MIT Professional Education (Global Alumni) · Course Guide interactivo
Applied Agentic AI for Organizational Transformation
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8 semanas · 8 módulos · 70 h
4–6 h/semana
40% entregas · 60% capstone
Certificado: survey + 80% obligatorias
Facultad: Dr. Abel Sanchez · Prof. John R. Williams
Índice
Descripción del curso
Diseñado para tomadores de decisión que reconocen el poder transformador de la IA. Dirigido a audiencia no técnica — gerentes, ejecutivos, C-suite. Empieza con fundamentos (historia de la IA, cómo funciona, hacia dónde va), pasa por el panorama actual de IA generativa y el campo emergente de la IA agéntica, y equipa al participante como adoptante estratégico temprano.
Con la base conceptual lista, el curso pasa a implementación: integración con infraestructura digital existente (APIs, software empresarial), evaluados frente a los objetivos operativos y financieros de la organización. Cada módulo incluye mini-proyectos prácticos: diseñar landing pages, crear bots, evaluar costos — sin requerir trasfondo técnico especializado.
Dado el impacto profundo de estas tecnologías, el curso también cubre consideraciones éticas y regulatorias: deepfakes, desinformación, prompt injection, y marcos de cumplimiento como GDPR, CCPA y HIPAA.
El curso culmina en un plan de adopción de IA de 2 a 5 páginas, aplicando todo lo aprendido para optimizar operaciones e impulsar innovación en la organización propia del participante.
Módulos y syllabus
Módulo 1 · Foundations of Generative and Agentic AI 4 h
Secciones
- Generative AI Fundamentals
- AI Chatbots: Past, Present, and Future
- Cost-Optimized Models and Performance Trade-Offs
- Multimodal AI: Audio, Image, and Language Systems
- AI Tools and the Shift Toward Agentic AI
Objetivos
- Evaluate the strategic value of AI functionalities such as chatbots, reasoning, and multimedia
- Construct an evaluation of the cost of an AI system
- Distinguish between major AI model types and terminology
Actividades
- Required Assignment 1.1: Evaluating the Cost of AI Systems
- Optional Forum 1.1: Tailored AI Assistants · Forum 1.2: AI Tools · Self-Study Knowledge Checks
Módulo 2 · The Rise of Agentic AI and Emerging AI Platforms 4 h
Secciones
- Emerging Agentic Platforms
- Vibe Coding and the "Vibe Living" Mindset
- Single vs. Multi-Agent Architectures
- Open-Source vs. Closed-Source AI Systems
- Cloud Infrastructure for Agentic AI Systems
Objetivos
- Explain the most relevant AI platform or approach for a specific sector and its application to agentic AI use cases
- Evaluate the key factors influencing the selection of open-source versus proprietary AI platforms within a specific organizational context
- Develop a landing page using AI; prompt AI to create a visual mock-up and functional HTML code; activate the code by saving and reuploading
- Explain a new AI workflow in an organization
Actividades
- Required Assignment 2.1: Vibe Coding
- Optional Forum 2.1: AI Platforms · Forum 2.2: Open-Source vs. Proprietary Models · Self-Study Knowledge Checks
Módulo 3 · Connecting Agents to Digital Ecosystems 4–6 h
Secciones
- Building Agents into Existing Workflows
- Integrating Generative and Agentic AI With Existing Systems: Challenges and Solutions
- Spotify Model Context Protocol (MCP) and Other Edge Cases of Agent Integration
- Empathy and Response Tuning for Customer-Facing Agents
- IoT Integration and Agent Ecosystems
Objetivos
- Construct a use case demonstrating agent-based interaction across integrated tools
- Write a structured email-style proposal that outlines a specific use case for an AI agent within an organizational context
- Analyze a business workflow to determine how an AI agent could improve efficiency, reduce costs, or enhance user experience
- Design an integration approach that specifies how the proposed agent would connect with existing systems, platforms, or APIs
- Evaluate the potential risks, ethical considerations, and success metrics associated with deploying the proposed AI agent
Actividades
- Required Assignment 3.1: Conceiving and Programming of Agents
- Optional Forum 3.1: Model Context Protocol (MCP) · Self-Study Knowledge Checks
Módulo 4 · Cybersecurity: Classic Scenarios, Agent Risks, Disinformation, and Systemic Impact 4–6 h
Secciones
- Classic and Current Cybersecurity Risks
- Cybersecurity Response and Prevention
- Limits of AI Perception and Error Correction
- Blockchain for AI Security and Trust
Objetivos
- Analyze organizational AI systems and workflows to identify potential cybersecurity risks, using the NIST Cybersecurity Framework categories
- Evaluate how accountability is defined and enforced alongside security practices and governance in AI systems
- Evaluate current security practices to identify gaps in access control, monitoring, response, and recovery capabilities
- Develop a structured AI risk and security plan, including stakeholders, training, and incident response procedures
- Recommend actions to improve organizational readiness across identify, protect, detect, respond, and recover domains
