrix: A ranked list of potential GenAI use cases with their evaluation scores, justifications, and a recommendation of which use case(s) to pursue.

"To-Be" Conceptual Design Document: A document containing the narrative vision, conceptual workflow diagrams, and high-level architecture for the selected use case.