Marcus Stiehler

Ex-Disney Solutions Consultant | Native German | AI & Digital Transformation

Write Specs an AI Can Actually Build From

The Walt Disney Company

Ambiguity used to cost you a rework cycle. Now it ships at speed — see where AI-assisted delivery goes wrong first.

Why AI Amplifies Vague Requirements

A three-question check that tells you whether a requirement is buildable, testable, and free of hidden assumptions.

The Precision Test for Any Requirement

Watch a real stakeholder request go from one vague sentence to a specification an AI coding tool can execute cleanly.

Turn a Fuzzy Ask Into a Spec, Live

AI Rapid Prototyping as a Requirements Discovery and Elicitation Technique

Modernizing Business Analysis

Angela Wick

AI/BA Expert - 25+ years practicing, shaping analysis, product and requirements.

Cohort 2 - Aug 12th Austrailia-NZ-APAC

Cohort 3 -US,EMEA,SA

You were trained to discover requirements by asking, listening, and writing it all down. That made sense when building software was slow and expensive.Building a working, clickable prototype is now nearly free. You can stand up a working prototype in minutes, and a working prototype pulls out what people actually need faster than any interview, user story, or spec. Put something real in front of stakeholders and the reactions pour out, and those reactions and feedback are critical!AI helps us write faster, but it doesn't mean that writing requirements is still the best way to collaborate.This workshop treats AI rapid prototyping as what it really is: a discovery and elicitation technique. You will build and use prototypes to surface hidden assumptions, expose disagreement, and align technical, business, and vendor teams fast.*This is a "bring your own tool workshop". Students can use any AI rapid prototyping tool. Common tools include: Loveable, Bolt, Replit, Claude Design, Claude Code, Gemini, and Gemini Canvas, ChatGPT, and Copilot. If you do not have access to any of these, we recommend purchasing a monthly access to Loaveable or Claude Pro for about $20 for the month of class.

Master AI prototyping as an elicitation skill, and shift from documenting requirements to discovering them in real time.

Reframe prototyping as the new front end of discovery

Use prototypes to elicit what people struggle to put into words

Know the difference between discovery and delivery prototyping

Prototype across UIs & Workflows, Reports, Data & APIs, features, and full apps 

Facilitate prototyping across business, tech, and vendor teams 

BAs, product owners, product and project managers, systems analysts, requirements engineers, and consultants who bridge business and tech

Eliminate uncertainty in shipping AI features

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

August-2026

“Does it work?”... “Is it good enough?”... “Can we ship it?”...How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?How do you choose the right metrics, interpret fuzzy results, and make a confident decision?This course gives you a framework to do just that.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

PMs leading AI features, growth, or platform initiatives

PMs who partner with ML teams and want to set evaluation standards

PMs who need to make clear “ship or hold” calls without doing the engineering

Your team adopted AI. Why hasn’t delivery improved?

Staff Software Engineer

Luis Vieira

Staff engineer

Agentic Engineering 1

<blockquote>AI coding tools make implementation faster. But typing code is only one part of getting software safely from idea to production.Teams still need to turn ambiguous requests into clear plans, understand how changes affect an existing system, verify that they are safe, and make sound decisions about what reaches production.When code becomes cheap to produce, context, judgment, and verification become the limiting factors.This is why faster coding does not automatically mean faster delivery. Improving one part of the workflow has limited impact when the rest of the system continues to create bottlenecks.This workshop gives you a practical method for improving delivery with agents. You’ll learn to find the constraints slowing down your SDLC, design agents that can safely take on bounded work, and improve them over time using proof of work, feedback, and evals.</blockquote>

Learn how to find delivery constraints, design trustworthy agent workflows, and improve them through evidence, feedback, and evals.

Understand the AI coding plateau

Find agent opportunities across the SDLC

Design a trustworthy agent harness

Improve agent workflows over time

Software engineers using AI coding tools looking to improve their team SDLC

Tech leads and engineering managers who want to understand how to increase engineering output using AI

Engineering leaders who adopted AI tools but aren't seeing it in delivery speed or business outcomes and want to understand why and fix it

Master AI for Better Software Architecture.
Stay Ahead.

Head of Enterprise Architecture

NatWest Group

Boston Consulting Group (BCG)

TIBCO

Vincenzo Marchese

Vincenzo is a practitioner with over 25 years of experience in global businesses

Cohort 3

Cohort 4

As AI is reshaping software engineering across every phase of its lifecycle, the ability to architect high quality software is increasingly becoming a differentiating skill.However, although AI makes Architecting faster, it does not necessarily make it better!In this hands-on workshop you will learn how to leverage AI as the your Co-Architect for better architectures and increased design velocity.This is not a prompt class nor a course on how to architect AI Systems. This is a AI-enabled Architecting accelerator.The workshop will teach you how to effectively use AI to architect Systems and Solutions. It will show how to integrate AI into your everyday architecting tasks deliberately and safely.Whether for generating diagrams, drafting Architecture Decisions, or communicating the architecture to your key stakeholders, AI can help. We will dive deep into AI to augment your capabilities, increase your speed, and ensure your quality and value are not sacrificed.It's not a matter of if. It's a matter of when, to what extent, and with what impact to the role of the architect.Don't just be in the loop. Choose to remain in the lead when making architecting &amp; design decisions.

Learn to use AI as the Architect’s Co-pilot for better architectural decisions and increased velocity.

Explain the impact of AI

AI-powered architectural decision-making

Maximise AI potential in Architecture

Realise Value from AI

Risk Mitigation

Practicing Architects (Software | Domain | Solution | Enterprise) wanting to use AI in their daily work

Software Engineers and Technical Leads aspiring at leading architecting efforts

Product Owner and Managers aspiring at taking a more direct involvement in architecting

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Product managers who drive strategy, development, and launch of products.

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Using AI coding assistants and tools to write, review, and ship code faster.

Automating professional workflows with AI tools — productivity, operations, and task automation.

Write Specs an AI Can Actually Build From

What you'll learn

Why this topic matters

You'll learn from

Marcus Stiehler