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Military & Defense Executives
Foundation and Orientation
Foundation and Orientation
This opening section builds a common baseline for senior leaders, engineers, and acquisition professionals. We trace how AI evolved inside federal missions, clarify what GenAI and LLMs actually do (and where they fail), and show how to read model outputs with confidence. The session closes by grounding those skills in concrete defense and VA use cases—so teams leave with shared language, practical checks, and a clear sense of where AI belongs in real work.
This opening section builds a common baseline for senior leaders, engineers, and acquisition professionals. We trace how AI evolved inside federal missions, clarify what GenAI and LLMs actually do (and where they fail), and show how to read model outputs with confidence. The session closes by grounding those skills in concrete defense and VA use cases—so teams leave with shared language, practical checks, and a clear sense of where AI belongs in real work.
Aligned with VA Ethical Principles, NIST AI RMF, and OMB Memos M‑25‑21 and M‑25‑22.
Aligned with VA Ethical Principles, NIST AI RMF, and OMB Memos M‑25‑21 and M‑25‑22.
Outcomes You Can Expect
Outcomes You Can Expect
You will learn to:
You will learn to:
- Differentiate automation, ML, GenAI, and LLMs, and know where each fits in federal infrastructure
- Explain, in plain language, how LLMs produce answers and why they sometimes fail
- Apply fast “signal vs. noise” checks to verify AI‑generated analysis before it’s used
- Recognize common failure modes (over‑confidence, hallucination, spurious reasoning) and how to mitigate them
- Map mission‑relevant use cases across DoD, VA, and civilian agencies (e.g., ISR triage, logistics, claims review)
- Identify the data and workflow pre‑conditions a use case needs to succeed
- Understand high‑level integration patterns (GovCloud/zero‑trust) and why they matter for deployment
- Ask the right vendor and procurement questions to separate capability from hype
- Read model and system metrics (accuracy, calibration, provenance) for executive decision‑making
- Tie AI work to mission outcomes using MOP/MOE and simple readiness indicators
- Produce a crisp one‑page BLUF brief that states risk, confidence, and recommended next steps
- Establish a shared vocabulary so technical and non‑technical teams can move in lockstep
Outcomes You Can Expect
Outcomes You Can Expect
You will learn to:
You will learn to:
- Differentiate automation, ML, GenAI, and LLMs, and know where each fits in federal infrastructure
- Explain, in plain language, how LLMs produce answers and why they sometimes fail
- Apply fast “signal vs. noise” checks to verify AI‑generated analysis before it’s used
- Recognize common failure modes (over‑confidence, hallucination, spurious reasoning) and how to mitigate them
- Map mission‑relevant use cases across DoD, VA, and civilian agencies (e.g., ISR triage, logistics, claims review)
- Identify the data and workflow pre‑conditions a use case needs to succeed
- Understand high‑level integration patterns (GovCloud/zero‑trust) and why they matter for deployment
- Ask the right vendor and procurement questions to separate capability from hype
- Read model and system metrics (accuracy, calibration, provenance) for executive decision‑making
- Tie AI work to mission outcomes using MOP/MOE and simple readiness indicators
- Produce a crisp one‑page BLUF brief that states risk, confidence, and recommended next steps
- Establish a shared vocabulary so technical and non‑technical teams can move in lockstep
Instruction Modes Available
Instruction Modes Available
Self-Paced - Learn Independently with model + human support
Live Intensive - Instructor-led sessions for team or group tracks
Enterprise Custom - Full Design = White Label curriculum for orgs
Train the Trainer - Appoint an internal lead, we do the rest
On-Site / Private Workshop - We come to you. Tailored and Immersive
Self-Paced - Learn Independently with model + human support
Live Intensive - Instructor-led sessions for team or group tracks
Enterprise Custom - Full Design = White Label curriculum for orgs
Train the Trainer - Appoint an internal lead, we do the rest
On-Site / Private Workshop - We come to you. Tailored and Immersive
Foundation and Orientation
Foundation and Orientation
The AI Landscape: Federal Evolution to Present
Understand how artificial intelligence has progressed from automation to strategic command support, and why informed leadership, not technical mastery, defines readiness.
🟤How AI moved from data automation to mission‑critical analysis
The AI Landscape: Federal Evolution to Present
Understand how artificial intelligence has progressed from automation to strategic command support, and why informed leadership, not technical mastery, defines readiness.
