Agentic AI

Coding with AI

AI Evals

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product

User Research

Prototyping

Growth

Strategy

Influence

Leadership

Career Growth

Engineering

Machine Learning

Security

System Design

Design

Figma

Design Systems

Design Strategy

Marketing

Content Marketing

Go-to-Market

Product Marketing

B2B Marketing

AI for Leaders

Communication

Management

Storytelling

Founders

More

Everyone

Operators

Data Scientists

User Researchers

Project Managers

Sales People

Lawyers

Finance

Investors

Real Estate

Understand why AI raises the bar for BAs, define role value in an AI context, and describe your role and value to others.

BA-Cube Founder | LinkedIn Instructor

Angela Wick

25+ years practicing, shaping, and teaching analysis, product, and requirements.

The Business Analyst Role in the Age of AI

Everyone's role is changing, especially in software and projects. AI is seemingly collapsing roles, or is it changing the roles and tasks? AI introduces ambiguity, probabilistic behavior, and continuous decision-making into systems designed for certainty. This course exists to answer a foundational question:What is the Business Analyst responsible for when AI changes how work gets done, and what analysis is needed for AI projects and AI Agents?This course focuses on role clarity. You will examine how AI changes productivity expectations, teaming, and the SDLC, and analysis tasks needed for AI projects, integration/automation, and Agentic AI.This course is designed to reshape how you think about your role before you learn execution mechanics.

Reframing the BA role for AI-enabled organizations

Why AI increases the need for business analysis rather than reducing it

Which BA tasks AI can take over and which responsibilities cannot be automated

Common role collapse patterns in AI initiatives

How AI breaks phase-based SDLC thinking

How to explain BA value as integration, judgment, and accountability

Business Analysts, Product Owners, Business Systems Analysts, Systems Analysts, Business Process Analysts, and similar roles.

BA Leaders redefining roles for AI-enabled teams

Professionals pulled into AI initiatives without clear accountability

Students will learn the AI literacy to evaluate AI ideas, surface risk, and make sound judgments without becoming technical specialists.

AI Literacy for Business Analysts

AI risk often comes from poor assumptions about what AI can and cannot do. It also comes from poor analysis and organizational dynamics of leaders asking for solutions and pressuring teams to deliver solutions rather than solving real problems.Analysis professionals have a huge role in this dynamic that can change everything!Analysts are increasingly expected to participate in AI decisions, yet most training focuses on tools or stays abstract. This course fills the gap in understanding AI capabilities: Not from a technical point of view, rather a capability point of view; business uses and patterns of functional usage.You will build a grounded understanding of AI capabilities, limits, patterns, and failure modes, and learn how requirements shift toward hypotheses, experiments, and continuous outcomes in AI-enabled work.This course explores the skills of:🎯 Identifying the right AI use cases for specific business scenarios🎯 Distinguishing between deterministic and probabilistic systems and deciding where each should be applied in a workflow🎯 Questioning and communicating AI use case fit and risks—and defining clear hypotheses for AI solutions

Giving BAs AI Literacy to Understand, Evaluate and be Accountable for AI 

Major AI capability types and their appropriate uses

GenerativeAI is just one piece of AI, and much more than "chat".

What AI is good at and where it fails

Generative AI and its Data Sources (Public vs. Private)

How requirements shift to hypotheses and experiments

Where humans must remain accountable in AI-enabled systems

Analysts, Project Managers, Business Leaders and others involved in AI discussions or initiatives

Analysis Leaders responsible for AI-related risk

Analysts who want confidence without pretending technical expertise

Learn how to use AI to accelerate customer discovery, validate solutions, and make better product decisions, without leaving your desk

Product Innovation & AI Trainer

Capital One

Cisco

Wesco

Google

Valerio Zanini

Product Management and Innovation trainer. Author, "AI for Product Managers".

Cohort 2

Cohort 3

Using AI in product management is not just about getting better at ChatGPT. It's about leveraging AI across the product development life cycle (PDLC) to accelerate, expand, and simplify what's possible.Unfortunately, most AI courses for PMs focus on how to build AI-powered features. But PMs can benefit from AI practices regardless of the type of product they are building. This is the focus of this course: helping PMs expand the daily practice of product management and giving PMs super powers.Over two weeks, you’ll learn—by doing—how to use AI to accelerate discovery, synthesize insights, explore solutions, and support decisions, while understanding where AI helps and where it can mislead. This is a hands-on, instructor-led cohort for PMs who make real product decisions.Duration: 2 weeks (short, intensive, focused) + Graduation Demo dayFormat: Instructor-led cohort with 2 sessions per week (see schedule below)Between sessions: Hands-on individual assignments (to build the practical muscles)

Get AI super powers to grow in your PM job

Accelerate your Product Management job

Use AI to accelerate customer discovery

Build repeatable AI-augmented workflows you can reuse in real product work

Understand when AI can accelerate insights

Expand solution space and prototype quickly with AI

Product Managers, Product Owners, Product Leaders, and entire product teams. Startup founders and product designers.

PMs who want to move beyond “AI curiosity” to real, practical use of AI in product discovery and development, with hands-on practice.

This course is not a beginner tutorial on ChatGPT. It's a deep-dive on how to use AI tools in product work.

By the end of AI 202 you will have a more stable relationship with both technology and responsibility.

Human Judgment in the Age of AI

Microsoft

Accenture

University of Washington

Teresa Escrig, PhD in AI

Cohort 4

AI does not replace leadership — It tests the quality of human judgment. AI is powerful — and increasingly present in everyday decisions.But AI does not correct human judgment.It amplifies the internal structure of the person using it.When that structure is scattered, decisions become reactive.When it is coherent, clarity and leadership expand.AI 202 is a structured program designed to strengthen the internal clarity that AI amplifies — so decisions remain steady even as capability accelerates.<h3>What We Observed in Cohort 1</h3>Participants completed the same self-assessment before and after five weeks.On a 1–5 clarity scale (1 = steady, 5 = reactive) average scores improved by more than one full point.Participants reported:• Less overthinking • Fewer reactive decisions • Greater steadiness under pressure • Increased confidence in executionSeveral began skeptical about AI.They left with a clear framework for what to delegate — and what not to.The goal is simple:Clearer decisions — before they scale across teams and outcomes.

Clarity and decision sovereignty in the age of AI

Make clear decisions under pressure

Use AI as a precise collaborator

Maintain steadiness and focus

Recognize and adjust thinking patterns AI amplifies

AI 101 for Conscious Creators (included at no additional cost).

Leaders with Decision Authority Founders, executives, and investors scaling responsibility without losing clarity

Leaders in Transition Professionals redefining judgment, authority, and identity in the AI era

Leaders by Responsibility (Not Title) People whose choices affect others—and who want clarity before decisions scale

Decision Making

Cohort-based courses

You may be interested in