IntelliCademy AI Transformation Leader

• 32 hours of instruction across four days, delivered by IntelliGenesis instructors.
• Aligned to DCWF work roles AI Innovation Leader (902) and AI Adoption Specialist (753).
• Progresses from strategic foundations to applied governance and operational planning.
• Discussion-based learning, scenario analysis, planning exercises and decision-making activities.

• Senior leaders and executives responsible for modernisation and transformation.
• Program and project managers overseeing AI-enabled initiatives.
• Innovation leaders and digital transformation personnel.
• Policy, strategy and governance professionals.
• Operational leaders integrating AI into mission environments.
• Change agents and decision-makers shaping enterprise AI adoption.

• Experience in leadership, management, program oversight, innovation, policy, operations or transformation-related roles.
• Familiarity with organisational decision making, strategic planning or enterprise initiatives.
• Basic understanding of emerging AI capabilities and their potential application.
• Ability to engage with policy, governance, risk and performance discussions at an organisational level.
• A laptop or device capable of accessing course materials and completing practical exercises.

By the end of this course, participants will be able to:

• align AI adoption with mission priorities and strategic objectives
• establish governance and risk oversight structures that institutionalize responsible AI principles
• guide policies, funding strategies and acquisition models for sustainable, accountable AI operations
• shape workforce strategies and capability frameworks that enable scalable AI innovation
• define performance criteria and executive reporting that monitor AI value, effectiveness and trustworthiness
• provide executive oversight aligned to organizational risk tolerance, security standards and ethics
• advise senior decision-makers with evidence-based recommendations balancing innovation, risk and policy

• Day 1: AI and Machine Learning Foundations
• Module 0: Course Introduction
  • Course objectives, agenda, usage policy and a note on reference frameworks.
  • Course scenario introduction - participants may also bring a scenario of their own.
• Module 1.1: Basics of Data
  • What data is, data types and storage, volume versus value, population versus sample, and human context in data.
• Module 1.2: The AI/ML Lifecycle
  • Key definitions, roles and skills, the lifecycle end to end, and the train/test split.
• Module 1.3: Machine Learning
  • When to use machine learning; supervised learning, regression and classification; unsupervised learning.
  • From prediction to action, with a machine-learning scenario.
• Module 1.4: Deep Learning and Artificial Intelligence
  • Neural networks and basic architecture; prediction versus generation.
  • Natural language processing, large language models, prompt development and engineering.
  • AI versus AI systems versus agentic AI, retrieval-augmented generation, reinforcement learning, AI by capability.
• Module 1.5: Monitoring, Deployment and Evaluation
  • Model-to-mission impact, defining success, establishing baselines, and performance metrics that matter.
  • Evaluating beyond accuracy; production deployment, scope and integration into operational ecosystems.
  • Drift, updating strategies, rollout methods, version control, accountability and feedback loops.
• Day 2: Cybersecurity, Management and Stakeholders
• Module 2: Networking and Cybersecurity Fundamentals
  • Collaboration with cybersecurity teams, the CIA triad, authentication and authorization, securing APIs.
  • Access control and secrets management, secure coding practices, encryption and secure logging.
  • Data privacy and compliance, anomaly detection, and adversarial AI/ML.
  • Common threats - data poisoning, SQL injection, phishing and malware.
  • Networking for AI/ML, and cloud and model deployment basics.
• Module 3.1: The AI Leader's Role
  • Mission, strategy to execution, the balanced scorecard, and how leaders think about AI.
  • Turning problems into programs and thinking beyond today.
• Module 3.2: Stakeholder Analysis and Engagement
  • Stakeholders, the power-interest matrix, engagement strategies, the stakeholder register and RACI.
  • Bridging the gap: synchronization, consultation, continuity, communication and the SCQA framework.
• Module 3.3: Workforce Leadership and Team Management
  • Multidisciplinary teams and the roles to coordinate, workforce structures and RACI.
  • Recruitment and retention challenges, and building talent internally.
• Module 3.4: Resource and Program Management
  • Resources and demand, sustaining support, making the numbers work, optimizing limited resources.
  • Leadership during deployment, and turning AI ideas into funded initiatives.
• Day 3: Governance, Ethics, Policy and Risk
• Module 4.1: Ethics
  • Ethical concerns and common ethical pillars, how to implement them, ethical frameworks and boundaries.
• Module 4.2: Regulations
  • What is legal, regulation focus, AI regulation in operations and in DoD operations.
  • Statutory foundations, legal constraints and triggers for legal review.
  • PHI, PII and sensitive data; data use and reuse; regulatory red flags.
• Module 4.3: Policies
  • Where policy comes from and how it lands in operations; the DoD responsible AI policy landscape.
  • America's AI Action Plan and the DoW AI Strategy.
  • Common policy, acquisition and deployment requirements; compliance and operational consequences.
• Module 4.4: Governance
  • Governance bodies and charters, governance across the lifecycle, and evidence.
  • Reporting and escalation methods, intervention and control authority, auditability and defensible decisions.
• Module 5: Risk Management
  • AI/ML risk assessments: identifying and evaluating risks, qualitative and quantitative methods.
  • Mitigating risks, monitoring, and risk assessment frameworks and tools.
• Day 4: Adoption, Advocacy and Education
• Module 6.1: Human-Centered AI Systems
  • Socio-technical systems, human-centered design, human factors and their fundamental concepts.
  • User experience and UX design for AI systems.
• Module 6.2: Training, Awareness and Education
  • Instructional methods and design frameworks; the OPM training needs assessment and evaluation field guide.
  • Common mistakes in AI training.
• Module 6.3: Motivation and Culture The psychology of AI adoption and of motivation, motivational frameworks, and building a safe, innovative culture.
• Module 6.4: AI Strategy
  • Purpose and common pitfalls, how to build an AI strategy, and turning insights into strategy.
• Module 6.5: Change Management
  • Why change fails, change management frameworks, AI adoption, measuring adoption and adoption signals.
  • Course conclusion.

• Four days of instructor-led delivery built around a running scenario, with discussion and
• exercises throughout - participants may work the supplied scenario or one from their own
• organization.
• Each module closes with a timed quiz: 15 minutes, two attempts, 80% to pass.
• The course concludes with a review and course survey.

Pairs with the IntelliCademy AI/ML Specialist bootcamp for the technical practitioner track.