🟤How AI moved from data automation to mission‑critical analysis
🟤Where GenAI and LLMs fit within defense and federal infrastructure
🟤Where GenAI and LLMs fit within defense and federal infrastructure
🟤Major milestones in federal adoption (DARPA, NIST AI RMF, VA initiatives)
🟤Major milestones in federal adoption (DARPA, NIST AI RMF, VA initiatives)
🟤The changing relationship between human judgment and machine output
🟤Emerging priorities: trust, interoperability, and lawful autonomy
🟤The changing relationship between human judgment and machine output
🟤Emerging priorities: trust, interoperability, and lawful autonomy
GenAI and LLMs: What They Are and Why They Matter
Build fluency in the systems now shaping intelligence, logistics, and procurement, without falling into hype.
🟤Core mechanics of generative models and language learning
GenAI and LLMs: What They Are and Why They Matter
Build fluency in the systems now shaping intelligence, logistics, and procurement, without falling into hype.
🟤Core mechanics of generative models and language learning
🟤How model scale, data, and prompting affect reliability and failure modes
🟤How model scale, data, and prompting affect reliability and failure modes
🟤Evaluating GenAI tools for defense and acquisition workflows
🟤Evaluating GenAI tools for defense and acquisition workflows
🟤Forecasting and simulation with AI‑assisted analytics
🟤Forecasting and simulation with AI‑assisted analytics
🟤Aligning model capacity with mission scope and ethical bounds
🟤Aligning model capacity with mission scope and ethical bounds
How LLMs Work (and How to Recognize Their Outputs)
Learn to recognize when a model’s answer is useful, risky, or wrong, and to apply results responsibly within command or compliance chains.
🟤Reading and verifying AI‑generated analysis
How LLMs Work (and How to Recognize Their Outputs)
Learn to recognize when a model’s answer is useful, risky, or wrong, and to apply results responsibly within command or compliance chains.
🟤Reading and verifying AI‑generated analysis
🟤Signal vs. noise: identifying over‑confidence and hallucination
🟤Signal vs. noise: identifying over‑confidence and hallucination
🟤Integrating LLMs within GovCloud and zero‑trust patterns
🟤Integrating LLMs within GovCloud and zero‑trust patterns
🟤Adversarial risk, data provenance, and model drift
🟤Adversarial risk, data provenance, and model drift
🟤Hands‑on diagnostic lab: interpreting outputs and setting safeguards
🟤Hands‑on diagnostic lab: interpreting outputs and setting safeguards
Use Cases in DoD, VA, and Civilian Agencies
Translate mission requirements into responsible, effective AI deployment across federal contexts.
🟤Defense Use Cases:
- Mission forecasting and wargame/battlefield simulation with AI
- ISR (Intelligence, Surveillance, Reconnaissance) data triage using LLMs
- Logistics and maintenance prediction for supply‑chain continuity
Use Cases in DoD, VA, and Civilian Agencies
Translate mission requirements into responsible, effective AI deployment across federal contexts.
🟤Defense Use Cases:
- Mission forecasting and wargame/battlefield simulation with AI
- ISR (Intelligence, Surveillance, Reconnaissance) data triage using LLMs
- Logistics and maintenance prediction for supply‑chain continuity
🟤Veterans Affairs Use Cases:
- NLP for triaging veteran health records and benefits claims
- Guided self‑service navigation (beyond basic “chatbots”)
- Predictive modeling for health trends and suicide risk monitoring (with human oversight)
🟤Veterans Affairs Use Cases:
- NLP for triaging veteran health records and benefits claims
- Guided self‑service navigation (beyond basic “chatbots”)
- Predictive modeling for health trends and suicide risk monitoring (with human oversight)
🟤Civilian Agency Use Cases:
- Public health monitoring and emergency response
- Fraud detection and claims auditing via ML
- Process automation in federal administrative workflows
🟤Civilian Agency Use Cases:
- Public health monitoring and emergency response
- Fraud detection and claims auditing via ML
- Process automation in federal administrative workflows
🟤Cross-Sector Relevance:
- Human-in-the-loop deployment models
- Where to avoid AI: false positives, low-trust environments, or ethical grey zones
- Interagency collaboration through shared AI protocols and data standards
🟤Cross-Sector Relevance:
- Human-in-the-loop deployment models
- Where to avoid AI: false positives, low-trust environments, or ethical grey zones
- Interagency collaboration through shared AI protocols and data standards
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