• Business and product leaders driving growth, innovation and AI-enabled initiatives, who need a structured way to identify high-value opportunities and develop practical blueprints for adoption and execution
• AI, data and technology leaders responsible for AI strategy, technology adoption and enterprise readiness, who need frameworks for evaluating and shaping AI initiatives that align with business objectives
• Consultants and transformation advisors guiding clients through AI adoption and operating model change, who are looking for a repeatable methodology that connects business challenges to viable agentic AI opportunities
• AI product and solution professionals, including product managers, business analysts and solution architects working alongside AI teams, who want to connect business needs with effective AI-enabled workflows

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

• Assess the reliability of AI-driven decisions based on context, data quality and human oversight
• Identify business workflows best suited for agentic automation based on value and feasibility
• Define the personas, outcomes, data and workflow requirements for an agent system
• Design a multi-agent architecture for a complex business outcome
• Develop a Prompt Product Requirements Document (PRD) that defines an agent system’s knowledge, decision rules, actions and safeguards
• Evaluate governance and scaling considerations for responsible organizational adoption

Module 1: The Brain of AI

Examine how AI creates value through role automation and how human and AI decision making differ. Identify how context, data quality and oversight affect AI reliability, and pinpoint a workplace or societal problem AI could help address.

The module also explores:

• AI value creation and role automation
• Human vs. AI decision making
• Brain-inspired AI and neural networks

Module 2: Managing and Achieving Outcomes With AI

Prioritize potential AI opportunities using value and feasibility criteria while defining the desired outcomes, users and data requirements.

This module also explores:

• Divergent and convergent thinking in opportunity framing
• The Filter Prioritization Matrix
• Persona development
• The outcome equation
• The data vision statement

Module 3: The Four Workflows of AI

Identify which of four AI workflow types best support the selected opportunity, determine the required data inputs and outputs and map the surrounding data ecosystem.

This module also explores:

• The four workflows of AI
• Maslow's hierarchy of data science
• Data share of wallet
• Proxy metrics and hypothesis development
• The Data PRD

Module 4: Playground — Hands-On With AI Tools and Agents

Gain guided, hands-on experience across multiple AI platforms, applying concepts from earlier modules to build assistants, prototypes and specifications, and compare outputs, reliability and data considerations across tools.

This module also includes:

• Domain-specific AI assistant configuration (ChatGPT)
• Prototyping and wireframing (Manus)
• Prompt experimentation (DeepSeek)
• Product specification development (NotebookLM)
• LLM project configuration (Claude Projects)

Module 5: AI Prompt Engineering: Prompting is the New Executive Skill

Treat prompting as an executive capability. Match AI models and intelligence levels to task requirements, then progressively refine a structured prompt using context, persona and a reusable skill.

This module also explores:

• Native AI prompting
• AI intelligence allocation levels
• Model selection
• Self-prompting and repeatable workflows

Module 6: The Five Agents That Matter

Explore five agent types and the semantic understanding, data, actions, governance and orchestration that enable agent systems to operate effectively.

This module also explores:

• The five-agent taxonomy
• The Reason-Act-Observe agent loop
• Semantic understanding for AI agents
• The risk-versus-reward matrix
• Agent governance policies and cost function

Module 7: From Data PRD to Prompt PRD: Multi-Agent Architecture

Using tax automation as a real-world example, learn how to define the purpose, knowledge, decision rules and oversight requirements of a complex agentic AI system. Apply the framework to your own opportunity to develop a Prompt PRD that brings these elements together.

This module also explores:

• The Prompt PRD
• Jobs to Be Done
• Selecting a North Star example
• The knowledge index and legal/ethical requirements

Module 8: Agentic AI Governance and Scale — The Inches That Matter

Examine the governance and scaling decisions required for responsible adoption. Apply the Find It, Bottle It, Scale It framework, and consider data sovereignty, cost and the effects of AI on communities and national interests.

This module also explores:

• The Find It, Bottle It, Scale It framework
• Cost and organizational value
• Data sovereignty and ownership
• Community and national sovereignty considerations

Capstone Project

The capstone project is a progressive assignment in which you select a meaningful organizational or societal problem and design a conceptual agent system to address it. Each module of the curriculum contributes a distinct artifact or design decision, letting you test assumptions and refine the proposed system as your understanding of key concepts develops.

• Module 1: Define a Problem Hypothesis that identifies the organizational or societal challenge the participant’s agent system will address. 
• Module 2: Develop an AI Opportunity Brief that brings together a target persona, a scored Problem Hypothesis and a data vision statement.
• Module 3: Apply frameworks to the selected opportunity, integrating artifacts from Modules 2 and 3.
• Module 4: Complete a cross-platform reflection comparing outputs, reliability, limitations and the need for human oversight across two or more platforms.
• Module 5: Produce a Structured Prompt Prototype and compare pre- and post-refinement outputs.
• Module 6: Develop a Multi-Agent Architecture Blueprint that defines each agent’s role, decision boundaries and system requirements. This blueprint will inform the Prompt PRD developed in Module 7.
• Module 7:  Develop a Prompt PRD integrating the persona, data vision statement, Data PRD and Multi-Agent Architecture Blueprint developed throughout the program.
• Module 8: Submit governance and scaling guidelines addressing cost, data use, community impact, human oversight and audit requirements, culminating in a Governance and Scaling Brief.

Birju Shah - Academic Director; Clinical Professor of Product Management, Analytics, and AI; x-Head of Product for Uber AI; x-Head of Digital Yield Products at Monsanto; x-CPO/CTO of Redesign Health