Detailed Course Outline
Module 1: Foundation of AI for Program Strategy – Introduction
As a Program Director, you face pressure to deliver AI-enabled results without writing code yourself. This module gives you the confidence to talk to technical teams, question vendors, and make informed choices about where AI truly adds value. You use it to judge proposals, spot unrealistic claims, and guide projects so that automation, ethics, and impact stay aligned with your program goals.
In this module, you explore core ideas of AI, machine learning, and deep learning, see how AI systems learn from data, and follow the full project lifecycle from data collection to deployment. You review real examples like healthcare triage and retail recommendation engines, examine AI’s impact on society and sustainability, and complete a Teachable Machine lab that lets you build and test a simple image classifier yourself.
Module 2: Identifying AI Opportunities & Use Cases
As a Program Director, you are expected to spot where AI can move the needle instead of chasing hype. This module helps you see which problems are worth solving with AI, avoid low-value experiments, and make confident calls on where to invest time, budget, and stakeholder attention so your AI agenda directly supports organizational goals.
In this module, you work with practical tools like the AI Canvas and the Value vs Feasibility Matrix, learn to spot AI-ready processes such as repetitive work, data-heavy workflows, and personalization needs, and apply prioritization methods like weighted scoring and risk-adjusted ROI. You also explore fraud detection and project management case studies and use Trello in a guided activity to map, score, and rank AI opportunities for your own context.
Module 3: Governance & Ethics in AI
You work in a space where AI decisions can directly affect people’s careers, money, health, and access to services, so getting governance and ethics right is nonnegotiable. This module helps you protect your organization from reputational, legal, and societal risk while still unlocking AI’s benefits, so you can lead with confidence when questions of bias, fairness, and accountability arise.
In this module, you dive into Responsible AI principles, global governance models like the EU AI Act and NIST AI RMF, and practical tools for spotting and reducing bias across the AI lifecycle. You work with real cases in hiring and credit scoring, apply techniques such as diverse datasets, bias testing, and human-in-the-loop oversight, and use Google’s What-If Tool to test fairness scenarios and decide which governance actions you should trigger.
Module 4: AI Project Lifecycle & Integration
You face growing pressure to turn AI ideas into real outcomes while keeping budgets, risks, and teams aligned. This module helps you run AI projects in a structured way, connect technical work to business goals, and make confident choices about when to build, buy, or partner so you can deliver reliable, explainable AI that actually fits your organization’s workflows.
In this module, you follow the CRISP-DM lifecycle from problem definition and data preparation through modeling, evaluation, and deployment. You compare build–vs–buy–vs–partner options using practical decision criteria, explore predictive maintenance use cases with no-code tools, and see how Jira and Asana support timelines, tasks, and governance checkpoints. You also simulate a full AI project in Asana so you can practice orchestrating roles, milestones, and progress across the AI lifecycle.
Module 5: Data Strategy & Infrastructure for AI
You rely on data to make AI projects work, yet you also carry the risk when that data is messy, siloed, or sensitive. This module helps you treat data as a strategic asset, so you can demand better quality, enforce governance, and make confident choices about privacy, cloud tools, and infrastructure that keep AI reliable and compliant at scale.
In this module, you explore data governance, stewardship, and quality management, learn how to design and run data pipelines on cloud platforms like AWS and Azure, and see how to manage sensitive data using privacy-by-design, anonymization, and federated learning. You also walk through a retail inventory use case, a healthcare privacy case study, and a hands-on Airbyte and Google Sheets lab that lets you build a no-code pipeline for AI-ready data.
Module 6: AI Integration: Build vs Buy vs Partner
You are frequently asked whether to build AI in-house, license tools, or rely on expert partners—and those choices lock in cost, speed, risk, and future flexibility. This module helps you make those calls with confidence, so you can back your recommendations with clear trade-offs and avoid ad-hoc, vendor-driven decisions that weaken your AI roadmap.
In this module, you explore when to build custom models, when to buy off-the-shelf products, and when to partner with vendors, comparing options on cost, time, expertise, scalability, and risk. You work with evaluation frameworks, scorecards, and a predictive-maintenance case to assess proposals on accuracy, integration effort, support, and long-term fit, then practice using a Google Sheets template to choose the best vendor for a real scenario.
