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Section 1. Concept: What AI governance means for small teams

Definition and scope for small teams

Pixel Quest: Governance Foundations Review

Why lightweight governance matters for speed

Governance vs bureaucracy: finding balance

Three pillars of practical governance

Key stakeholders and boundaries

Governance mindset for founders and teams

Early success metrics and signals

Section 2. Operating implications and organizational impact: translating concept into practice

Design a lightweight operating model

Assign roles without creating silos

Establish decision rights and a living log

Synchronize with product and engineering rhythms

Document without friction: minimally viable policies

Pilot governance with small projects

Learn and adapt: feedback loops and retros

Cultivating a Culture of Learning and Adaptation

Quiz

Quiz 1: Foundations of Governance

Quiz 2: The Three Pillars

Quiz 3: Roles and Decision Rights

Quiz 4: Operational Rhythms

Quiz 5: Adaptation and Culture

Course Feedback

Course Evaluation Survey

Agile AI Governance: Lightweight Oversight for Small Teams is a practical, no-bureaucracy course designed for startups, lean product squads, and resource-constrained teams shipping AI features fast. Instead of heavyweight compliance frameworks, you’ll learn compact, high-impact guardrails that preserve velocity while reducing risk. Through clear concepts, operating models, and actionable templates, this course shows you how to embed just-enough governance into your existing agile rhythms—sprints, standups, reviews—without adding friction. You’ll distinguish governance from red tape, define decision rights, and implement a living decision log that creates accountability and traceability in minutes, not meetings. Explore the three pillars of practical governance—people, policy, and process—and see how they work together to prevent costly rework, reputational damage, and product delays. You’ll map stakeholder roles across the AI lifecycle, craft minimally viable policies, and pilot improvements on low-risk initiatives before scaling. With quizzes, a gamified recap, and step-by-step workflows, you’ll build a culture of shared ownership for AI safety, backed by measurable signals of success. By the end, you’ll have a lightweight operating model that aligns product, engineering, and leadership, enabling faster, safer decisions at every release.

Agile AI Governance: Lightweight Oversight for Small Teams

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            <p>In the rapidly evolving landscape of artificial intelligence, small teams and startups often face a critical dilemma: the necessity of rapid innovation versus the requirement for responsible oversight. Traditional AI governance models, typically designed for large-scale enterprises, frequently introduce excessive bureaucracy that can stifle development velocity and exhaust limited resources. This course, Agile AI Governance: Lightweight Oversight for Small Teams, addresses this challenge by defining a specialized framework tailored for resource-constrained environments. It explores how to distinguish between value-added guardrails and restrictive red tape, ensuring that oversight serves as a catalyst for sustainable growth rather than a barrier to progress.</p>

<p>Participants will examine the three core pillars of practical governance—people, policy, and process—and learn to translate these concepts into a lightweight operating model. The curriculum covers the assignment of decision rights, the maintenance of living decision logs, and the synchronization of oversight with existing agile engineering rhythms. By focusing on minimally viable policies and iterative feedback loops, students and professionals will gain the skills necessary to foster a culture of shared ownership and safety. This approach empowers small teams to manage AI risks effectively while maintaining the agility required to compete in a high-stakes technological environment.</p>
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