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
- Recognise 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 organisation
Who it is 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 actually sits in the military technology landscape, 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
Operationalisation & roadmapping
- Governance and oversight (1h) — the regulatory landscape and what compliance actually requires.
- Building an AI-ready organisation (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
All materials are unclassified and every scenario is sanitized for training. Where a cohort needs classified context, that is arranged separately and in advance.
Request dates, or a private cohort.
This runs as a scheduled cohort or privately for a single organisation, 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 will tell you honestly whether this is the right course for them.