Module 7: AI Risk Management & Compliance
Working with AI across programs exposes you to legal, ethical, and reputational risk whenever systems touch data, customers, or the public. You need clear ways to spot problems, challenge risky designs, and prove compliance so regulators, leaders, and stakeholders can trust the AI you deploy. In this module, you explore AI risk management and compliance frameworks such as the EU AI Act, GDPR, and NIST AI RMF, learn how to detect and reduce bias across the AI lifecycle, and practice applying tools like AI Fairness 360, Fairlearn, KNIME, and PAIR Facets to real-world cases in finance, law enforcement, and customer decisioning. You leave with practical checklists, processes, and metrics to keep AI initiatives both innovative and accountable.
Module 8: AI Tools & Techniques for Project Management
You lead AI initiatives that span data teams, engineers, and business stakeholders, and without the right tools projects easily slip, duplicate effort, or lose visibility. This module helps you run AI work like a repeatable system, so you see who is doing what, where bottlenecks sit, and how timelines, data pipelines, and model iterations stay aligned with business goals.
In this module, you explore AI-focused project management platforms such as Trello, Asana, Monday.com, and Jira, along with cloud data tools including AWS, Azure, and Google Cloud. You work through an AI workflow management case in retail and a guided Asana lab where you design timelines, set milestones, assign tasks, track dependencies, and use dashboards to monitor progress across the full AI lifecycle.
Module 9: Leadership in AI
You are expected to lead AI initiatives through complexity, conflicting expectations, and constant change, not just approve projects and budgets. This module helps you guide teams through uncertainty, win trust from skeptical stakeholders, and turn AI from isolated pilots into visible wins that strengthen your credibility and move the organization toward responsible, scalable adoption.
In this module, you explore leadership strategies for AI projects, change management techniques, and stakeholder mapping tools such as Miro. You work with communication frameworks, progress dashboards, and storytelling methods tailored to executives, technical teams, and frontline staff, then apply them in a predictive-maintenance manufacturing case and a hands-on stakeholder communication mapping exercise to design your own AI leadership and communication plan.
Module 10: Scaling AI Initiatives
You are likely running AI pilots that work in one corner of the business but struggle to expand them across regions, teams, and systems. This module helps you move from isolated experiments to organization-wide AI that is reliable, compliant, and aligned with strategy, so your investments scale instead of stalling. You use it to anticipate obstacles in data, infrastructure, and culture before they slow down deployment. In this module, you explore the shift from pilot to full-scale rollout, study common scaling challenges, and use AI organizational maturity models to assess how ready your company is for enterprise AI. You work through a retail recommendation scaling case, map data, infrastructure, and governance needs, and complete a Lucidchart lab where you build a visual roadmap with phases, milestones, risks, and feedback loops for your own AI initiatives.
Module 11: Future Trends in AI
You operate in a space where AI is changing faster than most roadmaps, and you’re expected to spot what’s signal vs noise. This module helps you anticipate where AI is heading, connect future trends to your programs, and make confident calls on where to invest so you don’t get locked into short-lived tools or miss high-impact opportunities.
In this module, you explore emerging technologies such as generative AI, edge AI, multi-agent systems, and advanced AI governance tools, then see how they play out in real-world domains like autonomous vehicles and smart mobility. You also work with hands-on demos using platforms like Hugging Face and TensorFlow so you can evaluate, pilot, and communicate next-wave AI possibilities in practical, program-ready terms.
Module 12: Capstone Project & Presentation
This capstone module gives you the chance to pull everything together and prove you can design and lead a full AI initiative, not just talk about concepts in isolation. You use it to showcase your strategic thinking, decision-making, and communication skills in a setting that closely mirrors how you would pitch and defend an AI roadmap in your own organization.
In this module, you choose a real or hypothetical organization, craft an end-to-end AI strategy using structured templates, and cover opportunity selection, data and governance plans, solution design, and scaling. You then present your proposal, receive peer and instructor feedback, go through a final review against clear criteria, and earn certification that reflects your readiness to drive AI programs in the real world.