Use Cases
Airpower GenAI Use Cases
Practical, non-classified workflow patterns for training, acquisition, readiness, contractor operations, and AI governance.
These use cases are starting points for discussion. Each requires workflow design, data-boundary review, human oversight, and evaluation before implementation.
1Training & Readiness
Instructor Copilot
Helps instructors generate lesson plans, remediation notes, and student-specific training recommendations.
Debrief Summarizer
Turns notes, event data, and instructor observations into structured debrief outputs.
Training Deficiency Analyzer
Identifies recurring performance gaps across students, events, or syllabus phases.
Simulator Utilization Optimizer
Helps align simulator periods with training need, bottlenecks, and readiness priorities.
2Acquisition & Program Support
PWS / SOW Drafting Assistant
Helps structure performance work statements, statements of work, and requirement language.
Market Research Summarizer
Summarizes vendor capabilities, technology categories, and market signals.
Proposal Compliance Matrix Bot
Maps solicitation requirements to proposal sections and response owners.
Contract Document Review Assistant
Highlights obligations, deliverables, risks, and inconsistencies in contract-related documents.
3Contractor Operations
Capture Strategy Assistant
Supports account planning, opportunity analysis, competitor mapping, and win-theme development.
Past Performance Extractor
Finds relevant past performance examples from internal proposal and program materials.
Engineering Knowledge Assistant
Retrieves lessons learned, design rationale, and technical documentation.
Internal Policy Assistant
Answers employee questions using approved company policies and procedures.
4AI Governance
AI Use-Case Intake Workflow
Standardizes how teams propose, review, approve, and track AI use cases.
Human-in-the-Loop Review Process
Defines where people must review, approve, or override AI outputs.
Risk Register Builder
Helps teams identify, categorize, and track GenAI workflow risks.
Model Output Evaluation Rubric
Defines how AI outputs are tested for quality, accuracy, traceability, and usefulness.