AI
AI for Product Managers
This course introduces Product Managers to Artificial Intelligence (AI) concepts and the strategies needed to successfully define, build, and manage AI-powered products.
Participants will learn how to:
- Ensure responsible AI practices, including governance and ethical considerations.
- Develop an AI-first mindset and strategy for product development.
- Accelerate product discovery using AI for market research and competitor analysis.
- Build and validate hypotheses and prioritize features with confidence using AI-driven tools.
- Collaborate effectively with data science and engineering teams.
This course is included in the AI Annual Learning Pass.
Duration
½ day
Audience
- Product Managers and Product Owners.
- Directors and VPs of Product.
- Product Marketing Managers and Product Operations Managers.
- Product Designers and other professionals involved in product development exploring AI adoption.
- Professionals transitioning into AI-focused Product Management
Prerequisites
- No technical background or coding experience is typically required.
- Basic familiarity with product management methodologies and the product lifecycle is helpful.
- Future-Proof Your Role: Stay competitive and in-demand by mastering the skills to lead AI initiatives (PMs who embrace AI will replace those who don’t).
- Deliver Impact: Learn a problem-first, outcome-led approach to spot real AI opportunities and create funded business cases.
- Master the New Toolstack: Gain hands-on practice with Generative AI tools and prompt engineering for faster discovery, strategy, and prototyping.
- Lead Confidently: Bridge the gap between business, data science, and engineering to manage the unique challenges (like uncertainty and data drift) of the AI product lifecycle.
| Module | Topics Covered |
| Introduction to AI in Product Management | ● What is AI, ML, Deep Learning, and Generative AI?
● The AI Hype Cycle and value frameworks. ● How AI changes the Product Manager role and product lifecycle. |
| Defining the AI Product Strategy | ● Identifying and framing high-ROI AI problems and opportunities.
● Creating an AI business case and modeling monetization options. ● Data requirements, governance, and bias risks for AI/ML. |
| The AI Product Development Lifecycle | ● Key differences between AI and traditional product development (e.g., unpredictable outcomes, continuous model improvement).
● Working with data scientists and engineers, and defining AI-specific product requirements (PRDs). ● Experimentation, A/B testing, and rapid prototyping. |
| AI Tools and Prompt Engineering | ● Leveraging Generative AI tools (e.g., ChatGPT, Gemini) for faster PM workflows.
● Mastering prompt engineering basics and advanced techniques. ● Using AI for market research, synthesizing customer feedback, and tailoring communication. |
| Responsible AI and Measuring Success | ● Ethical and governance considerations, bias mitigation, and privacy checks.
● Defining and measuring AI product success (KPIs and ROI evaluation). ● Post-launch monitoring, performance, and addressing data drift. |
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Asynchronous courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.