Actividades
- Required Assignment 4.1: AI Risks and Security Plan
- Optional Forum 4.1: Everyday Cybersecurity Threats · Forum 4.2: AI Systems and Accountability · Self-Study Knowledge Checks
Módulo 5 · AI Agents by Business Function 4–6 h
Secciones
- The AI Maturity Cycle
- Agentic AI in Product Development Lifecycle
- Agents by Business Function Across the Enterprise
- Agentic AI in Health Care: Key Applications and Impact
- Agentic AI in Browsers: Potentials and Risks
- Agent Architecture in the Enterprise: Centralized vs. Embedded
- The Role of Consultants in Transformation Journeys
Objetivos
- Identify an appropriate AI agent architecture for a given organizational context and explain key trade-offs
- Identify opportunities for AI-enabled BPO and describe their potential organizational impact
- Describe the organizational context relevant to a proposed AI-driven product design initiative
- Summarize the current product design workflow within an organization to establish a baseline for improvement
- Select an appropriate AI technology for integration into a product design process based on its capabilities and relevance
- Develop a structured plan outlining how AI can be integrated into a product design workflow to improve efficiency, effectiveness, or quality
Actividades
- Required Assignment 5.1: AI and the Design Process
- Optional Forum 5.1: Centralized vs. Embedded Agents · Forum 5.2: AI-Enabled Business Process Outsourcing (BPO) · Self-Study Knowledge Checks
Módulo 6 · The Last Mile — From Pilot to Practice 4–6 h
Secciones
- Voice Agents: Synthesis, Phone Systems, and Real-Time Applications
- Last-Mile Integration: Why Pilots Succeed but Deployments Stall
- Internal Resistance and Change Management
- Monitoring Agent Performance (Metrics, Key Performance Indicators, and Feedback Loops)
Objetivos
- Propose measurable KPIs that evaluate the effectiveness of an AI system in relation to business outcomes
- Describe the organizational context relevant to an AI implementation
- Summarize the purpose and functionality of a proposed AI system within a business workflow
- Write three to five KPIs that measure the effectiveness of an AI implementation
- Evaluate how the selected KPIs align with business goals, and indicate whether the AI system is achieving its intended outcomes
Actividades
- Required Assignment 6.1: KPIs for AI
- Optional Forum 6.1: KPI Brainstorm · Self-Study Knowledge Checks
Módulo 7 · Governance, Compliance, and Agent Testing 4–6 h
Secciones
- Regulatory Frameworks for Data Privacy
- Evaluating Agent Behavior With Testing Strategies
- Agent Speed vs. Oversight: Where to Insert Guardrails
- Documentation and Compliance Readiness
Objetivos
- Create guiding questions that identify key regulatory and implementation considerations in real-world AI healthcare scenarios
- Classify AI use cases using the risk-speed quadrant framework
- Identify applicable regulatory frameworks (e.g., GDPR, CCPA, HIPAA) relevant to a specific AI use case
- Analyze the risks associated with deploying AI systems, including both compliance and operational risks
- Apply appropriate testing strategies (e.g., sandboxing, A/B testing, safety checks) to evaluate AI system behavior
- Develop a comprehensive AI governance plan that integrates regulations, testing, risk mitigation, and documentation practices
Actividades
- Required Assignment 7.1: Governance Plan
- Optional Forum 7.1: Brainstorm Framework Implementation · Forum 7.2: Implementing Risk vs. Speed Quadrants · Self-Study Knowledge Checks
Módulo 8 · Ethics and Capstone ≥6 h
Secciones
Objetivos
- Evaluate ethical risks in a proposed AI system by identifying a potential issue, assessing its business impact, and recommending an appropriate mitigation strategy
- Explain how AI can be strategically integrated into organizational functions to create business value
- Evaluate the suitability of AI technologies for specific organizational use cases
- Analyze the cost, security, and operational implications of AI adoption
- Assess the human and organizational factors that influence successful AI implementation
- Synthesize course concepts into a structured approach for organizational AI adoption
Actividades
- Required Assignment 8.1: The Capstone (60% de la nota final)
- Optional Forum 8.1: Ethics in AI
Evaluación
| Componente | Peso | Cómo se califica |
|---|
| Required Assignments (8, una por módulo) | 40% | Completo / incompleto — todos los elementos pedidos = puntaje completo |
| Capstone | 60% | Culminación de todas las entregas anteriores |
- Certificado (MIT Professional Education Certificate of Completion + CEUs): initial survey + 80% de las obligatorias.
- No se requiere conocimiento previo de programación, matemáticas o estadística.
- Acceso al material: 6 meses después de terminar el curso, incluidos los videos.
- Soporte: support@globalalumni.org, respuesta en 24 h.
- Beneficios de alumni: 15% de descuento en cursos futuros de MIT PE, red de alumni, grupos de LinkedIn en español e inglés.