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How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.

ML Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Enterprise AI veteran: Shipping AI to production, with clarity and humor

Pilot Cohort

Summer 2026

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you're building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.--🚨 Summer cohort: June 2 – June 26 🚨Winter Cohort is running in Feb/March---What you get:🔄 The Loop, a 5-step framework you'll use to think through every AI project🧑‍🏫 Live sessions and office hours where you can bring your real problems📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 Production-ready checklists for RAG, Forecasting, GenAI🎥 Lifetime access to all recordings---This course is for you if:• Your AI works in demos but fails in production• You've spent months on a model only to realize you solved the wrong problem• You lead AI work but came from engineering, product, or PMYou'll learn from 200+ AI failures in 4 weeks. You gain years of experience and recognize the scars.

Most AI fails from bad framing, not bad models. Learn to fix that.

Frame AI problems right the first time

Diagnose what's actually broken

Own the problem, not just the solution

Steer your team past expensive mistakes

We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.

You understand what AI/ML is at a conceptual level

Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.

You've worked on or led AI projects in some capacity

This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.

No coding during the course; frameworks for thinking, not tutorials

Engineers who can build AI but want to master outcome engineering: knowing what to build, not just how

Building RAG, agents, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.

Managers who need to evaluate AI/ML proposals, set realistic milestones, and know when to escalate

Learn how to redesign end-to-end processes so AI strengthens your organization instead of fragmenting it.


BA-Cube Founder | LinkedIn Instructor

Angela Wick

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

AI-Enabled Systems & Process Design

Most AI initiatives focus on tools and tasks.Few redesign the process and/or system.When AI is inserted into broken or unclear processes, the result is:<ul><li>More exceptions</li><li>Hidden ownership gaps</li><li>Unclear escalation</li><li>Fragile automation</li><li>Accountability confusion</li><li>Scaling nightmares</li></ul>This means increased cost and unhappy customers!AI changes how systems behave. We must design coherent processes with the end-to-end in mind.That means intentionally aligning:<ul><li>Users and their scenarios</li><li>User experience and workflows</li><li>Business rules and policies</li><li>Data flows</li><li>AI guidance, guardrails, governance, and performance measures</li></ul>Business analysis is the skill set, mindset, and discipline that brings all of these elements together—integrating them into coherent solutions that deliver real business value.

Design AI-Enabled Business Systems That Actually Work

Differentiate embedded AI, automation, and agentic AI in end-to-end processes.

Analyze an end-to-end business process that includes AI components

Clarify ownership and decision accountability

Define, Design, and Build human checkpoints into AI workflows

Facilitate decisions about AI in the organization

Senior Business Analysts / Lead BAs

Product and Transformation Leads / BA Consultants

BA Managers modernizing delivery models

Learn to design and evaluate a production-ready media recommender: metrics, architecture, cold start, and drafting an effective roadmap.

Principal ML Engineer @ Disney, ex-Meta

The New York Times

The Walt Disney Company

ESPN

Columbia University

Katerina Zanos

Principal ML Engineer @ Disney, x-Meta, x-NYT

Cohort 1

Cohort 2

Build a production-ready Recommender System Blueprint for a media surface of your choice. This will be an artifact you can take back to work to guide implementation, align stakeholders, and make smarter roadmap calls.Most teams working on recommender systems don’t struggle because they lack algorithms. They struggle because they lack a practical, end-to-end playbook for shipping.You might know the building blocks (embeddings, retrieval, ranking, bandits), but still feel stuck on the hard parts:<ul><li>which metrics actually reflect what product wants</li><li>how to architect the system from logging to serving</li><li>what model to choose under real constraints</li><li>how to handle cold start and sparse data</li><li>and how to prove ML is worth it over heuristics</li></ul>I’ve built recommendation systems in production across news, feeds, and sports over the last 10 years (NYT, Meta, ESPN). This course turns that experience into a clear, reusable process you can apply to your own product without needing FAANG-scale infrastructure. By the end, you’ll be able to confidently design and evaluate production-ready recommenders, defend trade-offs, and use your Blueprint as a durable reference for future iterations.

Design and evaluate a media recommender system that ships to production.

Build a real-world mental model of recommenders (not just algorithms)

Design metrics and success criteria your team will actually align on

Architect the system: make the right calls on retrieval, ranking, and serving

Handle cold start with pragmatic strategies

Know when ML beats heuristics and how to prove it

You are able to interpret (not compute) common product metrics like retention/engagement and discuss tradeoffs.

Metric + product reasoning at a basic level

You can follow an architecture diagram and understand concepts like services/APIs, latency, reliability constraints.

Ability to follow system-level thinking

We will be working with specs, diagrams, and decision frameworks (not code).

Comfort making structured decisions from imperfect data

Senior ML Engineers &amp; Data Scientists who can build models but want sharper judgment on what to build next and how to defend it

Software Engineers transitioning into ML who want to think in systems + measurement, not just training code.

Tech Leads who need an end-to-end playbook to review designs, align metrics with product, and avoid expensive detours.

Learn how to define task autonomy, decision thresholds, guardrails, and human override in agent-enabled systems.

Agentic AI Skills for Business Analysts

Agentic AI changes the game because AI can autonomously act. It can initiate actions, make decisions, and operate across workflows with increasing autonomy. Risk escalates, and so does opportunity! Without deliberate, disciplined, and structured analysis, agents act on incomplete logic, unclear boundaries, and undefined escalation paths. That is where exposure multiplies. This course matters because Business Analysts are uniquely positioned to define autonomy limits, decision thresholds, guardrails, and monitoring structures before agents scale. It equips you to design control into autonomy, so AI-driven systems remain accountable, coherent, and aligned with business intent rather than becoming fragile at scale.AI is moving beyond assistance. Agents can:<ul><li>Initiate actions on their own</li><li>Make decisions</li><li>Trigger workflows</li><li>Operate continuously</li></ul>This changes the role of Business Analysis.When agents act without clearly defined:<ul><li>Goals</li><li>Boundaries</li><li>Escalation rules</li><li>Monitoring thresholds</li></ul>Organizations lose control.This course teaches how to design and govern agent behavior responsibly.

AI Fundamentals

Apply AI in your role

Career Development

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Career development

Craft

AI Fundamentals

Craft

AI Fundamentals

Leadership fundamentals

AI Fundamentals

Leadership fundamentals

By Role

System Design

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