Workshop
AI for Decision Support in Defense Operations
How machine learning and data fusion enhance situational awareness and planning
- Duration
- 3 days
- Format
- Live virtual
- Hours
- 9:30 AM – 5:30 PM ET
- Programming
- Not required
You will be able to
- Explain core AI and ML concepts as they bear on operational decision support
- Recognize how data fusion improves the quality and timeliness of a decision
- Evaluate the opportunities, limits and risks of AI across mission areas
- Apply a structured approach to integrating AI into planning cycles
- Design an initial roadmap for adoption in a unit or organization
Who it's for
- Officers, analysts, planners and logisticians
- Defense contractors and federal program managers
- Intelligence and mission-support personnel
What you need first
- Familiarity with defense or government operations is helpful
- No programming or advanced mathematics required
Agenda
Day 1
Foundations & mission context
- Introduction and course orientation (45m) — where AI sits in current military technology, and why that matters operationally.
- AI and machine learning essentials (1.5h) — how data and algorithms become mission outcomes, worked through real defense examples.
- Data fusion for situational awareness (1.5h) — handling uncertainty, conflicting reporting, and time-critical decisions.
- Defense use cases, deep dive (2h) — case discussion covering the successes and, more usefully, the lessons learned.
- Mission mapping exercise (1h) — teams take a scenario, find the decision points, and propose where AI would and would not help. Presented back for peer critique.
Day 2
Building AI-enabled decision support
- From mission needs to AI requirements (1h) — problem framing, data needs assessment, and defining success measures that track the mission rather than the model.
- Human–machine teaming (1h) — explainability requirements, building warranted trust, and keeping commander oversight meaningful.
- System lifecycle and assurance (1h) — test, validation, red-teaming and continuous monitoring.
- Contested logistics scenario workshop (2h) — teams take a problem end to end, from identification through to solution architecture.
- Security and ethics (1h) — OPSEC implications, adversarial AI risk, and the DoD AI Ethical Principles applied to cases rather than recited.
- Measuring impact (1h) — readiness, decision speed, accuracy and cost-benefit.
Day 3
Operationalization & roadmapping
- Governance and oversight (1h) — the current regulations and what compliance requires.
- Building an AI-ready organization (1h) — practical tools for growing capability in-house rather than renting it indefinitely.
- Capstone project (2.5h) — requirements analysis, solution design, implementation roadmap, risk assessment.
- Team presentations and peer feedback (1.5h) — structured critique to surface the implementation problems early.
- Future trends and close (1h) — what is coming, and how to stay current without chasing every announcement.
Materials provided
- Illustrated slides and a glossary of AI terms
- Mission vignettes and data-fusion templates
- A decision-support framework and roadmap checklist
- Recommended reading list
Security & classification
The published course and all its materials are unclassified, and every scenario is sanitized for training. Any additional security requirements are subject to contract requirements and confirmed eligibility.
Request dates, or a private cohort.
This runs as a scheduled cohort or privately for a single organization, virtual or on-site. Tell us the room — how many people, how senior, and what they need to be able to do afterwards — and we'll tell you whether this is the right course for them.